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        <title>BenchLM - AI Benchmarking Platform</title>
        <link>https://benchlm.ai</link>
        <description>AI model benchmark comparisons, analysis, and insights.</description>
        <lastBuildDate>Mon, 13 Apr 2026 22:01:23 GMT</lastBuildDate>
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        <copyright>Copyright © 2026 BenchLM</copyright>
        <item>
            <title><![CDATA[Claude API Pricing: Haiku 4.5, Sonnet 4.6, and Opus 4.6 (April 2026)]]></title>
            <link>https://benchlm.ai/blog/posts/claude-api-pricing</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/claude-api-pricing</guid>
            <pubDate>Mon, 13 Apr 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Current Anthropic Claude API pricing from official model pages, including prompt caching, batch discounts, and the current 1M context beta notes.]]></description>
            <content:encoded><![CDATA[
Claude's pricing tells a simpler story than most comparison tables suggest. Three tiers, one consistent 5x output-to-input ratio, two discount levers. But the interesting question isn't "what does Claude cost?" — it's "when does Claude's quality premium pay for itself?" Cheaper models exist. Some of them score higher on aggregate benchmarks. The case for Claude has never been about being the cheapest option — it's about whether the quality gap on instruction following, writing, and precision tasks saves you enough rework to justify the price difference.

This guide uses Anthropic's current public model pages for [Haiku 4.5](https://www.anthropic.com/claude/haiku), [Sonnet 4.6](https://www.anthropic.com/claude/sonnet), and [Opus 4.6](https://www.anthropic.com/claude/opus), combined with benchmark data from [BenchLM.ai](/) and [Arena Elo scores](https://arena.ai/leaderboard/text), to help you decide whether Claude's pricing makes economic sense for your workload.

## Claude pricing at a glance

| Model | Input $/M | Output $/M | Notes |
|-------|-----------|------------|-------|
| [Claude Haiku 4.5](/models/claude-haiku-4-5) | $1.00 | $5.00 | Fastest, cheapest Claude tier; also available in Claude Code |
| [Claude Sonnet 4.6](/models/claude-sonnet-4-6) | $3.00 | $15.00 | Default production tier; 1M context beta on API only |
| [Claude Opus 4.6](/models/claude-opus-4-6) | $5.00 | $25.00 | Premium tier; 1M context beta on Claude Platform only |

Every current Claude tier keeps the same **5x output-to-input ratio**. That consistency makes back-of-the-envelope budgeting easy: if you know your input cost, multiply by five for output. No other major provider is this predictable — OpenAI's ratios range from 3x to 8x depending on the model, and Gemini's vary by context length tier.

The tier spacing is also clean. Sonnet costs 3x Haiku on both input and output. Opus costs 1.67x Sonnet. That Opus-to-Sonnet gap is worth remembering — it's much smaller than you might expect if ]]></content:encoded>
            <author>Glevd</author>
            <category>pricing</category>
            <category>claude</category>
            <category>anthropic</category>
            <category>api</category>
            <category>cost</category>
            <category>guide</category>
        </item>
        <item>
            <title><![CDATA[DeepSeek API Pricing: deepseek-chat vs deepseek-reasoner (April 2026)]]></title>
            <link>https://benchlm.ai/blog/posts/deepseek-api-pricing</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/deepseek-api-pricing</guid>
            <pubDate>Mon, 13 Apr 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Current DeepSeek API pricing from the official docs: deepseek-chat and deepseek-reasoner, cache-hit vs cache-miss pricing, output pricing, and the current V3.2 endpoint mapping.]]></description>
            <content:encoded><![CDATA[
DeepSeek's pricing page is the simplest in the industry — two endpoints, one pricing table, three numbers. But those three numbers tell a story that changes how you should think about LLM cost optimization. At $0.028 per million input tokens on cache hits, DeepSeek makes input tokens essentially free. The real question becomes: what's the quality trade-off, and when does it matter?

This guide uses the current official [DeepSeek pricing page](https://api-docs.deepseek.com/quick_start/pricing/), combined with benchmark data from [BenchLM.ai](/) and cross-provider pricing from sibling posts on [Claude](/blog/posts/claude-api-pricing), [OpenAI](/blog/posts/openai-api-pricing), and [Gemini](/blog/posts/gemini-api-pricing), to help you decide when DeepSeek's pricing makes it the right — and wrong — choice.

## DeepSeek pricing — the simplest table in the industry

| Endpoint | Model Version | Context | Input Cache Hit $/M | Input Cache Miss $/M | Output $/M |
|----------|---------------|---------|----------------------|----------------------|------------|
| `deepseek-chat` | DeepSeek-V3.2 | 128K | $0.028 | $0.28 | $0.42 |
| `deepseek-reasoner` | DeepSeek-V3.2 | 128K | $0.028 | $0.28 | $0.42 |

Two endpoints. Same underlying model. Same price. The real cost split in DeepSeek's current pricing is not chat versus reasoner — it is **cache hit versus cache miss**, a 10x difference on input tokens.

Compare this to the pricing complexity at other providers. OpenAI publishes separate rates for GPT-5.4, GPT-5.4 nano, GPT-5.4 mini, o3, and o4-mini — each with different input, output, and reasoning token prices. Anthropic has three Claude tiers with different ratios. Gemini has context-length-dependent pricing tiers. DeepSeek has one table with three numbers. That simplicity is worth appreciating, even if the model isn't competing at the frontier.

Output pricing is flat at **$0.42 per million tokens** regardless of caching or endpoint choice. There are no separate reasoning toke]]></content:encoded>
            <author>Glevd</author>
            <category>pricing</category>
            <category>deepseek</category>
            <category>api</category>
            <category>cost</category>
            <category>guide</category>
            <category>budget</category>
        </item>
        <item>
            <title><![CDATA[Gemini API Pricing: Current Flash, Flash-Lite, and Pro Rates (April 2026)]]></title>
            <link>https://benchlm.ai/blog/posts/gemini-api-pricing</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/gemini-api-pricing</guid>
            <pubDate>Mon, 13 Apr 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Current Gemini API pricing from Google's official docs: 3.1 Pro Preview, 3.1 Flash-Lite Preview, 3 Flash Preview, 2.5 Flash, 2.5 Pro, plus Batch and Flex pricing.]]></description>
            <content:encoded><![CDATA[
Gemini API pricing is more complex than any other major provider's. Google splits pricing by model, by service tier (Standard, Batch, Flex), and — for Pro models — by prompt size. That three-dimensional pricing grid is why most comparison tables get Gemini wrong. It's also why Gemini can be either the cheapest frontier option or one of the pricier ones, depending entirely on how you use it.

Five current models, three service tiers, and a prompt-size threshold on two of those models means dozens of price combinations. This guide walks through all of them, explains what most pricing summaries miss, and helps you figure out which combination actually minimizes your bill.

This guide uses the current official [Gemini pricing page](https://ai.google.dev/gemini-api/docs/pricing) and [Gemini rate-limit page](https://ai.google.dev/gemini-api/docs/rate-limits). Use our [cost calculator](/tools/cost-calculator) for quick estimates and our [token counter](/tools/token-counter) to check prompt sizes before you ship.

## The pricing you need to know

Here are the current official rates for every Gemini model with API pricing, organized by service tier. For Pro models, note the prompt-size threshold — this is the detail that most comparison sites omit.

### Gemini 3.1 Pro Preview

The newest Pro model. Materially more expensive than 2.5 Pro, with prompt-size-dependent pricing.

| Tier | Input $/M (<=200K) | Input $/M (>200K) | Output $/M (<=200K) | Output $/M (>200K) |
|------|---------------------|---------------------|----------------------|----------------------|
| Standard | $2.00 | $4.00 | $12.00 | $18.00 |
| Batch | $1.00 | $2.00 | $6.00 | $9.00 |
| Flex | $1.00 | $2.00 | $6.00 | $9.00 |

No free tier listed for this model.

### Gemini 2.5 Pro

Google's previous-generation Pro model. Lower pricing than 3.1 Pro Preview, same prompt-size threshold structure.

| Tier | Input $/M (<=200K) | Input $/M (>200K) | Output $/M (<=200K) | Output $/M (>200K) |
|------|---------------]]></content:encoded>
            <author>Glevd</author>
            <category>pricing</category>
            <category>gemini</category>
            <category>google</category>
            <category>api</category>
            <category>cost</category>
            <category>guide</category>
            <category>free tier</category>
        </item>
        <item>
            <title><![CDATA[OpenAI API Pricing: GPT-5.4, GPT-5.2, and GPT-5.1 (April 2026)]]></title>
            <link>https://benchlm.ai/blog/posts/openai-api-pricing</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/openai-api-pricing</guid>
            <pubDate>Mon, 13 Apr 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Current OpenAI API pricing from official docs: GPT-5.4, GPT-5.2, GPT-5.1, cached input rates, Batch API discounts, and the pricing details that actually matter.]]></description>
            <content:encoded><![CDATA[
OpenAI's API pricing is simpler than it looks — but most comparison tables miss the detail that matters most. Every [GPT-5.4](/models/gpt-5-4) family model has a **cached input** rate at 10% of normal pricing, and the Batch API cuts everything by 50%. That means the effective price of GPT-5.4 for a well-architected application is often half or less of the headline rate. This guide covers the real pricing, the decision tree for choosing the right model, and when OpenAI is (and isn't) the best value.

All prices below come from two official OpenAI sources: the live [API pricing page](https://openai.com/api/pricing/) for the GPT-5.4 family, and the [GPT-5.2 launch page](https://openai.com/index/introducing-gpt-5-2/) for GPT-5.2, GPT-5.1, and GPT-5 Pro pricing. Use our [cost calculator](/tools/cost-calculator) for quick estimates and our [token counter](/tools/token-counter) to sanity-check prompt size before you ship.

## Current OpenAI pricing at a glance

### GPT-5.4 family on the live pricing page

| Model | Input $/M | Cached Input $/M | Output $/M | Batch Input $/M | Batch Output $/M |
|-------|-----------|------------------|------------|-----------------|------------------|
| [GPT-5.4](/models/gpt-5-4) | $2.50 | $0.25 | $15.00 | $1.25 | $7.50 |
| [GPT-5.4 mini](/models/gpt-5-4-mini) | $0.75 | $0.075 | $4.50 | $0.375 | $2.25 |
| [GPT-5.4 nano](/models/gpt-5-4-nano) | $0.20 | $0.02 | $1.25 | $0.10 | $0.625 |

OpenAI notes that those rates are the **standard processing rates for context lengths under 270K**. The Batch columns reflect the flat 50% discount OpenAI applies to both input and output on the Batch API.

### GPT-5.2 and earlier GPT-5 pricing still published by OpenAI

| Model | Input $/M | Cached Input $/M | Output $/M |
|-------|-----------|------------------|------------|
| [GPT-5.2](/models/gpt-5-2) | $1.75 | $0.175 | $14.00 |
| [GPT-5.2 Pro](/models/gpt-5-2-pro) | $21.00 | — | $168.00 |
| [GPT-5.1](/models/gpt-5-1) | $1.25 | $0.125 | $10.00 |
| GPT-5 P]]></content:encoded>
            <author>Glevd</author>
            <category>pricing</category>
            <category>openai</category>
            <category>gpt-5</category>
            <category>api</category>
            <category>cost</category>
            <category>guide</category>
        </item>
        <item>
            <title><![CDATA[GPT-5 vs Gemini in 2026: Full Benchmark Breakdown]]></title>
            <link>https://benchlm.ai/blog/posts/gpt5-vs-gemini-2026</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/gpt5-vs-gemini-2026</guid>
            <pubDate>Thu, 09 Apr 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[GPT-5.4 and Gemini 3.1 Pro are one point apart on BenchLM's leaderboard. We compare coding, math, reasoning, multimodal, agentic, pricing, and context window — with Gemini Deep Think as the reasoning wildcard.]]></description>
            <content:encoded><![CDATA[
GPT-5.4 and Gemini 3.1 Pro are separated by a single point on BenchLM's overall leaderboard — 84 to 83. But the score hides a deeper story: these models represent fundamentally different bets on what frontier AI should be. OpenAI is building a reasoning-first agent OS. Google is building a natively multimodal platform and pricing it to win volume. And with Gemini 3 Pro Deep Think, Google now has a reasoning specialist that matches GPT-5.4 on the hardest problems while offering a 2M-token context window.

Here's how they actually compare.

