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LLM Leaderboard & AI Model Benchmarks — October 2026

As of October 28, 2026, the BenchLM AI leaderboard ranks 215 models. Gemini 4 Carbon is first by point estimate. Conditional ranges do not establish rank confidence. Open the evidence before choosing.

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Current BenchAlign leaders

Snapshot October 28, 2026

Method
  1. Google logo

    Google01

    Score88.42Conditional range 74.1–100.0
    Agentic
    Not measured
    Coding
    82
    Reasoning
    Not measured
    Multimodal
    Not measured
    Knowledge
    88
    Multilingual
    Not measured
    Instruction following
    Not measured
    Math
    Not measured

    Estimatedwider uncertainty

    Details for Gemini 4 Carbon
  2. Anthropic logo

    Anthropic02

    Score86.3190% interval 81.7–90.9
    Agentic
    90
    Coding
    83
    Reasoning
    86
    Multimodal
    92
    Knowledge
    89
    Multilingual
    Not measured
    Instruction following
    Not measured
    Math
    Not measured

    Supportedenough independent evidence

    Details for Claude Opus 5.5
  3. OpenAI logo
    GPT-6 AstraSupported

    OpenAI03

    Score86.0990% interval 80.6–91.6
    Agentic
    76
    Coding
    76
    Reasoning
    93
    Multimodal
    Not measured
    Knowledge
    86
    Multilingual
    Not measured
    Instruction following
    Not measured
    Math
    Not measured

    Supportedenough independent evidence

    Details for GPT-6 Astra

Price context: GPT-6.1 Terra retains 91% of the top score with an output price 50% lower.

Data refreshed
October 28, 2026
Supported
77 models
Ranking method
BenchAlign v5.8

The LLM leaderboard — October 2026 rankings

Score, model size, context, price, and benchmark detail in one table. Filter state syncs to the URL so every view is shareable. Supported and Estimated labels show how much independent evidence backs each position.

Supported means we have enough independent evidence to stand behind the row. Estimated means the rank is visible and the uncertainty is wider.

Supportedenough independent evidenceEstimatedwider uncertainty

Parameters show total billions; active counts appear when a source reports them. Size filters use total parameters: Tiny ≤4B, Small >4–<40B, Medium 40–150B, and Large >150B.

AG Agentic CO Coding RE Reasoning MM Multimodal & Grounded KN Knowledge ML Multilingual IF Instruction Following MA Math

Showing 25 of 1002 models · 215 ranked

Source-linked · the table lists all 1002 tracked models; 215 carry a BenchAlign rank

How the score is built

What the table covers

The BenchLM LLM leaderboard 2026 ranks 215+ models and tracks 1002+ large language models side by side across 717 benchmarks — from SWE-bench and LiveCodeBench for coding to GPQA Diamond and MMLU-Pro for knowledge and reasoning. Whether you need the best AI models 2026 has to offer for agentic workflows, math, multilingual tasks, or instruction following, our AI benchmark comparison tables make it easy to see how GPT-5, Claude, Gemini, DeepSeek, Llama, and dozens of other frontier and open-source models stack up on both benchmarks and operator tradeoffs like price and context. Supported and Estimated labels separate evidence strength from score, so incomplete reporting does not automatically remove a model from the comparison.

What changed in October 2026

  1. Gemini 4 Carbon (Google) holds the #1 spot on the BenchAlign leaderboard at 88.42.
  2. 83 model releases tracked in October 2026.
  3. OpenAI climbed 1 spot in the provider race.
  4. 77 Supported and 138 Estimated models appear in the public leaderboard.

Dataset journalData verified

Build a head-to-head comparison

Choose any two ranked models. The comparison opens with the decision, exact units, and evidence coverage.

Decision-ready picks

Current models that clear the ranking, freshness, and evidence thresholds for each decision.

