Rankings of the best AI models for reasoning tasks. Compare models by logic, planning, and problem-solving capabilities.
The best AI for reasoning right now is Claude Mythos Preview by Anthropic, followed by Claude Fable 5 — ranked by GPQA Diamond, ARC-C, and multi-step logic benchmarks.
| Model | Best for | Top strength | Watch out | Cost · Context |
|---|---|---|---|---|
| Claude Mythos Preview Anthropic | Anthropic preview model — early-access benchmark only | Strong early signal on research + retrieval tasks | Preview-only; pricing and availability subject to change | — |
| Claude Opus 4.8 Anthropic | Frontier on extended-thinking reasoning tasks | Long-form coherence — voice and structure stay consistent over thousands of tokens | The highest output price of any frontier model — not the default for cost-sensitive workflows | $5.00 / $25.00 1.0M ctx |
| GPT-5.5 OpenAI | Top scores on the hardest reasoning suites | Frontier scores across reasoning, math, coding, and research | Premium pricing — match the variant (Pro / Instant) to the task | $5.00 / $30.00 1.1M ctx |
| GPT-5.5 Pro OpenAI | Top scores on the hardest reasoning suites | Frontier scores across reasoning, math, coding, and research | Premium pricing — match the variant (Pro / Instant) to the task | — |
| Qwen3.7 Max Alibaba Cloud / Qwen Team | Alibaba's newest — strongest open-weight Asian frontier | Excellent multilingual coverage (50+ languages) | Western provider coverage lags | $1.25 / $3.75 1.0M ctx |
| Claude Opus 4.6 Anthropic | Frontier on extended-thinking reasoning tasks | Long-form coherence — voice and structure stay consistent over thousands of tokens | The highest output price of any frontier model — not the default for cost-sensitive workflows | $5.00 / $25.00 1.0M ctx |
| Gemini 3.1 Pro Google | Google's most capable widely-available model | Best-in-class multimodal reasoning (images, charts, video) | Pro variant pricing approaches Opus territory | $2.50 / $15.00 1.0M ctx |
Anthropic preview model — early-access benchmark only
Frontier on extended-thinking reasoning tasks
Top scores on the hardest reasoning suites
Top scores on the hardest reasoning suites
Alibaba's newest — strongest open-weight Asian frontier
Frontier on extended-thinking reasoning tasks
Google's most capable widely-available model
Anthropic preview model — early-access benchmark only
When to useEvaluation and benchmark comparison only — not for production.
Frontier on extended-thinking reasoning tasks
When to useWhen output quality matters more than cost or latency.
Top scores on the hardest reasoning suites
When to useWhen you want the single highest-scoring model and budget isn't the constraint.
Top scores on the hardest reasoning suites
When to useWhen you want the single highest-scoring model and budget isn't the constraint.
Alibaba's newest — strongest open-weight Asian frontier
When to useMultilingual workloads; open-weight evaluations.
Frontier on extended-thinking reasoning tasks
When to useWhen output quality matters more than cost or latency.
As of June 2026, Claude Mythos Preview leads reasoning benchmarks with a score of 71.2, followed by Claude Fable 5 (70.5) and Claude Opus 4.8 (64.3). These rankings test logical deduction and multi-step inference — tasks where the model must construct novel conclusions, not recall memorized facts.
Ranked by 351 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.'