## Quick comparison: GPT-5.4 vs Gemini 3.1 Pro vs Deep Think

| Category | GPT-5.4 | Gemini 3.1 Pro | Deep Think | Winner |
|---|---|---|---|---|
| **Overall Score** | 84 | 83 | 79 | GPT-5.4 (by 1 point) |
| **Type** | Reasoning | Non-Reasoning | Reasoning | — |
| **Context Window** | 1.05M | 1M | 2M | Deep Think |
| **SWE-bench Verified** | 84 | 75 | 58 | GPT-5.4 |
| **SWE-Pro** | 57.7 | 72 | 63 | Gemini 3.1 Pro |
| **AIME 2025** | 99 | — | 98 | GPT-5.4 / Deep Think |
| **MATH-500** | 99 | 97 | 92 | GPT-5.4 |
| **GPQA Diamond** | 92.8 | 94.3 | 97 | Deep Think |
| **MuSR** | 94 | 93 | 93 | GPT-5.4 |
| **LongBench v2** | — | 93 | 94 | Deep Think |
| **MRCRv2** | 97 | 90 | 96 | GPT-5.4 |
| **ARC-AGI-2** | 73.3 | 77.1 | 45.1 | Gemini 3.1 Pro |
| **BrowseComp** | 82.7 | 86 | 87 | Deep Think |
| **OSWorld** | 75 | 68 | 73 | GPT-5.4 |
| **MMMU-Pro** | 81.2 | 83.9 | 95 | Deep Think |
| **Price (in/out per 1M)** | $2.50 / $15 | $1.25 / $5 | TBD | Gemini 3.1 Pro |

No model sweeps the table. GPT-5.4 wins on math, factual recall, and desktop agents. Gemini 3.1 Pro wins on multimodal, real-world coding (SWE-Pro), and price. Deep Think wins the hardest reasoning benchmarks but trails on practical tasks.

## Coding: different strengths, different benchmarks

| Benchmark | GPT-5.4 | Gemini 3.1 Pro | Deep Think |
|---|---|---|---|
| [SWE-bench Verified](/benchmarks/sweVerified) | 84 | 75 | 58 |
| [SWE-bench Pro](/benchmarks/swePro) | 57.7 | 72 |]]></content:encoded>
            <author>Glevd</author>
            <category>comparison</category>
            <category>gpt-5</category>
            <category>gemini</category>
            <category>benchmarks</category>
            <category>guide</category>
        </item>
        <item>
            <title><![CDATA[Mythos Preview is the first frontier model Anthropic decided not to ship. The benchmarks show why.]]></title>
            <link>https://benchlm.ai/blog/posts/mythos-preview-anthropic-not-shipping</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/mythos-preview-anthropic-not-shipping</guid>
            <pubDate>Tue, 07 Apr 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Claude Mythos Preview beats Opus 4.6 by double digits on every coding benchmark Anthropic released. Then they shelved it. Here's what the numbers actually show, and why the shipping decision matters more than the launch.]]></description>
            <content:encoded><![CDATA[
# Mythos Preview is the first frontier model Anthropic decided not to ship. The benchmarks show why.

*Last updated April 7, 2026. All benchmark data sourced from [Anthropic's Project Glasswing announcement](https://www.anthropic.com/glasswing) and the [Claude Mythos Preview system card](https://www-cdn.anthropic.com/53566bf5440a10affd749724787c8913a2ae0841.pdf). See the model profile on [BenchLM](/models/claude-mythos-preview).*

A model autonomously found a remote crash bug in OpenBSD, one of the most security-hardened operating systems on earth, in code that had survived 27 years of human review.

That same model found a 16-year-old vulnerability in FFmpeg, in a single line of code that automated fuzzers had hit five million times without ever flagging. Then it located several Linux kernel vulnerabilities and chained them together to escalate from ordinary user access to full machine control. No human steering. No prompting tricks. The model just did it.

Anthropic built that model, watched it do all of this, and decided not to release it. They are calling it Claude Mythos Preview, and it is the most important thing Anthropic has announced this year — not because of what it can do, but because of what they chose not to do with it.

That's the part of the announcement worth paying attention to.

---

## What is Claude Mythos Preview?

[Claude Mythos Preview](/models/claude-mythos-preview) is an unreleased frontier model from Anthropic, announced April 2026 as part of [Project Glasswing](https://www.anthropic.com/glasswing). Glasswing is a coordinated industry effort built around Mythos that includes twelve launch partners: Amazon Web Services, Anthropic, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorgan Chase, the Linux Foundation, Microsoft, NVIDIA, and Palo Alto Networks. Forty additional organizations that build or maintain critical software infrastructure also have access. Anthropic put $100M of model usage credits behind it, and donated another $4M direc]]></content:encoded>
            <author>BenchLM</author>
            <category>anthropic</category>
            <category>claude</category>
            <category>mythos</category>
            <category>cybersecurity</category>
            <category>benchmarks</category>
            <category>agentic</category>
            <category>coding</category>
        </item>
        <item>
            <title><![CDATA[Best LLM for RAG in 2026: Top Models Ranked for Retrieval-Augmented Generation]]></title>
            <link>https://benchlm.ai/blog/posts/best-llm-rag</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/best-llm-rag</guid>
            <pubDate>Mon, 06 Apr 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[We ranked LLMs for RAG by IFEval, knowledge benchmarks, context window, long-context retrieval accuracy, and pricing. Here are the top models for retrieval-augmented generation in 2026.]]></description>
            <content:encoded><![CDATA[
The best LLM for RAG in 2026 is [GPT-5.4 Pro](/models/gpt-5-4-pro) for accuracy, [Gemini 3.1 Pro](/models/gemini-3-1-pro) for cost-efficiency, and [DeepSeek V3](/models/deepseek-v3) for open-source deployments.

RAG is the most common enterprise LLM architecture — retrieve relevant documents, pass them to a model, generate a grounded answer. The model you choose determines whether that answer is accurate, well-structured, and faithful to your source material. Three capabilities matter most: **instruction following** (does the model format answers as your system prompt dictates), **knowledge comprehension** (can it understand complex retrieved content), and **long-context retrieval** (does it actually use the documents you pass it).

## What matters in a RAG model

Not every benchmark matters for RAG. A model's coding or math score tells you nothing about how well it will ground answers in retrieved documents. Here's what does:

**[IFEval](/benchmarks/ifeval)** — Measures whether a model follows specific verifiable instructions. In RAG, this determines if the model respects your output format, citation requirements, and response constraints. A model that ignores "respond in JSON" or "cite your sources" is useless in production RAG.

**[GPQA](/benchmarks/gpqa) and knowledge benchmarks** — Models with stronger knowledge comprehension produce more accurate answers from retrieved technical content. GPQA Diamond tests PhD-level scientific reasoning — exactly the kind of content that enterprise RAG systems retrieve.

**[LongBench v2](/benchmarks/longbench-v2)** — Tests whether models can extract information from long passages. Critical for RAG systems that pass multiple retrieved chunks (often 10K-50K tokens total).

**MRCRv2** — Multi-hop reading comprehension. Tests whether models can connect information across multiple retrieved passages to answer complex questions. This is where cheap models fail hardest.

**Context window** — Sets the upper limit on how much retrieve]]></content:encoded>
            <author>Glevd</author>
            <category>rag</category>
            <category>retrieval</category>
            <category>knowledge</category>
            <category>comparison</category>
            <category>guide</category>
        </item>
        <item>
            <title><![CDATA[Best LLM for Writing in 2026: AI Models Ranked for Content Creation]]></title>
            <link>https://benchlm.ai/blog/posts/best-llm-writing</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/best-llm-writing</guid>
            <pubDate>Mon, 06 Apr 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Which AI model is best for writing in 2026? We rank Claude, GPT, Gemini, and open source LLMs by creative writing Arena scores, instruction-following benchmarks, and real-world content quality — with pricing for every budget.]]></description>
            <content:encoded><![CDATA[
The best LLM for writing in 2026 is [Claude Opus 4.6](/models/claude-opus-4-6) for long-form content, though [Gemini 3.1 Pro](/models/gemini-3-1-pro) leads on raw creative writing scores and costs 12x less on input tokens.

Writing quality is harder to benchmark than coding or math. There's no SWE-bench equivalent for prose — no single score that tells you which model writes the best blog post. Instead, we use a combination of [Arena creative writing Elo](https://arena.ai/leaderboard/text) (crowd-sourced human preference), instruction-following benchmarks ([IFEval](/benchmarks/ifeval)), and knowledge scores that affect factual accuracy.

## Top writing models, ranked

| Model | Arena Creative Writing | Arena Instruction Following | IFEval | MMLU | Price (in/out) |
|-------|----------------------|---------------------------|--------|------|----------------|
| Gemini 3.1 Pro | **1487** | 1490 | 95 | 99 | $1.25/$5 |
| Claude Opus 4.6 | 1468 | **1500** | 95 | 99 | $15/$75 |
| GPT-5.4 Pro | 1461 | 1488 | **97** | 99 | $30/$180 |
| Claude Sonnet 4.6 | 1443 | 1479 | 89.5 | 99 | $3/$15 |
| GLM-5 (Reasoning) | 1442 | 1445 | 92 | 96 | — |
| Grok 4.1 | 1431 | 1433 | 93 | 99 | $3/$15 |
| GPT-5.4 | 1423 | 1470 | 96 | 99 | $2.50/$15 |

*Scores from [BenchLM.ai](/). Arena Elo from [arena.ai](https://arena.ai/leaderboard/text). Prices per million tokens.*

Two metrics matter most for writing: **Arena Creative Writing** measures whether humans prefer one model's prose over another in blind comparisons. **IFEval** measures whether a model follows specific formatting and style instructions — critical for writers who need a particular tone, structure, or length.

## Claude Opus 4.6: the best writing model in 2026

Claude Opus 4.6 isn't the highest on Arena creative writing (Gemini 3.1 Pro leads by 18 Elo points). But it leads on instruction following — both on Arena's human-preference IF score (1500) and on IFEval (95).

Why does instruction following matter more than raw creative wri]]></content:encoded>
            <author>Glevd</author>
            <category>writing</category>
            <category>comparison</category>
            <category>ranking</category>
            <category>guide</category>
            <category>content</category>
        </item>
        <item>
            <title><![CDATA[How to Choose an LLM in 2026: Which AI Model Is Best for Your Use Case]]></title>
            <link>https://benchlm.ai/blog/posts/which-llm-to-use</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/which-llm-to-use</guid>
            <pubDate>Sat, 04 Apr 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[A step-by-step framework for choosing the right LLM in 2026. We compare Claude Opus 4.6, Gemini 3.1 Pro, GPT-5.4, DeepSeek, and open source models by use case, budget, and deployment needs — backed by benchmark data.]]></description>
            <content:encoded><![CDATA[
The right model depends on your use case. Here's the 60-second framework for choosing.

If you want a quick personalized recommendation, [take the 5-question quiz →](/tools/llm-selector). If you want to understand the reasoning behind the recommendations, keep reading.

## Start with your use case

This is the single most important decision. Every other factor — budget, speed, open source — is secondary to matching the model to what you actually need it to do.

### Coding

**Best choice: [Gemini 3.1 Pro](/models/gemini-3-1-pro)** — current BenchLM coding score of 94.3, leads on [SWE-bench Pro](/benchmarks/swePro) at 72, and costs just $1.25/$5 per million tokens.

**Runner-up: [GPT-5.4](/models/gpt-5-4)** — current coding score of 90.7, with 84 on both [SWE-bench Verified](/benchmarks/sweVerified) and [LiveCodeBench](/benchmarks/liveCodeBench). If you care most about the strongest raw coding benchmark rows, GPT-5.4 is still the safer pick.

**Writing-first alternative: [Claude Opus 4.6](/models/claude-opus-4-6)** — current coding score of 90.8 plus the best writing and editing quality of the three flagships. If you want one model for code and polished communication, Claude is still compelling despite the price.

**Budget alternative: [DeepSeek Coder 2.0](/models/deepseek-coder-2-0)** — scores 54 overall at just $0.27/$1.10 per million tokens. Strong enough for many production coding tasks.

→ [Full coding comparison](/blog/posts/best-llm-coding)

### Math and reasoning

**Best choice: [GPT-5.4](/models/gpt-5-4)** — AIME 2025: 99, BRUMO 2025: 97, MRCRv2: 97. The strongest mainstream reasoning model with broad published benchmark coverage.

**Runner-up: [Claude Opus 4.6](/models/claude-opus-4-6)** — AIME 2025: 98, HMMT 2025: 95. Remarkably strong math performance for a non-reasoning model, meaning faster responses.