DecisionModelCurrent measureProvenance
  • Best open weightHighest-scoring evidence-qualified current open-weight model.Ling 3.1 FlashInclusionAI65.47overall scoreEstimated · 11 source families
  • Best near-frontier value91% of the leading score at a lower output price.GPT-6.1 TerraOpenAI$5output / 1M tokensEstimated · 1 source family
  • Fastest measuredFastest externally measured model that also clears the ranking and evidence thresholds.Gemini 3.5 Flash-LiteGoogle461tokens / secArtificial Analysis · updated 2026-10-28
  • Largest useful contextLargest context among evidence-qualified current models retaining at least half of the leading score.GPT-6 AstraOpenAI1.05Mcontext windowSupported · 3 source families

Current models that clear rank, freshness and evidence thresholds

Qualification method

How we work

A ranking is a shortlist.

Rank orders the evidence; it does not make the decision. Every row here opens to its sources so you can disagree with us.

We do not invent scores to fill a table.

A model without a public, independent row stays unranked, and says so. Vendor-run numbers are shown, labeled, and kept out of the rank.

When we were wrong, the fix is on the page.

Scoring bugs, missing coverage, and re-weightings are dated and linked, because a ranking you cannot audit is a launch post with a table.

The AI race, read as a record instead of a spectacle

Track which release led its month, how provider depth changed, and whether the crown moved.

Months
6
Releases
297
Lead changes
5

Best ranked model released in Oct 2026

Gemini 4 CarbonGoogle
Score88.42
RankProviderTop-three avg.Models
01OpenAI83.937
02Anthropic83.122
03Google79.120

Six-month release record

Explore the release timeline

Scoring methodology

Scoring methodology8 weighted categories, 60 ranking benchmarks, and 373 display-only records.Inspect method
Category weights
100%
Scored categories
8
Ranking benchmarks
60
Display-only
373

Each model's overall score is a normalized weighted average of category averages. Within each category, benchmark results are normalized to a common scale and combined using weights that favor harder, less-saturated evaluations.

Confidence remains separate from score. It shows how much public benchmark evidence supports a result, while display-only benchmarks stay visible for context without receiving ranking weight.

Benchmark scores come from official provider tables, model cards, release announcements, and benchmark-native public leaderboards. External consensus signals are bounded calibration inputs and are not exposed in exported data.

Read the complete methodology
Category and weightBenchmarks that affect the scoreCoverage
  • Agentic22%

    Terminal-Bench 3.0 · Terminal-Bench 4.0 · AA AutomationBench · AA EnterpriseOps-Gym · AA Harvey LAB v1.0 · AA Tau3 Banking · Terminal-Bench 2.0 · Terminal-Bench 2.1 · BrowseComp · HLE w/ tools · GDPval-AA · OSWorld-Verified · JobBench · MCP Atlas · Toolathlon · Toolathlon-Verified · AutomationBench · Agents' Last Exam · DeepSearchQA · BFCL v4 · τ³-bench results · Terminal-Bench 2.1 (Vals)

    22 weighted · 103 context-only

  • Coding20%

    SWE-Rebench · SWE-bench Pro · VulcanBench v3 · FrontierCode 1.1 Main · SWE Multilingual · FrontierSWE v2 · SciCode · AA-SciCode · LiveCodeBench (Vals) · DeepSWE

    10 weighted · 57 context-only

  • Reasoning17%

    LongBench v2 · MRCRv2 · ARC-AGI-2 · ARC-AGI-3 · AA-LCR · CritPt

    6 weighted · 31 context-only

  • Multimodal12%

    MMMU-Pro · AA-MMMU-Pro · OfficeQA Pro · CharXiv

    4 weighted · 71 context-only

  • Knowledge12%

    GPQA · SuperGPQA · MMLU-Pro · HLE · AA-GPQA Diamond · AA-HLE · AA-Omniscience Accuracy · HLE w/o tools · GPQA Diamond (Vals) · MMLU-Pro (Vals)

    10 weighted · 54 context-only

  • Multilingual7%

    MMLU-ProX

    1 weighted · 13 context-only

  • Instruction Following5%

    IFBench · AA-IFBench

    2 weighted · 15 context-only

  • Math5%

    AIME26 · HMMT Feb 2026 · FrontierMath v2 (Tiers 1-3) · FrontierMath v2 (Tier 4) · USAMO 2026

    5 weighted · 29 context-only

Scoring methodology · BenchAlign v5.8

Inspect method