Monthly 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 45.39M 2.0B 44.1233
2 thudm/glm-z1-32b-0414 32.98M 71.12M 2.1564
3 thudm/glm-z1-rumination-32b-0414 14.96M 27.72M 1.8532
4 deepseek/deepseek-r1-distill-qwen-1.5b 84.57M 105.03M 1.2419
5 deepseek/deepseek-r1-distill-llama-70b 7.8B 7.2B 0.9288
6 deepseek/deepseek-r1-distill-qwen-14b 136.55M 91.45M 0.6697
7 qwen/qwq-32b 2.5B 1.6B 0.6504
8 deepseek/deepseek-r1-0528-qwen3-8b 643.23M 392.25M 0.6098
9 openai/o1-mini 206.32M 117.34M 0.5687
10 deepseek/deepseek-r1-distill-llama-8b 987.75M 485.04M 0.4911