**Open source: [GLM-5 (Reasoning)](/models/glm-5-reasoning)** — AIME 2025: 98, BRUMO 2025: 96. Matches frontier proprietary models on competition math.

]]></content:encoded>
            <author>Glevd</author>
            <category>guide</category>
            <category>decision-framework</category>
            <category>comparison</category>
            <category>selection</category>
        </item>
        <item>
            <title><![CDATA[Best Open Source LLM in 2026: Rankings, Benchmarks, and the Models Worth Running]]></title>
            <link>https://benchlm.ai/blog/posts/best-open-source-llm</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/best-open-source-llm</guid>
            <pubDate>Wed, 01 Apr 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Which open source LLM is best in 2026? We rank the top open weight models by real benchmark data — GLM-5, Qwen3.5, Gemma 4, Kimi K2.5, Llama — and compare them to proprietary leaders.]]></description>
            <content:encoded><![CDATA[
The best open source LLM right now is GLM-5 (Reasoning) from Zhipu AI, scoring 85 on BenchLM.ai's overall leaderboard. GLM-5.1 follows at 84, Qwen3.5 397B (Reasoning) sits at 81, and GLM-5 rounds out the next tier at 77.

That's a significant shift. Two years ago, Llama dominated the open source conversation. Today, Chinese labs — Zhipu AI, Alibaba, Moonshot AI, and DeepSeek — hold most of the top positions among open weight models, with Google's Gemma 4 31B breaking into the top 5. The best open source LLMs in 2026 are not where most people expect them to be.

## Top open source LLMs ranked by benchmarks

| Rank | Model | Creator | Overall | Context |
|------|-------|---------|---------|---------|
| 1 | [GLM-5 (Reasoning)](/models/glm-5-reasoning) | Zhipu AI | **85** | 200K |
| 2 | [GLM-5.1](/models/glm-5-1) | Zhipu AI | **84** | 203K |
| 3 | [Qwen3.5 397B (Reasoning)](/models/qwen3-5-397b-reasoning) | Alibaba | **81** | 128K |
| 4 | [GLM-5](/models/glm-5) | Zhipu AI | **77** | 200K |
| 5 | [Gemma 4 31B](/models/gemma-4-31b) | Google | **74** | 256K |
| 6 | [GLM-4.7](/models/glm-4-7) | Zhipu AI | **72** | 200K |
| 7 | [Kimi K2.5](/models/kimi-k2-5) | Moonshot AI | **68** | 128K |
| 8 | [Qwen3.5-122B-A10B](/models/qwen3-5-122b-a10b) | Alibaba | **68** | 262K |

*Scores from [BenchLM.ai open source leaderboard](/best/open-source). Overall score is BenchLM.ai's benchmark-weighted composite.*

This table reveals something non-obvious: the models with the highest overall scores are not always the ones with the best individual benchmark rows. Some open models still post stronger isolated coding results than GLM-5 (Reasoning), but GLM-5 (Reasoning) wins overall because its knowledge, reasoning, and math profile is much broader.

## How close are open source models to proprietary ones?

The honest answer: closer than ever, but still behind.

| Model | Type | Overall | MMLU | AIME 2025 | SWE-Verified | LiveCodeBench |
|-------|------|---------|------|-----------|----------]]></content:encoded>
            <author>Glevd</author>
            <category>open-source</category>
            <category>comparison</category>
            <category>ranking</category>
            <category>self-hosting</category>
            <category>guide</category>
        </item>
        <item>
            <title><![CDATA[Best Chinese LLMs in 2026: GLM-5, Kimi K2.5, DeepSeek V3.2, Qwen, and Every Model Ranked]]></title>
            <link>https://benchlm.ai/blog/posts/best-chinese-llm</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/best-chinese-llm</guid>
            <pubDate>Mon, 30 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Which Chinese LLM is best in 2026? We rank GLM-5, GLM-5.1, Qwen3.5, Kimi K2.5, DeepSeek V3.2, MiMo, and more using current BenchLM data across coding, math, reasoning, and agentic work.]]></description>
            <content:encoded><![CDATA[
The Chinese frontier is stronger and more crowded than the old GLM-vs-Qwen-vs-DeepSeek framing suggests. Z.AI now has the top two rows in this slice with GLM-5 (Reasoning) at 85 and GLM-5.1 at 84. Alibaba still has the broadest lineup. Moonshot's Kimi rows remain important, especially for coding. DeepSeek is still the cheapest widely known open-weight option, but it has fallen meaningfully behind the top Chinese entries on overall score.

## The top Chinese models right now

| Rank | Model | Creator | Score | Type | Open Weight | Context |
|------|-------|---------|-------|------|-------------|---------|
| 1 | [GLM-5 (Reasoning)](/models/glm-5-reasoning) | Z.AI | 85 | Reasoning | Yes | 200K |
| 2 | [GLM-5.1](/models/glm-5-1) | Z.AI | 84 | Non-Reasoning | Yes | 203K |
| 3 | [Qwen3.5 397B (Reasoning)](/models/qwen3-5-397b-reasoning) | Alibaba | 81 | Reasoning | Yes | 128K |
| 4 | [Kimi K2.5 (Reasoning)](/models/kimi-k2-5-reasoning) | Moonshot AI | 79 | Reasoning | No | 128K |
| 5 | [GLM-5](/models/glm-5) | Z.AI | 77 | Non-Reasoning | Yes | 200K |
| 6 | [Qwen3.6 Plus](/models/qwen3-6-plus) | Alibaba | 77 | Non-Reasoning | No | 1M |
| 7 | [GLM-4.7](/models/glm-4-7) | Z.AI | 72 | Reasoning | Yes | 200K |
| 8 | [Kimi K2.5](/models/kimi-k2-5) | Moonshot AI | 68 | Non-Reasoning | Yes | 128K |
| 9 | [Qwen3.5-122B-A10B](/models/qwen3-5-122b-a10b) | Alibaba | 68 | Non-Reasoning | Yes | 262K |
| 10 | [Qwen3.5 397B](/models/qwen3-5-397b) | Alibaba | 66 | Non-Reasoning | Yes | 128K |

The most important change here is that **GLM-5.1 is now ranked** and immediately sits near the very top. The second is that the Chinese leaderboard is no longer just one or two labs deep. Z.AI, Alibaba, and Moonshot all have serious rows in the upper tier.

## How the Chinese frontier compares to the global frontier

| Model | Creator | Score |
|-------|---------|-------|
| Gemini 3.1 Pro | Google | 94 |
| GPT-5.4 | OpenAI | 94 |
| Claude Opus 4.6 | Anthropic | 92 |
| **GLM-5 (Reasoning)** | **Z.AI]]></content:encoded>
            <author>Glevd</author>
            <category>chinese</category>
            <category>comparison</category>
            <category>deepseek</category>
            <category>qwen</category>
            <category>glm</category>
            <category>kimi</category>
            <category>step</category>
            <category>mimo</category>
            <category>ranking</category>
            <category>guide</category>
        </item>
        <item>
            <title><![CDATA[ChatGPT vs Claude vs Gemini in 2026: The Definitive Comparison]]></title>
            <link>https://benchlm.ai/blog/posts/chatgpt-vs-claude-vs-gemini-2026</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/chatgpt-vs-claude-vs-gemini-2026</guid>
            <pubDate>Mon, 30 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The best AI model depends on your use case. We compare Gemini 3.1 Pro, Claude Opus 4.6, and GPT-5.4 across coding, writing, reasoning, multimodal, price, and speed using current benchmark data.]]></description>
            <content:encoded><![CDATA[
The best AI model depends on your use case. GPT-5.4 and Gemini 3.1 Pro are now tied on overall score, GPT-5.4 leads on knowledge and agentic depth, Gemini offers the best value and multimodal profile, and Claude Opus 4.6 remains the strongest writing-first option. Here's how they compare on BenchLM's current data.

## Quick comparison: ChatGPT vs Claude vs Gemini

| Category | GPT-5.4 | Claude Opus 4.6 | Gemini 3.1 Pro | Winner |
|---|---|---|---|---|
| **Overall Score** | 94 | 92 | 94 | Tie (GPT-5.4 / Gemini 3.1 Pro) |
| **Coding Score** | 90.7 | 90.8 | 94.3 | Gemini 3.1 Pro |
| **Math Score** | 94.5 | 89.4 | 70.7 | GPT-5.4 |
| **Reasoning Score** | 93 | 90 | 97 | Gemini 3.1 Pro |
| **Agentic Score** | 93.5 | 92.6 | 87.8 | GPT-5.4 |
| **Multimodal Score** | 87.9 | 84.2 | 90.4 | Gemini 3.1 Pro |
| **Knowledge Score** | 97.6 | 92.4 | 95.6 | GPT-5.4 |
| **Speed** | Reasoning (slower) | Non-reasoning (faster) | Non-reasoning (faster) | Claude / Gemini |
| **Price (in/out)** | $2.50 / $15 | $15 / $75 | $1.25 / $5 | Gemini 3.1 Pro |
| **Context Window** | 1.05M | 1M | 1M | All comparable |

All three are frontier models. GPT-5.4 and Gemini 3.1 Pro are tied at 94 overall, with Claude Opus 4.6 just two points behind at 92. The practical winner still depends on which categories matter most to your workflow.

## GPT-5.4: Best for long-context work

GPT-5.4 is OpenAI's current flagship and is tied for the top overall score at 94 on BenchLM. It uses chain-of-thought reasoning at inference time, which adds latency but helps on the hardest problems.

### Strengths

**Coding.** GPT-5.4 still leads on individual coding benchmarks with 84 on both [SWE-bench Verified](/benchmarks/sweVerified) and [LiveCodeBench](/benchmarks/liveCodeBench). On BenchLM's current blended coding score it sits at 90.7, just behind Claude Opus 4.6 (90.8) and Gemini 3.1 Pro (94.3). Its raw SWE-bench and LiveCodeBench performance still make it one of the strongest repository-engineering models in the group]]></content:encoded>
            <author>Glevd</author>
            <category>comparison</category>
            <category>chatgpt</category>
            <category>claude</category>
            <category>gemini</category>
            <category>guide</category>
        </item>
        <item>
            <title><![CDATA[How LLM Token Pricing Works: A Complete Guide to API Costs in 2026]]></title>
            <link>https://benchlm.ai/blog/posts/llm-token-pricing</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/llm-token-pricing</guid>
            <pubDate>Thu, 26 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Learn how LLM API pricing works — from tokens, input/output costs, and reasoning tokens to vision, embedding, and fine-tuning pricing. Includes real cost examples, free tiers, and 6 strategies to cut your AI spend.]]></description>
            <content:encoded><![CDATA[
LLM APIs charge per token — typically $0.05 to $75 per million tokens depending on the model. A token is roughly 4 characters or 0.75 words. Here's exactly how pricing works, what drives the cost differences between models, and how to estimate and optimize your spend.

Want to check token counts right now? Try our [free LLM token counter](/tools/token-counter) — paste any text and see counts across GPT-5, Claude, Gemini, and more.

## What is a token?

A token is the basic unit of text that language models process. Rather than reading character-by-character or word-by-word, LLMs use **tokenizers** that split text into subword pieces.

Most modern LLMs use Byte-Pair Encoding (BPE), which learns common character sequences from training data. The result:

- Common words like "the" or "and" → 1 token
- Longer words like "hamburger" → 3 tokens ("Ham" + "bur" + "ger")
- Rare technical terms may become 4-5 tokens

**Rule of thumb:** 1 token ≈ 4 characters ≈ 0.75 words in English.

Different models use different tokenizers, so the same text produces different token counts. The differences are usually within 5-10% for English text but can be larger for code, non-Latin scripts, or specialized terminology.

You can check exact counts with our [LLM token counter](/tools/token-counter), which uses real tokenizers for OpenAI models and calibrated estimates for others.

## Input vs. output token pricing

Every LLM API charges separately for **input tokens** (your prompt, system message, and any context) and **output tokens** (the model's response). Output tokens always cost more — typically 2-5x the input price.

Why? Input tokens are processed in a single forward pass through the model. Output tokens require **autoregressive generation**: the model must predict each token one at a time, running a full probability calculation across its vocabulary for every single output token.

