Best AI Models for Reasoning
A composite ranking for multi-step reasoning, abstract inference, and difficult question answering. The table favors models with evidence across more than one reasoning benchmark.
GPT-6 Astra
OpenAI
Claude Opus 5
Anthropic
GPT-6.1 Sol
OpenAI
Ranked model table
Scores are based on the visible benchmark set and available metadata.
| Rank | Model | Score | Evidence | Input price | Context |
|---|---|---|---|---|---|
| #1 | GPT-6 Astra OpenAI | 94.8 | 4 benchmarks · High | $10/M | 1.1M |
| #2 | Claude Opus 5 Anthropic | 92.3 | 4 benchmarks · High | $5.00/M | 1M |
| #3 | GPT-6.1 Sol OpenAI | 91.8 | 3 benchmarks · Medium | $2.00/M | 1.1M |
| #4 | Gemini 4 Argon Google DeepMind | 91.3 | 3 benchmarks · Medium | $2.00/M | 1.0M |
| #5 | Claude Fable 5 Anthropic | 90 | 4 benchmarks · High | $10/M | 1M |
| #6 | GPT-5.6 Sol OpenAI | 89.4 | 4 benchmarks · High | $4.00/M | 1.1M |
| #7 | Claude Opus 5.5 Anthropic | 89.4 | 3 benchmarks · Medium | $4.00/M | 1M |
| #8 | GPT-5.5 Pro OpenAI | 89.2 | 4 benchmarks · High | $30/M | 1.1M |
| #9 | GPT-6 Sol OpenAI | 88.9 | 3 benchmarks · Medium | $2.00/M | 1.1M |
| #10 | Gemini 3.8 Flash Google DeepMind | 88.3 | 4 benchmarks · High | $0.82/M | 1.0M |
| #11 | Gemini 3.7 Flash Google DeepMind | 87.5 | 3 benchmarks · Medium | $0.75/M | 1.0M |
| #12 | Claude Sonnet 5.5 Anthropic | 87.3 | 4 benchmarks · High | $2.00/M | 1M |
Reasoning benchmarks are proxies. A high score here does not guarantee better answers in every professional or domain-specific setting.
BenchGecko ranks models from published benchmark scores and model metadata. Scores do not measure every use case, and missing data can affect rankings.
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Questions
What makes a model good at reasoning?
BenchGecko uses published scores on reasoning benchmarks such as GPQA Diamond, BBH, ARC-AGI, SimpleBench, and HLE.
Why not use one benchmark only?
Single benchmarks can be saturated or narrow. The composite uses multiple reasoning tests and labels confidence by evidence coverage.
Are missing scores counted as zero?
No. Missing scores reduce coverage confidence but are not treated as failed benchmark attempts.