Daily By Model High Reasoning Models (excluding free models) TOP 10

Thinking Ratio Indicator Guide

The thinking ratio is calculated based on the reasoning tokens / input tokens ratio. The higher this ratio, the more internal reasoning processes the model undergoes.

This metric indicates how deeply the model thinks before generating a response. Models with a higher thinking ratio are likely to produce more sophisticated results in tasks such as complex problem solving, logical reasoning, and multi-step planning. However, a high thinking ratio does not necessarily mean better performance. In some tasks, excessive internal reasoning may incur unnecessary computational costs or be inefficient in situations where concise responses are needed. Therefore, this metric should be interpreted according to the characteristics and purpose of the task.

Rank Model Name Input Tokens Reasoning Tokens Thinking Ratio
1 perplexity/sonar-deep-research 1.15M 62.70M 54.4622
2 thudm/glm-z1-32b-0414 50.22K 165.29K 3.2912
3 thudm/glm-z1-rumination-32b-0414 21.52K 33.83K 1.5716
4 deepseek/deepseek-r1-distill-llama-8b 32.06M 35.35M 1.1026
5 deepseek/deepseek-r1-distill-llama-70b 186.27M 198.12M 1.0636
6 deepseek/deepseek-r1-distill-qwen-1.5b 2.65M 2.02M 0.7622
7 openai/o1-mini-2024-09-12 146.85K 109.25K 0.744
8 openai/o1-mini 3.80M 2.83M 0.7438
9 qwen/qwq-32b 81.89M 47.49M 0.5799
10 openai/o1-preview 1.16M 559.28K 0.4808