Here's what this looks like across major models:

| Model | Input $/M | Output $/M | Output/Input Rati]]></content:encoded>
            <author>Glevd</author>
            <category>pricing</category>
            <category>tokens</category>
            <category>cost</category>
            <category>guide</category>
            <category>api</category>
            <category>embeddings</category>
            <category>vision</category>
            <category>fine-tuning</category>
            <category>cost optimization</category>
            <category>free tier</category>
        </item>
        <item>
            <title><![CDATA[React Native Evals: The Mobile App Coding Benchmark Explained]]></title>
            <link>https://benchlm.ai/blog/posts/react-native-evals-mobile-benchmark</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/react-native-evals-mobile-benchmark</guid>
            <pubDate>Tue, 24 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[React Native Evals measures whether AI coding models can complete real React Native implementation tasks across navigation, animation, and async state. Here's what it tests, why it matters, and how it differs from SWE-bench and LiveCodeBench.]]></description>
            <content:encoded><![CDATA[
[React Native Evals](/benchmarks/reactNativeEvals) is one of the clearest examples of where AI coding benchmarks are heading next: less abstract algorithm work, more framework-specific product implementation. It is an open benchmark from Callstack focused on real React Native tasks, not generic Python patches or contest problems.

That makes it useful for a very specific reason. Benchmarks like [SWE-bench Verified](/benchmarks/sweVerified), [SWE-bench Pro](/benchmarks/swePro), and [LiveCodeBench](/benchmarks/liveCodeBench) tell you a lot about general coding strength. They do not tell you enough about whether a model understands the quirks of a production mobile stack.

## What React Native Evals tests

The public React Native Evals dashboard describes itself as an evaluation framework for AI coding agents on React Native code generation tasks. It emphasizes three things:

- working app behavior
- recommended architecture choices
- strict constraint adherence

The current public dashboard groups tasks into areas like navigation, animation, and async state. It also shows repeated runs, token usage, and cost, which makes it more operational than many older benchmark pages.

That is important because React Native work is rarely about one isolated function. It usually involves lifecycle behavior, state hydration, platform-friendly patterns, and library-specific integrations that are easy to get almost right but still ship broken UX.

## Current public leaderboard

As of the public March 24, 2026 overview snapshot, the top React Native Evals rows are:

| Model | Overall |
|---|---|
| [Composer 2](/models/composer-2) | 96.2 |
| [Claude Opus 4.6](/models/claude-opus-4-6) | 84.4 |
| [GPT-5.4](/models/gpt-5-4) | 82.6 |
| [GPT-5.3 Codex](/models/gpt-5-3-codex) | 80.9 |
| [Gemini 3.1 Pro](/models/gemini-3-1-pro) | 78.9 |
| [Claude Sonnet 4.6](/models/claude-sonnet-4-6) | 77.9 |
| [Kimi K2.5](/models/kimi-k2-5) | 74.9 |
| [GLM-5](/models/glm-5) | 74.2 |
| [Grok 4](/models/grok]]></content:encoded>
            <author>Glevd</author>
            <category>benchmarks</category>
            <category>coding</category>
            <category>react-native</category>
            <category>mobile</category>
            <category>explainer</category>
        </item>
        <item>
            <title><![CDATA[State of LLM Benchmarks 2026: Rankings, Trends, and What Actually Changed]]></title>
            <link>https://benchlm.ai/blog/posts/state-of-llm-benchmarks-2026</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/state-of-llm-benchmarks-2026</guid>
            <pubDate>Sun, 22 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[State of LLM benchmarks in 2026: current BenchLM rankings, category leaders, benchmark trends, open vs closed performance, and what still matters after the latest scoring changes.]]></description>
            <content:encoded><![CDATA[
The benchmark picture in April 2026 is different from the one many people still have in their head. The old story was simple: one or two headline models sat clearly above the field, older knowledge benchmarks still mattered too much, and open-weight rows were interesting but not yet close. The current data is messier and more useful.

The top of the leaderboard is now fragmented. Claude Mythos Preview sits at 99 overall, but the broader mainstream frontier cluster is tighter: Gemini 3.1 Pro and GPT-5.4 are tied at 94, with Claude Opus 4.6 and GPT-5.4 Pro at 92. Open-weight models have moved up too. GLM-5 (Reasoning) is at 85, GLM-5.1 at 84, and Qwen3.5 397B (Reasoning) at 81.

All data below reflects BenchLM's live dataset, last updated **April 8, 2026**.

## Key findings

- **The very top is no longer a single-model story.** Claude Mythos Preview leads at 99, but the broader mainstream frontier is a 94/94/92/92 cluster.
- **Coding is still one of the best separators.** Claude Mythos Preview, Gemini 3.1 Pro, GPT-5.4 Pro, Claude Opus 4.6, and GPT-5.4 all remain tightly grouped, but real spread still exists.
- **Agentic benchmarks still matter.** GPT-5.4 remains one of the clearest broad-purpose leaders on agentic work, while narrow specialist rows can still spike higher.
- **Open-weight rows are now real top-tier entrants.** GLM-5 (Reasoning), GLM-5.1, and Qwen3.5 397B (Reasoning) are not novelty rows anymore.
- **Benchmark choice matters more than ever.** The older saturated tests are still useful for context, but the frontier is now decided by harder benchmarks with meaningful spread.

## The overall leaderboard

### Top 10 models overall

| Rank | Model | Creator | Overall | Notes |
|------|-------|---------|---------|-------|
| 1 | Claude Mythos Preview | Anthropic | 99 | Current overall leader |
| 2 | Gemini 3.1 Pro | Google | 94 | Best value mainstream flagship |
| 3 | GPT-5.4 | OpenAI | 94 | Strongest broad OpenAI default |
| 4 | Claude Opus 4.6 | Anthropic |]]></content:encoded>
            <author>Glevd</author>
            <category>ranking</category>
            <category>benchmarks</category>
            <category>comparison</category>
            <category>guide</category>
            <category>llm</category>
        </item>
        <item>
            <title><![CDATA[Are AI Benchmarks Reliable? The Data Contamination Problem]]></title>
            <link>https://benchlm.ai/blog/posts/benchmark-reliability</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/benchmark-reliability</guid>
            <pubDate>Wed, 18 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[AI benchmarks are useful but flawed. Data contamination inflates scores when models train on test questions. Here's how it works, which benchmarks resist it, and how BenchLM accounts for reliability.]]></description>
            <content:encoded><![CDATA[
Are AI benchmarks reliable? Yes, but with important caveats. Benchmarks are the best standardized tool we have for comparing language models — but some benchmark scores are inflated by data contamination, and others have become too easy to differentiate frontier models. Understanding which benchmarks you can trust changes how you read every leaderboard.

The single biggest threat to benchmark reliability is data contamination: when a model has seen the test questions during training.

## What is data contamination?

Data contamination happens when an LLM's training data includes questions, answers, or closely paraphrased versions of the benchmark used to evaluate it. The model doesn't need to memorize test cases verbatim — even partial exposure to similar problem patterns can inflate scores.

Think of it like a student who studied from a leaked exam. They might score 95% on that specific test, but give them a different exam on the same material and they might score 70%. The first score measures memorization; the second measures understanding. Data contamination creates the same gap in LLM evaluation.

Training data for large language models is scraped from the open internet at massive scale. If a benchmark's test questions have been publicly available for years — on GitHub, in research papers, in blog posts discussing the answers — there's a strong probability they ended up in the training corpus. No amount of post-hoc filtering fully eliminates this risk.

## How contamination affects benchmark scores

The effects are concrete and measurable:

**Inflated scores.** A model trained on contaminated data scores higher than its genuine capability warrants. Studies have documented 5-15+ point score inflation from contamination on popular benchmarks.

**False differentiation.** Two models with similar real-world ability can show a 10-point benchmark gap if one was trained on data that happened to include more benchmark questions. The leaderboard ranking becomes noise rat]]></content:encoded>
            <author>Glevd</author>
            <category>benchmarking</category>
            <category>data-contamination</category>
            <category>llm</category>
            <category>evaluation</category>
            <category>reliability</category>
        </item>
        <item>
            <title><![CDATA[Best Budget LLMs in 2026: GPT-5.4 Mini, Nano, MiniMax M2.7, and Every Cheap Model Ranked]]></title>
            <link>https://benchlm.ai/blog/posts/best-budget-llms-2026</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/best-budget-llms-2026</guid>
            <pubDate>Wed, 18 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Which budget LLM should you use in 2026? We rank GPT-5.4 mini, GPT-5.4 nano, MiniMax M2.7, Claude Haiku 4.5, Gemini Flash, DeepSeek, and more by benchmarks and price.]]></description>
            <content:encoded><![CDATA[
GPT-5.4 mini and nano just landed alongside MiniMax M2.7 — three new budget models in 48 hours. The capability floor keeps rising while prices drop. GPT-5.4 mini brings reasoning-class intelligence to $0.75/M input. MiniMax M2.7 quietly beats it on SWE-bench Pro at less than half the price.

This guide ranks every major LLM under $1.50 per million input tokens by benchmark performance, with pricing breakdowns and use-case recommendations. All scores from the [BenchLM.ai leaderboard](/) and [pricing page](/llm-pricing).

## The budget tier landscape (March 2026)

There are now more than 15 models priced under $1.50/M input tokens. The quality range is enormous — from GPT-5 nano at $0.05/M input to Gemini 3.1 Pro at $1.25/M scoring 94 overall.

### Ultra-budget: under $0.50/M input

| Model | Creator | Input/Output | Context | Overall Score | Type |
|-------|---------|-------------|---------|---------------|------|
| GPT-5 nano | OpenAI | $0.05/$0.40 | 400K | 36 | Reasoning |
| Seed 1.6 Flash | ByteDance | $0.08/$0.30 | 256K | — |  |
| Gemini 3.1 Flash-Lite | Google | $0.10/$0.40 | 1M | — |  |
| Step 3.5 Flash | StepFun | $0.10/$0.30 | 256K | — |  |
| GPT-5.4 nano | OpenAI | $0.20/$1.25 | 400K | 58 | Reasoning |
| Mercury 2 | Inception | $0.25/$0.75 | 128K | — |  |
| DeepSeek V3 | DeepSeek | $0.27/$1.10 | 128K | 49 | Non-Reasoning |
| DeepSeek Coder 2.0 | DeepSeek | $0.27/$1.10 | 128K | 62 | Non-Reasoning |
| MiniMax M2.7 | MiniMax | $0.30/$1.20 | 200K | 60* | Non-Reasoning |
| Grok 3 Mini | xAI | $0.30/$0.50 | 128K | 49* | Non-Reasoning |

*\*MiniMax M2.7 and Grok 3 Mini still have sparse coverage relative to the best-supported frontier rows, so treat their overall scores as directional rather than definitive.*

### Budget-frontier: $0.50–$1.50/M input

| Model | Creator | Input/Output | Context | Overall Score | Type |
|-------|---------|-------------|---------|---------------|------|
| Gemini 3 Flash | Google | $0.50/$3.00 | 1M | 67 | Non-Reasoning |
| Kimi K2.5 |]]></content:encoded>
            <author>Glevd</author>
            <category>budget</category>
            <category>comparison</category>
            <category>pricing</category>
            <category>guide</category>
            <category>ranking</category>
        </item>
        <item>
            <title><![CDATA[Best LLM for Coding in 2026: Ranked by SWE-bench, LCB, and Real-World Performance]]></title>
            <link>https://benchlm.ai/blog/posts/best-llm-coding</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/best-llm-coding</guid>
            <pubDate>Thu, 12 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Which AI model is best for coding in 2026? We rank major LLMs by SWE-bench Pro and LiveCodeBench, with SWE-bench Verified shown as a historical baseline and React Native Evals tracked as a display benchmark for mobile app work.]]></description>
            <content:encoded><![CDATA[
The coding leaderboard changed after BenchLM started weighting SWE-Rebench properly. GPT-5.4 now leads the current coding table at 73.9, followed by Claude Opus 4.6 at 72.5 and Kimi K2.5 (Reasoning) at 70.4.

BenchLM.ai's current coding score weights [SWE-Rebench](/benchmarks/sweRebench), [SWE-bench Pro](/benchmarks/swePro), [LiveCodeBench](/benchmarks/liveCodeBench), and [SWE-bench Verified](/benchmarks/sweVerified). HumanEval is still useful as context, but it is too saturated to drive the main coding rank by itself.

One newer display benchmark worth watching is [React Native Evals](/benchmarks/reactNativeEvals). It does not affect BenchLM's weighted coding rank today, but it fills a real coverage gap by testing framework-specific mobile app implementation work that generic repository and competitive-programming benchmarks do not capture well. If React Native or Expo-style product work matters in your stack, read the [React Native Evals explainer](/blog/posts/react-native-evals-mobile-benchmark) alongside the main coding leaderboard.

