Which AI model is best for coding in 2026? We rank major LLMs by BenchLM's verified coding score — SWE-Rebench, SWE-bench Pro, LiveCodeBench, and SWE-bench Verified — with pricing and task-specific picks.
Share This Report
Copy the link, post it, or save a PDF version.
As of June 2026, the best verified coding model on BenchLM is Claude Opus 4.8 (76.4). The bigger story: open-weight models have nearly closed the coding gap. DeepSeek V4 Pro (Max) sits within half a point of the leader, and most of the verified top ten is now open weight.
BenchLM's coding score weights SWE-Rebench, SWE-bench Pro, LiveCodeBench, and SWE-bench Verified, prioritizing fresh repository-style engineering signals over saturated legacy benchmarks. The table below is generated from the live leaderboard at build time, so it always matches the coding leaderboard.
One newer display benchmark worth watching is React Native Evals. 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 alongside the main coding leaderboard.
| Rank | Model | Type | License | Score |
|---|---|---|---|---|
| 1 | Claude Opus 4.8 | Reasoning | Proprietary | 76.4 |
| 2 | DeepSeek V4 Pro (Max) | Reasoning | Open Weight | 75.9 |
| 3 | Nemotron 3 Ultra | Reasoning | Open Weight | 74.2 |
| 4 | DeepSeek V4 Pro (High) | Reasoning | Open Weight | 73.8 |
| 5 | DeepSeek V4 Flash (Max) | Reasoning | Open Weight | 73.7 |
| 6 | Qwen3.7 Max | Reasoning | Proprietary | 73.6 |
| 7 | Claude Opus 4.7 (Adaptive) | Reasoning | Proprietary | 72.9 |
| 8 | DeepSeek V4 Flash (High) | Reasoning | Open Weight | 72.2 |
| 9 | Kimi K2.6 | Reasoning | Open Weight | 72 |
| 10 | Qwen3.7 Plus | Reasoning | Proprietary | 71.1 |
| 11 | MAI-Thinking-1 | Reasoning | Proprietary | 71 |
| 12 | GLM-4.7 | Reasoning | Open Weight | 70.6 |
Verified scores from the BenchLM.ai coding leaderboard, regenerated on every site build. Newly released models with sparse early results (e.g. Claude Mythos 5 and Claude Fable 5) rank provisionally much higher but are excluded here until enough verified benchmarks land.
A year ago the conversation was "how far behind is open source?" The answer now is: barely. DeepSeek V4 Pro (Max) at 75.9 trails Claude Opus 4.8 by half a point on the verified coding score, and Nemotron 3 Ultra, DeepSeek V4 Flash, and Kimi K2.6 all sit above or near the strongest GPT-5.x verified coding rows.
The economics follow. Claude Opus 4.8 runs $5/$25 per million tokens. DeepSeek V4 Pro runs $1.74/$3.48 via API — roughly 3x cheaper on input and 7x cheaper on output — and you can self-host it. For agent loops that burn hundreds of millions of tokens, that difference decides the architecture.
Look at the HumanEval column on any leaderboard. Six frontier models score 91+. Several 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 GitHub repos. LiveCodeBench pulls fresh competitive programming problems so models can't memorize them.
If someone quotes you a HumanEval score in 2026, ask them about SWE-bench instead.
Every model in the verified top ten is a reasoning model. That's new — through early 2026, non-reasoning rows like Claude Opus 4.6 and Gemini 3.1 Pro were competitive at the top.
The trade-off is latency. Reasoning models think before they respond, which can add seconds to minutes of first-answer latency. For autocomplete and interactive assistants, a fast non-reasoning model or a light reasoning tier is still the right call; save the heavy reasoning rows for multi-file bug fixes and agent sessions where quality dominates.
Short completions (under 50 tokens) don't require SWE-bench-level capability. The latency and cost profile matter more than marginal benchmark differences.
Best options: Gemini 3.1 Pro ($2/$12) for cost-sensitive high-volume use, or DeepSeek V4 Flash ($0.14/$0.28) where every millisecond and cent counts.
This is exactly what SWE-bench measures, and where the verified leaders earn their rank.
Best option: Claude Opus 4.8 if budget allows; DeepSeek V4 Pro for near-identical quality at a fraction of the cost.
Agentic coding burns tokens fast, so the cost column matters as much as the score column. Claude Opus 4.8 at $5/$25 adds up quickly in agent loops making hundreds of calls.
Best option: DeepSeek V4 Pro ($1.74/$3.48) or Kimi K2.6 for sustainable agent economics; Claude Opus 4.8 or Claude Sonnet 4.6 for teams committed to Anthropic's tooling stack.
