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Best AI for Reasoning in 2026

Rankings of the best AI models for reasoning tasks. Compare models by logic, planning, and problem-solving capabilities.

276 models429 benchmarks
Updated 276 models reviewedMethodology

The short answer

The best AI for reasoning right now is GPT-5.6 Sol by OpenAI, followed by Claude Opus 5 — ranked by GPQA Diamond, ARC-C, and multi-step logic benchmarks.

Best Overall
GPT-5.6 SolHighest combined arena + benchmark score
Best Value
Muse Spark 1.1Lowest input price among the top-ranked models
Longest Context
GPT-5.6 SolLargest context window among the top-ranked models

At a glance

  • Claude Opus 5$5.00 / $25.00

    Frontier on extended-thinking reasoning tasks

    Strength
    Long-form coherence — voice and structure stay consistent over thousands of tokens
    Watch out
    The highest output price of any frontier model — not the default for cost-sensitive workflows
  • Claude Mythos Preview—

    Anthropic preview model — early-access benchmark only

    Strength
    Strong early signal on research + retrieval tasks
    Watch out
    Preview-only; pricing and availability subject to change
  • Claude Opus 4.8$5.00 / $25.00

    Frontier on extended-thinking reasoning tasks

    Strength
    Long-form coherence — voice and structure stay consistent over thousands of tokens
    Watch out
    The highest output price of any frontier model — not the default for cost-sensitive workflows

Capsule reviews of the top models

  1. 01
    Anthropic

    Frontier on extended-thinking reasoning tasks

    Strengths
    • Long-form coherence — voice and structure stay consistent over thousands of tokens
    • Strong instruction following on tone, length, and format
    • Reliable on multi-step tasks where errors compound (agents, refactors, synthesis)
    Watch-outs
    • The highest output price of any frontier model — not the default for cost-sensitive workflows
    • Slower than mini/flash siblings; prefer Sonnet for interactive UX

    When to useWhen output quality matters more than cost or latency.

    Input
    $5.00/ M tokens
    Output
    $25.00/ M tokens
    Context
    1.0Mtokens
    License
    proprietary
    See model pageCompare side-by-side
  2. 02
    Anthropic

    Anthropic preview model — early-access benchmark only

    Strengths
    • Strong early signal on research + retrieval tasks
    • Tests new Anthropic capabilities before GA
    Watch-outs
    • Preview-only; pricing and availability subject to change
    • Not yet wired into most production providers

    When to useEvaluation and benchmark comparison only — not for production.

    See model pageCompare side-by-side
  3. 03
    Anthropic

    Frontier on extended-thinking reasoning tasks

    Strengths
    • Long-form coherence — voice and structure stay consistent over thousands of tokens
    • Strong instruction following on tone, length, and format
    • Reliable on multi-step tasks where errors compound (agents, refactors, synthesis)
    Watch-outs
    • The highest output price of any frontier model — not the default for cost-sensitive workflows
    • Slower than mini/flash siblings; prefer Sonnet for interactive UX

    When to useWhen output quality matters more than cost or latency.

    Input
    $5.00/ M tokens
    Output
    $25.00/ M tokens
    Context
    1.0Mtokens
    License
    proprietary
    See model pageCompare side-by-side

As of July 2026, GPT-5.6 Sol leads reasoning benchmarks with a score of 58.1, followed by Claude Opus 5 (57.7) and Claude Mythos Preview (56.6). These rankings test logical deduction and multi-step inference — tasks where the model must construct novel conclusions, not recall memorized facts.

Ranked by 429 benchmarks including GPQA Diamond (graduate-level reasoning), ARC-Challenge, and BBH (multi-step reasoning), sourced from official evaluations and independent reproductions.

  • Models with extended thinking capabilities (o-series, thinking models) consistently top reasoning benchmarks because they can allocate more compute per problem. Check the leaderboard above for current rankings — the top 3 positions shift with each major release.

  • Yes, but with limits. Top models handle multi-step deduction, constraint satisfaction, and causal reasoning well. They struggle with spatial reasoning, novel logical puzzles they haven't seen in training, and problems where surface-level patterns mislead. Reasoning scores are generally lower than knowledge-recall scores.

  • Knowledge is stored information (facts, dates, definitions). Reasoning is the ability to draw new conclusions from given premises. A model might know many physics facts but fail to solve a novel physics problem. The best models on this leaderboard excel at both, but this ranking specifically tests inference ability.

  • Yes. Extended thinking models cost 2-5x more per query because they generate internal reasoning chains before the final answer. The tradeoff is typically 10-30% higher accuracy on hard problems. For simpler tasks (classification, extraction, basic QA), standard models reason well enough at lower cost.

  • Chain-of-thought is when a model works through a problem step by step before giving a final answer, similar to showing work in math. Models that use chain-of-thought score significantly higher on reasoning benchmarks. Some models do this internally (extended thinking), others can be prompted to 'think step by step.'

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