## Top coding models, ranked

| Model | SWE-Rebench | SWE-bench Pro | LiveCodeBench | SWE-bench Verified | Coding score |
|-------|-------------|---------------|---------------|--------------------|--------------|
| GPT-5.4 | — | 57.7 | 84 | 84 | **73.9** |
| Claude Opus 4.6 | 65.3 | 74 | 76 | 80.8 | 72.5 |
| Kimi K2.5 (Reasoning) | 57.4 | 70 | 85 | 76.8 | 70.4 |
| GPT-5.2 | — | 55.6 | 79 | 80 | 70.2 |
| GLM-4.7 | — | 51 | 84.9 | 73.8 | 69.3 |
| Gemini 3.1 Pro | 62.3 | 72 | 71 | 75 | 68.8 |
| GPT-5.3 Codex | 58.2 | 56.8 | 85 | 85 | 68.6 |
| MiMo-V2-Flash | — | 52 | 80.6 | 73.4 | 67.9 |
| Grok 4 | — | 48 | 79.4 | 73 | 65.8 |
| MiniMax M2.7 | — | 56.22 | — | 78 | 64.4 |
| Claude Sonnet 4.6 | 60.7 | 64 | 54 | 79.6 | 62.7 |
| GLM-5 (Reasoning) | — | 67 | 58 | 62 | 62.4 |

*Scores from [BenchLM.ai leaderboard](/coding). Prices per million tokens.*

## GPT-5.3 Codex: the best coding model in 2026

GPT-5.4 now leads the coding leaderboard]]></content:encoded>
            <author>Glevd</author>
            <category>coding</category>
            <category>comparison</category>
            <category>swe-bench</category>
            <category>guide</category>
            <category>ranking</category>
        </item>
        <item>
            <title><![CDATA[BrowseComp Explained: How We Measure Web Research Agents]]></title>
            <link>https://benchlm.ai/blog/posts/browsecomp-browsing-benchmark</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/browsecomp-browsing-benchmark</guid>
            <pubDate>Thu, 12 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[BrowseComp evaluates whether AI models can search the web, gather evidence, and answer research questions instead of relying only on latent knowledge.]]></description>
            <content:encoded><![CDATA[
BrowseComp tests whether an AI model can find answers on the web, not just recall them from training. A model must plan a search, inspect sources, filter noise, and synthesize a correct answer. It is one of the most important benchmarks for evaluating research agents and web-integrated AI workflows.

BrowseComp is a benchmark for a very specific skill: finding the answer on the web when the answer is not already obvious from the model's internal knowledge.

That makes it one of the best public tests for research-oriented agents.

## What BrowseComp tests

The model has to:

1. decide what to search for
2. open and inspect sources
3. gather relevant evidence
4. avoid shallow or misleading pages
5. synthesize a correct answer

This is a different problem than scoring well on [MMLU](/benchmarks/mmlu) or [GPQA](/benchmarks/gpqa). Those knowledge benchmarks mostly test what the model already knows. BrowseComp tests whether it can **go get** what it needs.

## Why it matters

Many practical AI workflows now involve web research:

- market scans
- competitor analysis
- technical documentation lookup
- citation gathering
- open-ended question answering

If a model is weak at browsing, it may still sound confident while missing key evidence. BrowseComp helps separate fluent models from models that can actually do useful research.

## What a high score usually means

A strong BrowseComp score suggests the model is better at:

- planning a search strategy
- filtering noisy sources
- staying grounded in evidence
- answering with more factual discipline

It does not automatically make the model the best option for coding or math. It makes it a stronger candidate for research-heavy products and assistants.

## Best companion benchmarks

BrowseComp is especially useful when paired with:

- [SimpleQA](/benchmarks/simpleQa) for short-form factual accuracy
- [HLE](/benchmarks/hle) for frontier-difficulty knowledge
- [OSWorld-Verified](/benchmarks/osWorldVerified) for full workflow e]]></content:encoded>
            <author>Glevd</author>
            <category>benchmarks</category>
            <category>agentic</category>
            <category>research</category>
            <category>browsecomp</category>
            <category>explainer</category>
        </item>
        <item>
            <title><![CDATA[Claude Opus 4.6 vs GPT-5.4: Full Benchmark Breakdown (2026)]]></title>
            <link>https://benchlm.ai/blog/posts/claude-opus-vs-gpt-5</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/claude-opus-vs-gpt-5</guid>
            <pubDate>Thu, 12 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Claude Opus 4.6 vs GPT-5.4 head-to-head: current benchmark scores, pricing, and where each model actually wins. GPT-5.4 now leads overall, while Claude stays extremely close and still has real workflow-specific advantages.]]></description>
            <content:encoded><![CDATA[
GPT-5.4 now leads Claude Opus 4.6 on BenchLM's overall leaderboard, 94 to 92. That is the headline change. The more important point is that this is not a blowout. Claude is still extremely close on coding and agentic work, while GPT-5.4 keeps the cleaner edge on overall score, knowledge, math, and price-adjusted practicality.

If you only look at one or two raw benchmarks, you can still make either model look like the winner. GPT-5.4 wins the broader scoreboard. Claude still has real reasons to choose it, especially if your work is writing-heavy, latency-sensitive, or dependent on interaction quality rather than only the headline score.

## Current snapshot

| Metric | GPT-5.4 | Claude Opus 4.6 |
|--------|---------|-----------------|
| Overall score | **94** | 92 |
| Overall rank | **#3** | #4 |
| Coding score | 90.7 | **90.8** |
| Agentic score | **93.5** | 92.6 |
| Knowledge score | **97.6** | 92.4 |
| Math score | **94.5** | 89.4 |
| Price (in/out) | **$2.50 / $15** | $15 / $75 |
| Context window | 1.05M | 1M |

The category-level picture is clearer than the old 85-vs-82 framing ever was. Claude is still basically tied on coding, still close on agentic work, and still easier to justify when response style matters. GPT-5.4 is the stronger broad default because it combines a slightly higher overall score with much stronger cost efficiency and better knowledge depth.

## Raw benchmark comparison

| Benchmark | GPT-5.4 | Claude Opus 4.6 | Gap |
|-----------|---------|-----------------|-----|
| HLE | 48 | **53** | +5 Claude |
| GPQA | **92.8** | 91.3 | +1.5 GPT |
| MMLU-Pro | **93** | 82 | +11 GPT |
| SWE-bench Pro | 57.7 | **74** | +16.3 Claude |
| SWE-bench Verified | **84** | 80.8 | +3.2 GPT |
| LiveCodeBench | **84** | 76 | +8 GPT |
| Terminal-Bench 2.0 | **75.1** | 65.4 | +9.7 GPT |
| OSWorld-Verified | **75** | 72.7 | +2.3 GPT |
| BrowseComp | 82.7 | **83.7** | +1 Claude |
| SimpleQA | **97** | 72 | +25 GPT |
| LongBench v2 | **95** | 92 | +3 GPT |
| MRCRv2 | ]]></content:encoded>
            <author>Glevd</author>
            <category>comparison</category>
            <category>claude</category>
            <category>gpt-5</category>
            <category>benchmarks</category>
            <category>guide</category>
        </item>
        <item>
            <title><![CDATA[LLM API Pricing Comparison 2026: Every Major Model, Ranked by Cost]]></title>
            <link>https://benchlm.ai/blog/posts/llm-pricing-2026</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/llm-pricing-2026</guid>
            <pubDate>Thu, 12 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Full LLM API pricing comparison for 2026 — input/output token costs for GPT-5, Claude, Gemini, DeepSeek, Grok, and more. Find the cheapest model for your use case.]]></description>
            <content:encoded><![CDATA[
GPT-5 nano is the cheapest major LLM API at $0.05 per million input tokens. GPT-5.4 Pro is the most expensive at $30/$180. Claude Opus 4.6 costs $15/$75. For most production workloads, GPT-5.4 at $2.50/$15 still hits one of the best balances of capability and cost.

Pricing varies by more than 600x across major LLM APIs — from $0.05 to $30 per million input tokens. The right model for your workload depends on the task, volume, and how much quality you're trading for cost. This guide covers current pricing for every major model and breaks down the math for the most common use cases.

All prices are per million tokens. Check the [BenchLM.ai pricing page](/llm-pricing) for live pricing — rates change frequently.

## Full price table (March 2026)

| Model | Creator | Input | Output | Overall Score |
|-------|---------|-------|--------|---------------|
| GPT-5 nano | OpenAI | $0.05 | $0.40 | — |
| Gemini 3.1 Flash-Lite | Google | $0.10 | $0.40 | — |
| DeepSeek V3 | DeepSeek | $0.27 | $1.10 | — |
| DeepSeek Coder 2.0 | DeepSeek | $0.27 | $1.10 | — |
| Grok 3 Mini | xAI | $0.30 | $0.50 | — |
| Gemini 3 Flash | Google | $0.50 | $3.00 | — |
| DeepSeek R1 | DeepSeek | $0.55 | $2.19 | — |
| Gemini 3.1 Pro | Google | $1.25 | $5.00 | 94 |
| GPT-5.1 | OpenAI | $1.50 | $6.00 | 67 |
| GPT-5.2 Instant | OpenAI | $1.50 | $6.00 | 64 |
| GPT-5.3 Instant | OpenAI | $1.75 | $14.00 | 65 |
| GPT-5.2 | OpenAI | $2.00 | $8.00 | 77 |
| GPT-5.2-Codex | OpenAI | $2.00 | $8.00 | 73 |
| GPT-5.3-Codex-Spark | OpenAI | $2.00 | $8.00 | 63 |
| GPT-5.3 Codex | OpenAI | $2.50 | $10.00 | 80 |
| GPT-5.4 | OpenAI | $2.50 | $15.00 | 94 |
| Claude Sonnet 4.6 | Anthropic | $3.00 | $15.00 | 68 |
| Grok 4.1 | xAI | $3.00 | $15.00 | 76 |
| Mistral Large 3 | Mistral | $2.00 | $6.00 | — |
| Claude Opus 4.6 | Anthropic | $15.00 | $75.00 | 85 |
| GPT-5.2 Pro | OpenAI | $25.00 | $150.00 | 66 |
| GPT-5.4 Pro | OpenAI | $30.00 | $180.00 | 91 |

*Benchmark scores from [BenchLM.ai leaderboard](/). Prices per million to]]></content:encoded>
            <author>Glevd</author>
            <category>pricing</category>
            <category>comparison</category>
            <category>cost</category>
            <category>api</category>
            <category>guide</category>
        </item>
        <item>
            <title><![CDATA[OSWorld-Verified Explained: How We Measure Computer-Use Models]]></title>
            <link>https://benchlm.ai/blog/posts/osworld-verified-computer-use-benchmark</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/osworld-verified-computer-use-benchmark</guid>
            <pubDate>Thu, 12 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[OSWorld-Verified measures whether AI models can operate software interfaces and complete multi-step computer tasks with reliability.]]></description>
            <content:encoded><![CDATA[
OSWorld-Verified tests whether AI models can operate real software interfaces — not just describe how software works. The model must observe a screen, choose actions, maintain state across many steps, and recover from mistakes. It is one of the best public benchmarks for computer-use reliability in 2026.

OSWorld-Verified is about whether a model can use software, not just describe how software should be used.

That difference is what makes computer-use benchmarks so important now.

## What OSWorld-Verified measures

The benchmark puts models into interface-driven tasks where they need to:

1. understand the current screen or environment
2. choose the next action
3. keep state across many steps
4. avoid destructive mistakes
5. finish the workflow correctly

This is much closer to what people mean when they talk about AI assistants that can operate tools, apps, and desktop-style workflows.

## Why it matters

Computer-use models are increasingly used for:

- operations workflows
- QA and testing
- repetitive back-office tasks
- spreadsheet and document tasks
- multi-app automation

Those products fail if the model is only "smart in chat." They need models that can stay coherent while acting inside an interface.

## What makes it difficult

Computer-use is harder than ordinary prompt-response interaction because the model has to deal with:

- partial observability
- ambiguous UI states
- long action chains
- action recovery after mistakes
- the gap between planning and execution

That is why the spread on computer-use benchmarks is often more informative than the spread on saturated academic tests.

