Gemini 3 Pro Reasoning on MATH-500: score, rank and what it means
Gemini 3 Pro Reasoning scores 93.3 on MATH-500, placing it #9 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 Gemini 3 Pro Reasoning 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, Gemini 3 Pro Reasoning sits at #9 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 3.4 points. The median model in this index scores 76.0, which puts Gemini 3 Pro Reasoning above the midpoint.
Google DeepMind models average 81.8 on this benchmark. Gemini 3 Pro Reasoning is 11.5 points above that provider line, so it is a stronger result than the average Google DeepMind model.
Among frontier models, the average is 90.8. Gemini 3 Pro Reasoning is 2.5 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 93.3 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 $19.69 per million input tokens, Gemini 3 Pro Reasoning 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 |
|---|---|---|---|---|
| Grok 4 Max | xAI | 2000000 | $19.70 | 86.6 |
| Grok 4 Reasoning | xAI | 2000000 | $19.67 | 87.8 |
| Claude Opus 4.5 Max | Anthropic | 1000000 | $20.90 | 87.8 |
| Claude Opus 4.5 | Anthropic | 400000 | $19.42 | 87.7 |
| Gemini 3 Pro Reasoning | Google DeepMind | 200000 | $19.69 | 87.5 |
| Claude Opus 4.5 Turbo | Anthropic | 2000000 | $19.57 | 85.7 |
| Qwen3-Max Reasoning | Alibaba Cloud | 2000000 | $18.12 | 85.9 |
Other benchmarks for Gemini 3 Pro Reasoning
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 90.1 | 5 | 99th | |
| GPQA Diamond | 83.9 | 13 | 98th | |
| SWE-bench Verified | 76.3 | 27 | 96th | |
| HumanEval | 90.2 | 43 | 93th | |
| AIME 2025 | 88.8 | 12 | 98th | |
| LiveCodeBench | 82.0 | 25 | 96th | |
| IFEval | 93.8 | 13 | 98th | |
| TruthfulQA | 90.3 | 2 | 100th | |
| HellaSwag | 93.5 | 18 | 97th | |
| MMMU | 80.8 | 46 | 92th | |
| Arena Elo | 1485.0 | 16 | 97th |
FAQ
What score does Gemini 3 Pro Reasoning get on MATH-500?
Gemini 3 Pro Reasoning scores 93.3 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.
How does Gemini 3 Pro Reasoning rank on MATH-500?
It ranks #9 of 600 models, in the 99th percentile. The leader is GPT-5.2 Max at 96.7.
Is Gemini 3 Pro Reasoning good at MATH-500?
Yes, relative to the index: it is above the median and inside the top half of scores. 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 Gemini 3 Pro Reasoning cost?
Gemini 3 Pro Reasoning costs $19.69 per million input tokens and $98.36 per million output tokens.