Best AI for Reasoning
Rankings of the best AI models for reasoning tasks. Compare models by logic, planning, and problem-solving capabilities.
About this ranking
As of April 2026, Claude Opus 4.6 leads reasoning benchmarks with a score of 8018, followed by GLM-5.1 (5634) and Gemini 3 Pro (5478). These rankings test logical deduction and multi-step inference — tasks where the model must construct novel conclusions, not recall memorized facts.
Ranked by 318 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.'