## How to read it

Use OSWorld-Verified alongside:

- [Terminal-Bench 2.0](/benchmarks/terminalBench2) for terminal-heavy agent tasks
- [BrowseComp](/benchmarks/browseComp) for web research and evidence gathering
- [IFEval](/benchmarks/ifeval) for instruction discipline

That combination gives you a better read on whether the model can follow instructions, act reliably, a]]></content:encoded>
            <author>Glevd</author>
            <category>benchmarks</category>
            <category>agentic</category>
            <category>computer-use</category>
            <category>osworld</category>
            <category>explainer</category>
        </item>
        <item>
            <title><![CDATA[Terminal-Bench 2.0 Explained: How We Measure Agentic Coding]]></title>
            <link>https://benchlm.ai/blog/posts/terminal-bench-2-agentic-benchmark</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/terminal-bench-2-agentic-benchmark</guid>
            <pubDate>Thu, 12 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Terminal-Bench 2.0 measures whether AI models can work through real terminal-based coding and ops workflows instead of just answering in chat.]]></description>
            <content:encoded><![CDATA[
Terminal-Bench 2.0 tests whether an AI model can actually work in a terminal — inspect files, run commands, debug failures, and finish multi-step tasks. It exists because chat-style coding benchmarks no longer reveal whether a model is a capable coding agent. Models that look identical on HumanEval often separate sharply here.

Terminal-Bench 2.0 exists because chat-style coding benchmarks are no longer enough.

If a model can solve a function-completion task but falls apart once it needs to inspect files, run commands, debug failures, and keep track of state across steps, it is not a strong coding agent. Terminal-Bench 2.0 is built to expose exactly that gap.

## What Terminal-Bench 2.0 tests

The benchmark puts models into realistic terminal-style software workflows. Instead of asking for a single answer, it asks the model to:

1. inspect the environment
2. read and edit files
3. run commands
4. recover from errors
5. finish the task end-to-end

That makes it much closer to how coding agents are actually used in products.

## Why it matters

Benchmarks like [HumanEval](/benchmarks/humaneval) still tell you whether a model can write code from a prompt. Terminal-Bench 2.0 tells you whether the model can operate like an agent inside a repo or shell.

That distinction matters more in 2026 than it did even a year ago. The most valuable models are no longer the ones that simply autocomplete well. They are the ones that can complete real workflows with fewer interventions.

## What a good score means

A strong Terminal-Bench 2.0 score usually implies:

- strong coding fundamentals
- good step-by-step reasoning under uncertainty
- better recovery after failures
- stronger tool-use discipline

It does **not** necessarily mean the model is the best pure chat model or the best writer. This is a benchmark for execution under constraints.

## How to use it with other benchmarks

If you care about developer agents, Terminal-Bench 2.0 is best read alongside:

- [SWE-bench Verif]]></content:encoded>
            <author>Glevd</author>
            <category>benchmarks</category>
            <category>agentic</category>
            <category>coding</category>
            <category>terminal-bench</category>
            <category>explainer</category>
        </item>
        <item>
            <title><![CDATA[What Do LLM Benchmarks Actually Measure?]]></title>
            <link>https://benchlm.ai/blog/posts/what-benchmarks-measure</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/what-benchmarks-measure</guid>
            <pubDate>Thu, 12 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[LLM benchmarks don't measure intelligence. They measure specific, narrow abilities under controlled conditions. Here's what each benchmark type actually tests — and what it misses.]]></description>
            <content:encoded><![CDATA[
LLM benchmarks measure specific, narrow abilities under controlled conditions — not intelligence, not usefulness, not whether a model will work well in your product. A benchmark is a dataset of test cases with a scoring method. What it tells you depends entirely on what tasks it contains and how they're scored.

Understanding what different benchmark types actually test changes how you read every leaderboard.

## The fundamental constraint

Every benchmark is a proxy. It approximates some real-world ability using tasks that can be scored automatically and consistently. The approximation is imperfect, and the gap between benchmark performance and real-world performance varies a lot depending on how closely the benchmark resembles your actual use case.

This is why models with nearly identical overall scores can feel completely different to use. Aggregate scores obscure which specific capabilities each model is strong or weak in.

## What different benchmark types measure

### Knowledge benchmarks (MMLU, GPQA, HLE)

These measure whether a model can recall correct information from training data and reason over it. Most use multiple-choice format with a fixed set of answer options.

The core limitation: they test static knowledge at training time. A model that memorized the right answers to MMLU questions but can't reason about novel problems will score well. A model with excellent reasoning but gaps in specific facts will score poorly.

The saturation problem compounds this. MMLU is now meaningless for frontier model comparison — GPT-5.4 and Claude Opus 4.6 both score 99% and neither tells you which model knows more. [HLE](/benchmarks/hle) (10-47% range) and [SuperGPQA](/benchmarks/superGpqa) (55-95%) are the useful knowledge signals in 2026.

**What knowledge benchmarks miss:** They don't test whether a model can apply knowledge to novel problems, synthesize information across sources, or acknowledge what it doesn't know.

### Coding benchmarks (HumanEval, SWE-bench]]></content:encoded>
            <author>Glevd</author>
            <category>llm</category>
            <category>benchmarking</category>
            <category>evaluation</category>
            <category>explainer</category>
            <category>ai-evaluation</category>
        </item>
        <item>
            <title><![CDATA[AIME & HMMT: Can AI Models Do Competition Math?]]></title>
            <link>https://benchlm.ai/blog/posts/aime-hmmt-competition-math</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/aime-hmmt-competition-math</guid>
            <pubDate>Sat, 07 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[AIME and HMMT are high school math olympiad competitions now used to benchmark AI. Frontier models score 95-99% — competition math is effectively solved. Here's what that means.]]></description>
            <content:encoded><![CDATA[
Frontier AI models now score 95-99% on AIME and HMMT — competition math is effectively solved. The top 5 models are within 2 points of each other on both benchmarks. For comparing frontier models on math in 2026, BRUMO and MATH-500 provide more signal. AIME and HMMT remain useful as display benchmarks and floor checks for mid-tier models, but BenchLM.ai no longer weights them into the math score.

The American Invitational Mathematics Examination (AIME) and Harvard-MIT Mathematics Tournament (HMMT) are prestigious math competitions designed for the most talented high school students. They've become standard AI benchmarks — and the results are striking.

Frontier models now score 95-99% on these competitions. Competition-level math is, for practical purposes, solved by AI.

## AIME: What it tests

AIME is a 15-question, 3-hour examination. Each answer is an integer from 000 to 999. The problems require creative mathematical insight across algebra, geometry, number theory, and combinatorics.

In human competition, qualifying for AIME puts a student in the top ~5% nationally. A perfect score is exceptionally rare — in most years, fewer than a handful of students achieve it.

What makes AIME challenging is that problems rarely require advanced mathematical knowledge. Instead, they demand creative problem-solving: seeing non-obvious connections, applying techniques in novel ways, and constructing multi-step proofs. This is precisely why AIME became popular as an AI benchmark — it tests genuine mathematical reasoning.

We track three years: [AIME 2023](/benchmarks/aime2023), [AIME 2024](/benchmarks/aime2024), and [AIME 2025](/benchmarks/aime2025). Tracking multiple years helps detect whether models memorized specific problem sets or have generalizable math ability.

## HMMT: What it tests

HMMT is hosted jointly by Harvard and MIT and is one of the most competitive high school math tournaments in the US. Problems span algebra, geometry, combinatorics, and number theory a]]></content:encoded>
            <author>Glevd</author>
            <category>benchmarks</category>
            <category>math</category>
            <category>aime</category>
            <category>hmmt</category>
            <category>explainer</category>
        </item>
        <item>
            <title><![CDATA[Best LLM for Coding in 2026: What the Benchmarks Actually Show]]></title>
            <link>https://benchlm.ai/blog/posts/best-llm-for-coding</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/best-llm-for-coding</guid>
            <pubDate>Sat, 07 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[We ranked every major LLM by BenchLM's current coding formula — SWE-Rebench, SWE-bench Pro, LiveCodeBench, and SWE-bench Verified. Here's which models actually come out on top and why.]]></description>
            <content:encoded><![CDATA[
GPT-5.4 Pro is currently the top-ranked LLM for coding on BenchLM at 88.3, with Claude Opus 4.6 at 79.3 and Gemini 3.1 Pro at 77.8 close behind. The important change is methodological: BenchLM now gives real weight to SWE-Rebench in addition to SWE-bench Pro, LiveCodeBench, and SWE-bench Verified.

That change matters because it downweights one-off spikes and rewards fresher repository-style engineering signals. Models that looked artificially dominant when SWE-Rebench was ignored no longer sit at the top by default.

BenchLM's current coding score weights:

- SWE-Rebench: 35%
- SWE-bench Pro: 25%
- LiveCodeBench: 25%
- SWE-bench Verified: 15%

Here are the current top coding rows.

## The top 10 coding models

| Rank | Model | Type | SWE-Rebench | SWE-bench Pro | LiveCodeBench | Coding score |
|------|-------|------|-------------|---------------|---------------|--------------|
| 1 | GPT-5.4 Pro | Reasoning | — | 89 | 86 | 88.3 |
| 2 | Claude Opus 4.6 | Non-Reasoning | 65.3 | — | 76 | 79.3 |
| 3 | Gemini 3.1 Pro | Non-Reasoning | 62.3 | 72 | 71 | 77.8 |
| 4 | GPT-5.4 | Reasoning | — | 57.7 | 84 | 76.1 |
| 5 | GPT-5.2 | Reasoning | — | 55.6 | 79 | 75.6 |
| 6 | GPT-5.3 Codex | Reasoning | 58.2 | 56.8 | 85 | 75.1 |
| 7 | GPT-5.1-Codex-Max | Reasoning | — | 84 | 67 | 74.2 |
| 8 | Claude Sonnet 4.6 | Non-Reasoning | 60.7 | — | — | 74.2 |
| 9 | Grok 4.1 | Non-Reasoning | — | — | 73 | 73.9 |
| 10 | GPT-5.2-Codex | Reasoning | 56.8 | 86 | 66 | 73.2 |

Full rankings with filters: [Best LLMs for Coding](/coding).

## HumanEval is basically maxed out

Look at the HumanEval column. Six models score 91. Two more score 94-95. The benchmark has a ceiling problem — it tests function-level Python generation, and frontier models have gotten too good at it. HumanEval now tells you almost nothing about whether Model A is better than Model B at real coding work.

SWE-bench Verified and LiveCodeBench are where the actual separation happens. SWE-bench tests multi-file bug fixes in real G]]></content:encoded>
            <author>Glevd</author>
            <category>coding</category>
            <category>benchmarks</category>
            <category>comparison</category>
            <category>guide</category>
        </item>
        <item>
            <title><![CDATA[What Is Chatbot Arena Elo? How Human Preference Drives Rankings]]></title>
            <link>https://benchlm.ai/blog/posts/chatbot-arena-elo-explained</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/chatbot-arena-elo-explained</guid>
            <pubDate>Sat, 07 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Chatbot Arena ranks AI models using Elo ratings from blind human preference votes. Here's how the system works, what the scores mean, and how Elo compares to standardized benchmarks.]]></description>
            <content:encoded><![CDATA[
Chatbot Arena ranks AI models through blind human preference votes. Two anonymous models respond to your prompt, you pick the better one, and the results feed an Elo system. It captures what benchmarks can't: how a model feels to use. But Elo is not accuracy — it is preference. The two are not the same, and treating them as such is one of the most common mistakes in model selection.

Chatbot Arena (also called LMSYS Arena or Arena AI) is a platform where humans compare AI model outputs in blind head-to-head matchups. Users submit a prompt, two anonymous models respond, and the user picks which response they prefer. The results feed an Elo rating system — the same system used to rank chess players.

Arena Elo has become one of the most influential AI evaluation methods because it captures something benchmarks can't: how a model actually feels to use.

## How Elo works for AI

The Elo system was designed for chess in the 1960s and works on a simple principle: if you beat a high-rated opponent, your rating goes up more than if you beat a low-rated one. Over thousands of matchups, ratings converge on a model's "true" relative strength.

In Chatbot Arena:
- Each model starts with a default rating
- Every human preference vote updates both models' ratings
- More votes mean more accurate ratings
- Ratings are relative — they only measure how models compare to each other

Current top Arena Elo scores tracked on BenchLM.ai range from ~1200 for older models to ~1440 for frontier models.

### Understanding the Elo scale

A 100-point Elo difference translates to roughly a 64% expected win rate. So a model rated 1400 should beat a model rated 1300 about 64% of the time. At the top of the leaderboard, models are separated by only 20-40 Elo points — meaning matchups between frontier models are genuinely close, with the stronger model winning only 53-56% of the time.

