Auto Max on MATH-500: score, rank and what it means
Auto Max scores 72.6 on MATH-500, placing it #361 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.
Score distribution
The chart shows the top 10 models on MATH-500, with Auto Max highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | GPT-5.2 Max | OpenAI | 1000000 | $21.39 | 89.2 |
| 2 | GPT-5.1 Reasoning | OpenAI | 400000 | $20.27 | 88.3 |
| 3 | GPT-5.1 Pro | OpenAI | 200000 | $19.77 | 87.8 |
| 4 | o4 Reasoning | OpenAI | 1000000 | $19.69 | 87.5 |
| 5 | Grok 4 Max | xAI | 2000000 | $19.70 | 86.6 |
| 6 | Grok 4 Reasoning | xAI | 2000000 | $19.67 | 87.8 |
| 7 | Claude Opus 4.5 Max | Anthropic | 1000000 | $20.90 | 87.8 |
| 8 | Claude Opus 4.5 | Anthropic | 400000 | $19.42 | 87.7 |
| 9 | Gemini 3 Pro Reasoning | Google DeepMind | 200000 | $19.69 | 87.5 |
| 10 | Claude Opus 4.5 Turbo | Anthropic | 2000000 | $19.57 | 85.7 |
What this score means
On MATH-500, Auto Max sits at #361 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 24.1 points. The median model in this index scores 76.0, which puts Auto Max below the midpoint.
OpenRouter models average 70.2 on this benchmark. Auto Max is 2.4 points above that provider line, so it is a stronger result than the average OpenRouter model.
Among mid-range models, the average is 70.0. Auto Max is 2.6 points above that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.
Read a 72.6 as a relative signal, not an absolute guarantee. Benchmark scores depend on prompt format, evaluation harness and contamination controls. Use this page to narrow the shortlist, then test the model on your own data.
Price context
At $8.41 per million input tokens, Auto Max is above the median input price of $4.00 across the 600 models scored on MATH-500. The models immediately around it in the ranking are shown below so you can compare score and price together.
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| Mistral Small 3 Base | Mistral AI | 16384 | $3.19 | 72.7 |
| Firesearch Max | Fireworks AI | 8192 | $7.80 | 69.9 |
| Gemma 3 Base | Google DeepMind | 65536 | $3.09 | 71.1 |
| Sonar Mini | Perplexity | 65536 | $6.52 | 67.9 |
| Auto Max | OpenRouter | 8192 | $8.41 | 71.2 |
| OLMo 2 Base | Allen Institute for AI | 65536 | $2.39 | 67.4 |
| Claude 3.7 Sonnet Nano | Anthropic | 65536 | $6.93 | 68.9 |
Other benchmarks for Auto Max
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 74.5 | 342 | 43th | |
| GPQA Diamond | 69.4 | 280 | 53th | |
| SWE-bench Verified | 58.7 | 345 | 43th | |
| HumanEval | 73.7 | 360 | 40th | |
| AIME 2025 | 68.5 | 287 | 52th | |
| LiveCodeBench | 60.0 | 368 | 39th | |
| IFEval | 78.1 | 366 | 39th | |
| TruthfulQA | 77.5 | 267 | 56th | |
| HellaSwag | 83.5 | 302 | 50th | |
| MMMU | 66.2 | 367 | 39th | |
| Arena Elo | 1365.1 | 331 | 45th |
FAQ
What score does Auto Max get on MATH-500?
Auto Max scores 72.6 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.
How does Auto Max rank on MATH-500?
It ranks #361 of 600 models, in the 40th percentile. The leader is GPT-5.2 Max at 96.7.
Is Auto Max good at MATH-500?
It is below the median for this benchmark, so test it carefully before relying on it for this capability. The result should be read alongside its price and context window.
How is the MATH-500 score calculated?
MATH-500 is a mathematics benchmark. Scores in this index are modelled estimates on a 0–100 scale (or Elo for Arena), derived from capability tier and benchmark difficulty. They are not official leaderboard results.
What does Auto Max cost?
Auto Max costs $8.41 per million input tokens and $40.47 per million output tokens.