Best AI Models for Math
A math-focused composite built from arithmetic, competition-style, and frontier math benchmarks. Saturated benchmarks are useful, but coverage across harder tests gets clearer confidence.
GPT-5.5 Pro
OpenAI
GPT-5.5
OpenAI
DeepSeek V4 Pro
DeepSeek
Ranked model table
Scores are based on the visible benchmark set and available metadata.
| Rank | Model | Score | Evidence | Input price | Context |
|---|---|---|---|---|---|
| #1 | GPT-5.5 Pro OpenAI | 92.5 | 3 benchmarks · Medium | $30/M | 1.1M |
| #2 | GPT-5.5 OpenAI | 88.8 | 3 benchmarks · Medium | $5.00/M | 1.1M |
| #3 | DeepSeek V4 Pro DeepSeek | 88.5 | 2 benchmarks · Medium | $0.88/M | 1.0M |
| #4 | GPT-5.4 Pro OpenAI | 85.5 | 2 benchmarks · Medium | $30/M | 1.1M |
| #5 | Qwen3.7 Max Alibaba Qwen | 83.5 | 2 benchmarks · Medium | $1.48/M | 1M |
| #6 | GLM 5.2 z-ai | 83.1 | 2 benchmarks · Medium | <$0.01/M | 1.0M |
| #7 | Claude Opus 4.8 Anthropic | 82.4 | 3 benchmarks · Medium | $5.00/M | 1M |
| #8 | Grok 4.3 xAI | 81.8 | 2 benchmarks · Medium | $1.25/M | 1M |
| #9 | o4 Mini (batch) OpenAI | 81.6 | 2 benchmarks · Medium | $0.55/M | 200K |
| #10 | GPT-5.4 OpenAI | 79.2 | 3 benchmarks · Medium | $2.50/M | 1.1M |
| #11 | Kimi K2.7 Code moonshotai | 78.9 | 2 benchmarks · Medium | $0.60/M | 262K |
| #12 | o3 Mini (batch) OpenAI | 78.8 | 2 benchmarks · Medium | $0.55/M | 200K |
Math scores depend heavily on benchmark format. Use the linked benchmark pages before choosing a model for high-stakes mathematical work.
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
Which benchmarks are used for math rankings?
This page uses GSM8K, MATH-level tasks, AIME-style tasks, and FrontierMath when those scores are available.
Why show confidence labels?
A model with two math scores is less proven than a model with broader evidence. Confidence labels make that coverage visible.
Can this ranking replace manual math validation?
No. Use it as a benchmark-backed shortlist, then validate models on your own problem set.