When you see GPT-5.4 at Elo 1435 and Claude Opus 4.6 at Elo 1420, the practical difference is smaller than it look]]></content:encoded>
            <author>Glevd</author>
            <category>benchmarks</category>
            <category>arena</category>
            <category>elo</category>
            <category>explainer</category>
        </item>
        <item>
            <title><![CDATA[Claude Opus 4.6 vs GPT-5.4: Where Each Model Actually Wins]]></title>
            <link>https://benchlm.ai/blog/posts/claude-opus-4-6-vs-gpt-5-4</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/claude-opus-4-6-vs-gpt-5-4</guid>
            <pubDate>Sat, 07 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[A direct benchmark comparison of Claude Opus 4.6 and GPT-5.4 on current BenchLM data. GPT-5.4 now leads overall, while Claude remains highly competitive on coding and still wins on some workflow-specific factors.]]></description>
            <content:encoded><![CDATA[
GPT-5.4 now leads Claude Opus 4.6 on BenchLM's current overall score, 94 to 92. The old storyline where Claude clearly beat GPT-5.4 on the blended leaderboard no longer holds. What remains true is that Claude is still close, still preferable for some workflows, and still one of the strongest flagships in the dataset.

## Headline comparison

| | Claude Opus 4.6 | GPT-5.4 |
|---|---|---|
| Overall score | 92 | **94** |
| Overall rank | #4 | **#3** |
| Coding score | **90.8** | 90.7 |
| Agentic score | 92.6 | **93.5** |
| Knowledge score | 92.4 | **97.6** |
| Math score | 89.4 | **94.5** |
| API price | $15 / $75 | **$2.50 / $15** |

## Where Claude still wins

- **HLE:** 53 vs 48
- **SWE-bench Pro:** 74 vs 57.7
- **Interaction style:** non-reasoning, lower-latency, and often better for drafting and editing

These are not trivial edges. HLE is still one of the better hard-knowledge separators, and SWE-bench Pro remains one of the most meaningful software-engineering benchmarks in the public set.

## Where GPT-5.4 wins now

- **Overall score:** 94 vs 92
- **SWE-bench Verified:** 84 vs 80.8
- **LiveCodeBench:** 84 vs 76
- **Terminal-Bench 2.0:** 75.1 vs 65.4
- **OSWorld-Verified:** 75 vs 72.7
- **SimpleQA:** 97 vs 72
- **MMLU-Pro:** 93 vs 82
- **LongBench v2 / MRCRv2:** 95 / 97 vs 92 / 92

The pattern is straightforward: GPT-5.4 wins more of the broad-purpose benchmark set and does it at a much lower price.

## Coding: effectively a tie, but for different reasons

Claude and GPT-5.4 are now almost dead even on BenchLM's blended coding score, 90.8 to 90.7. That does not mean they are interchangeable.

- Pick **GPT-5.4** if you care most about raw SWE-bench Verified and LiveCodeBench performance.
- Pick **Claude Opus 4.6** if you care more about SWE-bench Pro and the quality of the interaction around the engineering work.

## Verdict

Use **GPT-5.4** if you want the stronger broad default and the better cost profile.

Use **Claude Opus 4.6** if you want a flagship model ]]></content:encoded>
            <author>Glevd</author>
            <category>comparison</category>
            <category>claude</category>
            <category>gpt</category>
            <category>benchmarks</category>
            <category>coding</category>
        </item>
        <item>
            <title><![CDATA[GPQA Diamond: The PhD-Level Science Benchmark]]></title>
            <link>https://benchlm.ai/blog/posts/gpqa-diamond-science-benchmark</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/gpqa-diamond-science-benchmark</guid>
            <pubDate>Sat, 07 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[GPQA tests AI models with graduate-level questions in biology, physics, and chemistry that are 'Google-proof' — even skilled non-experts with internet access can't answer them. Here's how it works.]]></description>
            <content:encoded><![CDATA[
GPQA Diamond is a benchmark of 198 PhD-level science questions in biology, physics, and chemistry. Human domain experts average 81% — top AI models now score 95-97%. It is the standard test for "can this model reason at a graduate science level?" in 2026.

GPQA (Graduate-Level Google-Proof Q&A) is a benchmark of 448 multiple-choice questions written by PhD-level domain experts in biology, physics, and chemistry. The questions are specifically designed so that even skilled non-experts with full internet access struggle to answer them.

If you can Google the answer, it's not a GPQA question.

## What makes GPQA different

Most knowledge benchmarks test recall — can the model regurgitate facts it learned during training? GPQA tests whether a model can apply deep domain expertise to novel questions. The difference is critical for evaluating AI models intended for scientific research, medical applications, or advanced engineering.

Each question was created through a rigorous process:

1. **Domain experts write questions** that require specialized graduate-level knowledge
2. **Other experts validate** that the answer is correct and unambiguous
3. **Non-experts attempt the questions** with full internet access — if non-experts can answer them, the questions are filtered out

This "Google-proof" design means GPQA scores reflect genuine understanding, not just memorization or search ability.

### The Diamond subset

GPQA Diamond is the hardest subset of GPQA, consisting of 198 questions that were specifically selected for maximum difficulty and expert agreement. When researchers reference "GPQA" in model evaluations, they usually mean the Diamond subset. The questions are so hard that domain experts — people with PhDs in the relevant field — only achieve about 81% accuracy. Non-experts with internet access score around 22%, barely above the 25% random baseline for four-choice multiple-choice questions.

This means a model scoring 95% on GPQA Diamond is outperforming the av]]></content:encoded>
            <author>Glevd</author>
            <category>benchmarks</category>
            <category>knowledge</category>
            <category>gpqa</category>
            <category>explainer</category>
        </item>
        <item>
            <title><![CDATA[HLE (Humanity's Last Exam): The Hardest Benchmark]]></title>
            <link>https://benchlm.ai/blog/posts/hle-humanitys-last-exam</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/hle-humanitys-last-exam</guid>
            <pubDate>Sat, 07 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Humanity's Last Exam is crowdsourced from thousands of domain experts and designed to probe the absolute frontier of AI. Top models score only 10-46%. Here's why HLE matters.]]></description>
            <content:encoded><![CDATA[
HLE is the hardest public AI benchmark available. Frontier models score 95-99% on most knowledge tests — on HLE, the best score is 46%. The 11-point gap between first and fifth place reveals performance differences that every other knowledge benchmark masks. If you want to know where frontier AI actually stands, HLE is the only benchmark that still has room to tell you.

Humanity's Last Exam (HLE) is the hardest public AI benchmark available. While frontier models score 95-99% on most knowledge benchmarks, HLE scores range from the single digits to the mid-40s. It's the one benchmark where the gap between models is impossible to ignore.

In a landscape where [MMLU](/benchmarks/mmlu) and even [GPQA](/blog/posts/gpqa-diamond-science-benchmark) are approaching saturation, HLE remains the clearest measure of where frontier AI actually stands — and where it falls short.

## What makes HLE different

HLE was crowdsourced from thousands of domain experts worldwide, organized by the Center for AI Safety and Scale AI. The questions are designed to:

- **Test frontier-level knowledge** — questions that even specialists find difficult
- **Cover cutting-edge domains** — advanced mathematics, theoretical physics, philosophy, and other fields at the edge of human knowledge
- **Resist memorization** — novel, expert-crafted questions not found in training data
- **Scale with AI progress** — the benchmark was designed to remain challenging as models improve

This isn't a test of whether a model can recall facts. It's a test of whether a model can reason at the level of the world's top researchers.

### How questions are sourced

HLE's question creation process is unprecedented in scale. Over 3,000 domain experts from top universities and research institutions contributed questions. Each question goes through multiple validation rounds:

1. **Expert creates a question** in their area of specialization — often at the frontier of their field
2. **Other experts verify** the answer is c]]></content:encoded>
            <author>Glevd</author>
            <category>benchmarks</category>
            <category>knowledge</category>
            <category>hle</category>
            <category>explainer</category>
        </item>
        <item>
            <title><![CDATA[LiveCodeBench: Why Static Coding Benchmarks Aren't Enough]]></title>
            <link>https://benchlm.ai/blog/posts/livecodebench-contamination-free</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/livecodebench-contamination-free</guid>
            <pubDate>Sat, 07 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[LiveCodeBench uses fresh competitive programming problems from LeetCode, Codeforces, and AtCoder to prevent data contamination. Here's why it matters and which models lead.]]></description>
            <content:encoded><![CDATA[
LiveCodeBench is the most contamination-resistant coding benchmark available. By sourcing fresh problems from LeetCode, Codeforces, and AtCoder after each model's training cutoff, it ensures scores reflect actual coding ability — not memorized solutions. Models that look identical on HumanEval spread 10+ points apart on LiveCodeBench.

LiveCodeBench solves one of the biggest problems in AI benchmarking: data contamination. Most coding benchmarks use fixed problem sets that were published years ago. Models trained on internet data may have seen these problems — or their solutions — during training. LiveCodeBench sidesteps this by continuously sourcing fresh problems.

This matters more than most people realize. Data contamination doesn't just inflate scores — it makes entire benchmarks unreliable for model comparison.

## How LiveCodeBench works

LiveCodeBench pulls new competitive programming problems from:

- **LeetCode** — the most popular coding interview platform
- **Codeforces** — competitive programming community with regular contests
- **AtCoder** — Japanese competitive programming platform known for high-quality problems

Problems are sourced after a model's training cutoff date, making it impossible for the model to have memorized solutions. The benchmark evaluates four capabilities:

1. **Code generation** — writing correct solutions from problem descriptions
2. **Self-repair** — fixing code when given error messages
3. **Code execution** — predicting program output without running the code
4. **Test output prediction** — understanding what tests should produce

### The refresh cycle

What makes LiveCodeBench unique is its continuous update process. New problems are added monthly as fresh contests occur on the source platforms. BenchLM.ai uses the most recent available evaluation for each model.

## Why contamination matters

Consider [HumanEval](/blog/posts/what-is-humaneval-coding-benchmark): its 164 problems have been public since 2021. Every major tra]]></content:encoded>
            <author>Glevd</author>
            <category>benchmarks</category>
            <category>coding</category>
            <category>livecodebench</category>
            <category>explainer</category>
        </item>
        <item>
            <title><![CDATA[MMLU vs MMLU-Pro: What Changed and Why It Matters]]></title>
            <link>https://benchlm.ai/blog/posts/mmlu-vs-mmlu-pro</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/mmlu-vs-mmlu-pro</guid>
            <pubDate>Sat, 07 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[MMLU and MMLU-Pro are the most cited knowledge benchmarks in AI. Here's what each measures, why MMLU is saturated, and why MMLU-Pro is the better discriminator in 2026.]]></description>
            <content:encoded><![CDATA[
MMLU is saturated — frontier models score 97-99% and the top 5 models are separated by just 2 points. MMLU-Pro fixes this with 10-choice questions and harder reasoning problems, creating a meaningful 85-91 spread that actually differentiates today's best models.

MMLU (Massive Multitask Language Understanding) has been the go-to knowledge benchmark since 2020. It tests models across 57 academic subjects with multiple-choice questions ranging from elementary to professional difficulty. But with frontier models now scoring 97-99%, it's lost its ability to separate the best from the rest.

MMLU-Pro was designed to fix this.

## How MMLU works

MMLU presents 4-choice multiple-choice questions across subjects like history, biology, computer science, law, and mathematics. A model reads a question and picks A, B, C, or D.

With 4 choices, random guessing gives you 25%. Early models struggled to beat 40-50%. Today's frontier models score 97-99%, meaning the benchmark is effectively saturated.

See current scores: [MMLU leaderboard](/benchmarks/mmlu)

## What MMLU-Pro changes

MMLU-Pro makes three key improvements:

1. **10 answer choices instead of 4** — Random guessing drops from 25% to 10%, reducing the role of luck
2. **More reasoning-focused questions** — Harder questions that require multi-step thinking, not just recall
3. **Better discrimination** — Top model scores range from ~85-91 instead of 97-99, creating meaningful separation

This makes MMLU-Pro a much better benchmark for comparing frontier models. A 5-point gap on MMLU-Pro is more informative than a 1-point gap on MMLU.