LiveCodeBench pulls fresh competitive programming problems continuously, so it stays contamination-resistant. The verified leaders above are also the LiveCodeBench leaders — check the LiveCodeBench benchmark page for current per-model scores.
No dedicated SQL benchmark exists at frontier level yet. Based on structured output and reasoning scores, the top verified coding rows all handle complex SQL reliably. For batch data pipelines, Gemini 3.1 Pro ($2/$12) and DeepSeek V4 Pro ($1.74/$3.48) are the cost-effective choices.
Test generation is underrepresented in benchmarks. Strong SWE-bench performance correlates with good test generation since fixing bugs often requires writing regression tests. Any of the verified top five is reliable here.
| Rank | Model | Type | License | Score |
|---|---|---|---|---|
| 1 | DeepSeek V4 Pro (Max) | Reasoning | Open Weight | 75.9 |
| 2 | Nemotron 3 Ultra | Reasoning | Open Weight | 74.2 |
| 3 | DeepSeek V4 Pro (High) | Reasoning | Open Weight | 73.8 |
| 4 | DeepSeek V4 Flash (Max) | Reasoning | Open Weight | 73.7 |
| 5 | DeepSeek V4 Flash (High) | Reasoning | Open Weight | 72.2 |
| 6 | Kimi K2.6 | Reasoning | Open Weight | 72 |
If you need to self-host or fine-tune, DeepSeek V4 Pro (Max) leads the open-weight rows, with Nemotron 3 Ultra and the DeepSeek V4 Flash family close behind. Kimi K2.6 — the successor to K2.5 — rounds out the practical short list, and GLM-4.7 remains a balanced option across coding, agentic, and math.
These aren't budget compromises anymore: the open-weight leaders are within a point or two of the best proprietary rows. The real decision is operational — self-hosting a 100B+ parameter model takes serious GPU capacity, and for most teams the hosted APIs (DeepSeek at $1.74/$3.48, MiniMax M3 at $0.30/$1.20) are the practical path.
Need the best possible coding model: Claude Opus 4.8. It currently leads the verified coding score, but the gap to DeepSeek V4 Pro is half a point.
Running an AI coding agent at scale: DeepSeek V4 Pro. Near-frontier quality at $1.74/$3.48 makes the agent loop math work.
Claude ecosystem: Claude Opus 4.8 for quality, Claude Sonnet 4.6 ($3/$15) for volume work.
Budget-first coding: DeepSeek V4 Flash ($0.14/$0.28), MiniMax M3 ($0.30/$1.20), or GLM-5.1 ($1.40/$4.40) depending on whether you care more about price, open weights, or context window.
→ See the full coding leaderboard · Compare SWE-bench scores · LiveCodeBench details · React Native Evals explainer
What is the best LLM for coding in 2026? As of June 2026, Claude Opus 4.8 (76.4) leads BenchLM's verified coding score, with DeepSeek V4 Pro (Max) and Nemotron 3 Ultra right behind.
How does Claude compare to GPT for coding? Claude Opus 4.8 currently tops the verified coding leaderboard, while the strongest GPT-5.x verified coding rows sit several points back. The gap is small enough that pricing and ecosystem usually decide it.
Is SWE-bench a good benchmark for coding AI? Yes — it's the most reliable coding signal available. It tests real bug-fixing on actual GitHub repositories, not toy functions. HumanEval is saturated and no longer differentiates frontier models.
What's the best coding model for an AI agent? DeepSeek V4 Pro for cost-sustainable agent loops, Claude Opus 4.8 for maximum quality, Kimi K2.6 if you want strong open-weight agent performance.
What's the best open-weight coding model? Currently DeepSeek V4 Pro (Max) (75.9) on BenchLM's verified coding score — it leads all open-weight rows and sits within half a point of the overall leader.
Benchmark scores from BenchLM.ai, regenerated from the live leaderboard on every build. Prices per million tokens, current as of June 2026.
Coding benchmarks shift with every model release. We send one email a week with what moved and why.
Share This Report
Copy the link, post it, or save a PDF version.
On this page
Which models moved up, what’s new, and what it costs. One email a week, 3-min read.
Free. One email per week.
Which LLM is best for voice agents in 2026? We rank models by first-answer latency and output speed — the metrics that actually decide voice — name the fastest capable models, and compare the voice-agent platforms (Retell, Vapi, OpenAI Realtime, ElevenLabs).
Best LLM for math 2026: GPT-5.4 leads AIME 2025, MATH-500, and BRUMO. Compare Claude, Gemini, DeepSeek-R1, GPT-5.5, and value picks by use case.
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.