Best Reasoning AI Models in 2026
Claude Fable 5.1 leads reasoning ai models on the September 2026 ranking with a score of 82.6, ahead of Claude Fable 5 (82.2) and Claude Opus 5 (82.1). Each row uses the public BenchAlign v5 contract and shows its evidence status.
Bottom line: Reasoning models (chain-of-thought) dominate the top of the leaderboard. Claude Fable 5 leads, but they cost more and are slower. Choose reasoning when accuracy matters more than speed.
About this ranking
Last verified: September 3, 2026
Top AI models with dedicated reasoning capabilities, ranked by benchmark performance.
Unless noted otherwise, ranking surfaces on this page use BenchLM’s provisional leaderboard lane rather than the stricter sourced-only verified leaderboard.
Claude Fable 5.1 leads this ranking with a score of 82.6, followed by Claude Fable 5 (82.2) and Claude Opus 5 (82.1). The top three are separated by just a few points — any of them would perform well for this use case.
The best open-weight option is Hy4 preview (ranked #7 with a score of 78.5). While proprietary models lead, open-weight options are within striking distance for teams willing to trade a few points of performance for full model control.
This ranking uses provisional overall weighted scores from the active scoring formula. For detailed model profiles, click any model name below. To compare two specific models head-to-head, use the "vs #" links.
What changed
Claude Fable 5 leads all reasoning models with the highest overall score.
GPT-5.4 best OpenAI reasoning model — leads knowledge at 98.
GPT-5.4 Pro premium tier with perfect multimodal and math scores.
How to choose
Full Rankings (141 models)
Key Takeaways
The top model is Claude Fable 5.1 by Anthropic with a BenchAlign v5 score of 82.6 and Estimated evidence.
The best open-weight model is Hy4 preview at position #7.
141 models are included in this ranking.
Score in Context
What these scores mean
Reasoning models use chain-of-thought to improve accuracy on complex tasks. They are ranked by the same overall BenchLM score. Reasoning models typically outperform standard models by 10-20 points on math and logic.
Known limitations
Reasoning models are slower and more expensive per token due to longer output chains. The speed/cost trade-off is not reflected in benchmark scores. For latency-sensitive applications, compare with non-reasoning models.
Explore More
Know when it’s worth switching models
The model to choose, the cheaper alternative, and the release we would wait on.
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