See current scores: [MMLU-Pro leaderboard](/benchmarks/mmluPro)

## Current rankings comparison

| Model | MMLU | MMLU-Pro |
|-------|------|----------|
| GPT-5.4 | 99 | 91 |
| Claude Opus 4.6 | 99 | 89 |
| GPT-5.3 Codex | 99 | 90 |
| GPT-5.2 | 98 | 87 |
| Gemini 3.1 Pro | 97 | 87 |

On MMLU, the top 5 models are within 2 points. On MMLU-Pro, the spread widens to 4 points. That's the differe]]></content:encoded>
            <author>Glevd</author>
            <category>benchmarks</category>
            <category>knowledge</category>
            <category>mmlu</category>
            <category>explainer</category>
        </item>
        <item>
            <title><![CDATA[SWE-bench Explained: How We Measure Real-World Coding]]></title>
            <link>https://benchlm.ai/blog/posts/swe-bench-explained</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/swe-bench-explained</guid>
            <pubDate>Sat, 07 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[SWE-bench Verified tests AI models on resolving real GitHub issues from Django, Flask, and scikit-learn. Here's how it works, why it matters, and which models score highest.]]></description>
            <content:encoded><![CDATA[
SWE-bench Verified gives AI models real GitHub bugs to fix. The model must navigate a production codebase, write a patch, and pass the test suite. GPT-5.3 Codex leads at 85; the top general-purpose models (GPT-5.4, Claude Opus 4.6) both score 80-81. It is the most predictive coding benchmark for real-world use in 2026.

SWE-bench Verified is the closest thing we have to a benchmark that measures real software engineering ability. Instead of toy problems, it gives AI models actual GitHub issues from popular open-source repositories and asks them to generate patches that fix the bugs.

## How SWE-bench works

The benchmark pulls real issues from repositories like Django, Flask, scikit-learn, and other production Python codebases. Each task includes:

1. **The issue description** from GitHub
2. **The repository codebase** at the commit before the fix
3. **A test suite** that passes after the correct fix is applied

The model must read the issue, understand the codebase, identify the relevant files, and produce a code patch. That patch is applied and the test suite runs. If the tests pass, it's a success.

SWE-bench Verified is a human-curated subset of 500 tasks from the original SWE-bench dataset, filtering out ambiguous or poorly defined issues.

## Why SWE-bench matters

SWE-bench tests skills that [HumanEval](/blog/posts/what-is-humaneval-coding-benchmark) doesn't touch:

- **Codebase navigation**: Finding the right files in a large repository
- **Bug comprehension**: Understanding what's broken from an issue description
- **Multi-file patches**: Changes that span multiple files and functions
- **Test awareness**: The fix must pass existing tests without breaking anything

This is much closer to what a developer actually does daily. It's why SWE-bench scores have become the primary metric for evaluating AI coding agents like Cursor, Copilot, and Claude Code.

## Current leaderboard

According to BenchLM.ai data, the top models on SWE-bench Verified are:

| Rank | ]]></content:encoded>
            <author>Glevd</author>
            <category>benchmarks</category>
            <category>coding</category>
            <category>swe-bench</category>
            <category>explainer</category>
        </item>
        <item>
            <title><![CDATA[What Is HumanEval? The Coding Benchmark Explained]]></title>
            <link>https://benchlm.ai/blog/posts/what-is-humaneval-coding-benchmark</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/what-is-humaneval-coding-benchmark</guid>
            <pubDate>Sat, 07 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[HumanEval tests whether AI models can generate correct Python functions from docstrings. Here's what it measures, why it's nearly saturated, and which benchmarks matter more in 2026.]]></description>
            <content:encoded><![CDATA[
HumanEval tests Python function generation from docstrings — pass the unit tests, score a point. Frontier models now score 91-95%, making it effectively saturated. It works as a minimum baseline check in 2026, but SWE-bench Verified and LiveCodeBench are the benchmarks that actually separate good coding models from great ones.

HumanEval is a benchmark of 164 hand-written Python programming problems. Each problem gives the model a function signature and docstring, and the model must generate a working function body. The generated code is tested against unit tests to check if it actually works.

It was created by OpenAI in 2021 and quickly became the standard way to measure whether an AI model can write code.

## How HumanEval works

Each problem in HumanEval includes:

1. A function signature with type hints
2. A docstring describing what the function should do
3. Example inputs and outputs
4. Hidden unit tests that verify correctness

The model generates code, and that code gets executed. If it passes all the unit tests, it's counted as correct. The final score is the percentage of problems solved (pass@1 means one attempt per problem).

This is important: HumanEval measures **functional correctness**, not whether the code looks right. A syntactically perfect solution that returns wrong answers scores zero. An ugly solution that passes all tests scores 100%.

## Why HumanEval is nearly saturated in 2026

Look at the scores on our [HumanEval leaderboard](/benchmarks/humaneval):

- Six frontier models score 91+
- Two specialized coding models score 94-95
- The gap between 1st and 10th place is only 7 points

When most top models score above 90%, the benchmark stops being useful for distinguishing between them. A model scoring 93 vs 91 on HumanEval doesn't tell you much about which one will be better at your actual coding tasks.

The problems in HumanEval are mostly introductory to intermediate difficulty — string manipulation, basic algorithms, and simple data struc]]></content:encoded>
            <author>Glevd</author>
            <category>benchmarks</category>
            <category>coding</category>
            <category>humaneval</category>
            <category>explainer</category>
        </item>
        <item>
            <title><![CDATA[Building Your Own LLM Benchmark: A Practical Guide]]></title>
            <link>https://benchlm.ai/blog/posts/building-custom-llm-benchmark</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/building-custom-llm-benchmark</guid>
            <pubDate>Fri, 22 Aug 2025 00:00:00 GMT</pubDate>
            <description><![CDATA[How to create a custom LLM benchmark for your specific use case — from defining tasks and building datasets to scoring models and avoiding common pitfalls.]]></description>
            <content:encoded><![CDATA[
Build a custom LLM benchmark when public ones don't cover your specific tasks. Start with 100-200 representative test cases, define a clear automated scoring method, prevent data contamination by using tasks from your own systems, and validate results with statistical confidence. Custom benchmarks give you ground truth for your actual use case that public benchmarks can't provide.

Public benchmarks like [SWE-bench](/benchmarks/sweVerified) and [MMLU](/benchmarks/mmlu) measure general capabilities. They're excellent for comparing models across a broad range of tasks. But if you need to know which model performs best on *your* specific tasks — your domain, your data, your quality standards — you need a custom benchmark.

This guide covers the practical steps: defining your evaluation goals, building a test dataset, setting up scoring, and avoiding the pitfalls that make custom benchmarks misleading.

## When to build a custom benchmark

Build a custom benchmark when:

- **Your domain has specialized vocabulary or context** that general benchmarks don't cover (medical, legal, finance, manufacturing)
- **Your quality criteria are specific** (output must follow a particular format, use specific terminology, match a style guide)
- **Public benchmarks are saturated** for the capability you care about and you need finer discrimination
- **Your task type isn't well-represented** in public benchmarks (specialized agentic workflows, proprietary API integration, etc.)

Don't build a custom benchmark if a public benchmark already covers your use case well — public benchmarks have thousands of test cases and years of validation work behind them.

→ [Check if your use case is covered by existing BenchLM.ai benchmarks](/)

## Step 1: Define your evaluation goals

Before writing a single test case, answer these questions:

**What capability are you testing?** Be specific. "Can the model write SQL queries?" is better than "can the model do data work?"

**What does success look like]]></content:encoded>
            <author>Glevd</author>
            <category>llm</category>
            <category>benchmarking</category>
            <category>development</category>
            <category>implementation</category>
            <category>custom-evaluation</category>
        </item>
        <item>
            <title><![CDATA[The Complete Guide to LLM Benchmarking: Everything You Need to Know]]></title>
            <link>https://benchlm.ai/blog/posts/complete-guide-llm-benchmarking</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/complete-guide-llm-benchmarking</guid>
            <pubDate>Fri, 22 Aug 2025 00:00:00 GMT</pubDate>
            <description><![CDATA[Everything you need to know about LLM benchmarking — what benchmarks measure, how to choose the right ones, common pitfalls, and how to interpret results for real-world model selection.]]></description>
            <content:encoded><![CDATA[
LLM benchmarks are standardized tests measuring model performance on coding, math, knowledge, and reasoning. The most important in 2026: SWE-bench Verified (real-world coding), HLE (frontier knowledge), LiveCodeBench (contamination-free coding), GPQA (PhD-level science). Use multiple benchmarks across your target categories — no single test predicts performance across all tasks.

LLM benchmarking has become the primary way to compare hundreds of AI models without running your own evaluations. But picking the right benchmarks, interpreting results correctly, and avoiding common pitfalls requires understanding how the system works.

This guide covers everything: what benchmarks actually measure, which ones matter in 2026, how to read scores, and how to avoid being misled by inflated or irrelevant numbers.

## What LLM benchmarks actually measure

A benchmark is a standardized test with a fixed set of problems and a scoring method. The model answers each question, the answers are evaluated (automatically or by humans), and the result is a score.

Different benchmarks measure different capabilities:

- **Knowledge**: Factual accuracy across academic subjects (MMLU, GPQA, HLE)
- **Coding**: Writing, debugging, and navigating code (HumanEval, SWE-bench, LiveCodeBench)
- **Math**: Solving mathematical problems (AIME, HMMT, MATH-500)
- **Reasoning**: Following multi-step logic (BBH, MuSR, SimpleQA)
- **Instruction following**: Precise compliance with instructions (IFEval)
- **Agentic**: Completing multi-step tasks autonomously (Terminal-Bench 2.0, BrowseComp, OSWorld-Verified)

No single benchmark covers everything. A model can be excellent at math and mediocre at coding. Benchmark scores are only meaningful when matched to your specific use case.

→ [See how BenchLM.ai weights benchmarks across 8 categories](/)

## The benchmark categories that matter in 2026

### Coding benchmarks

**[SWE-bench Verified](/benchmarks/sweVerified)** — 500 real GitHub issues from production]]></content:encoded>
            <author>Glevd</author>
            <category>llm</category>
            <category>benchmarking</category>
            <category>ai-evaluation</category>
            <category>machine-learning</category>
            <category>guide</category>
        </item>
        <item>
            <title><![CDATA[How to Interpret LLM Benchmark Results: A Practical Guide]]></title>
            <link>https://benchlm.ai/blog/posts/interpreting-llm-benchmark-results</link>
            <guid isPermaLink="false">https://benchlm.ai/blog/posts/interpreting-llm-benchmark-results</guid>
            <pubDate>Fri, 22 Aug 2025 00:00:00 GMT</pubDate>
            <description><![CDATA[How to read LLM benchmark scores correctly — what differences are meaningful, what to ignore, common misinterpretations, and how to translate benchmark data into model selection decisions.]]></description>
            <content:encoded><![CDATA[
A 1-2 point benchmark difference is usually noise — not a meaningful signal. Focus on gaps of 5+ points, use non-saturated benchmarks for frontier model comparison, and never compare scores across different benchmarks. HLE and SWE-bench tell you more about today's frontier models than MMLU or HumanEval.

LLM benchmarks are widely used but frequently misread. A model scoring 92 vs 90 on MMLU-Pro is not meaningfully better. A model scoring 85 vs 75 on SWE-bench probably is. Understanding which differences matter requires knowing how benchmarks work, what their limitations are, and what counts as signal vs noise.

This guide covers the key principles for reading benchmark results correctly.

## The basics: what benchmark scores represent

A benchmark score is the percentage of test cases answered correctly (or the average score across test cases). Higher is better within the same benchmark.

**What scores are not:**
- Comparable across different benchmarks
- Guaranteed to predict real-world performance
- Reliable at the 1-2 point level
- Meaningful on saturated benchmarks where top models cluster at 97-99%

**What scores are:**
- Useful for comparing models on the same benchmark
- Reliable at 5+ point differences (with sufficient sample size)
- Good proxies for capability in the category being tested
- Most useful when combined across multiple relevant benchmarks

## How much difference is meaningful?

The answer depends on the benchmark's sample size and the difficulty distribution of its questions.

**Rule of thumb:**
- 1-2 point difference: ignore, likely noise
- 3-4 points: possibly meaningful, check if statistically significant
- 5+ points: probably real, worth investigating further
- 10+ points: almost certainly a meaningful capability difference

On benchmarks with fewer test cases (like the 198-question GPQA Diamond), statistical uncertainty is higher than on benchmarks with 1,000+ questions. BenchLM.ai shows sample sizes for all benchmarks to help you assess ]]></content:encoded>
            <author>Glevd</author>
            <category>llm</category>
            <category>benchmarking</category>
            <category>performance-metrics</category>
            <category>data-analysis</category>
            <category>ai-evaluation</category>
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