BenchLM recommendation
Best Reasoning AI Models in 2026
Claude Mythos 5 leads reasoning ai models on BenchLM's July 2026 rankings with a score of 83.9, ahead of Claude Fable 5 (83.7) and GPT-5.6 Sol (82). Each row uses the public BenchAlign v5 contract and shows its evidence status.
Last verified: July 20, 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.
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.
Claude Mythos 5 leads this ranking with a score of 83.9, followed by Claude Fable 5 (83.7) and GPT-5.6 Sol (82). 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 GLM-5.1 (ranked #15 with a score of 67.7). Proprietary models hold a clear advantage in this category, though open-weight options may suffice for less demanding use cases.
This ranking is based on provisional overall weighted scores across BenchLM.ai's scoring formula tracked by BenchLM.ai. 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 (108 models)
Key Takeaways
The top model is Claude Mythos 5 by Anthropic with a BenchAlign v5 score of 83.9 and Supported evidence.
The best open-weight model is GLM-5.1 at position #15.
108 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.
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