Gemini 3 Flash Reasoning on MMMU: score, rank and what it means
Gemini 3 Flash Reasoning scores 79.8 on MMMU, placing it #58 of 600 models in the index. The benchmark average is 67.1 and the leader is GPT-5.2 Max at 86.9.
Score distribution
The chart shows the top 10 models on MMMU, with Gemini 3 Flash 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-5.2 | OpenAI | 2000000 | $19.71 | 88.0 |
| 4 | Claude Sonnet 4.5 Reasoning | Anthropic | 400000 | $19.48 | 87.7 |
| 5 | GPT-5.1 Max | OpenAI | 2000000 | $20.50 | 87.9 |
| 6 | Grok 4 Pro | xAI | 1000000 | $19.00 | 85.4 |
| 7 | Claude Opus 4.5 Reasoning | Anthropic | 2000000 | $20.91 | 88.2 |
| 8 | Claude Sonnet 4.5 Pro | Anthropic | 1000000 | $18.73 | 86.7 |
| 9 | GPT-5.2 Pro | OpenAI | 2000000 | $21.32 | 89.6 |
| 10 | GPT-5.2 Vision | OpenAI | 1000000 | $19.03 | 87.4 |
What this score means
On MMMU, Gemini 3 Flash Reasoning sits at #58 of 600. The leader, GPT-5.2 Max, scores 86.9, so the gap to the top is 7.1 points. The median model in this index scores 70.0, which puts Gemini 3 Flash Reasoning above the midpoint.
Google DeepMind models average 74.9 on this benchmark. Gemini 3 Flash Reasoning is 4.9 points above that provider line, so it is a stronger result than the average Google DeepMind model.
Among frontier models, the average is 81.6. Gemini 3 Flash Reasoning is 1.8 points below that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.
Read a 79.8 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 $17.05 per million input tokens, Gemini 3 Flash Reasoning is above the median input price of $4.00 across the 600 models scored on MMMU. 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 |
|---|---|---|---|---|
| Llama 4 Maverick | Meta AI | 200000 | $5.36 | 81.6 |
| Gemini 3 Flash Vision | Google DeepMind | 400000 | $15.06 | 81.9 |
| Grok 4 Max | xAI | 2000000 | $19.70 | 86.6 |
| DeepSeek-V3.2 | DeepSeek | 1000000 | $5.88 | 84.0 |
| Gemini 3 Flash Reasoning | Google DeepMind | 128000 | $17.05 | 83.7 |
| Claude 3.7 Sonnet Pro | Anthropic | 128000 | $16.07 | 83.3 |
| Claude Sonnet 4.5 Vision | Anthropic | 128000 | $16.99 | 83.8 |
Other benchmarks for Gemini 3 Flash Reasoning
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 87.2 | 32 | 95th | |
| GPQA Diamond | 81.8 | 32 | 95th | |
| SWE-bench Verified | 72.5 | 67 | 89th | |
| HumanEval | 86.9 | 91 | 85th | |
| MATH-500 | 86.2 | 89 | 85th | |
| AIME 2025 | 79.4 | 96 | 84th | |
| LiveCodeBench | 77.7 | 69 | 89th | |
| IFEval | 90.2 | 67 | 89th | |
| TruthfulQA | 86.6 | 43 | 93th | |
| HellaSwag | 92.2 | 40 | 93th | |
| Arena Elo | 1446.9 | 85 | 86th |
FAQ
What score does Gemini 3 Flash Reasoning get on MMMU?
Gemini 3 Flash Reasoning scores 79.8 on MMMU in this index. The benchmark average is 67.1 and the top score is 86.9.
How does Gemini 3 Flash Reasoning rank on MMMU?
It ranks #58 of 600 models, in the 90th percentile. The leader is GPT-5.2 Max at 86.9.
Is Gemini 3 Flash Reasoning good at MMMU?
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 MMMU score calculated?
MMMU is a multimodal 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 Flash Reasoning cost?
Gemini 3 Flash Reasoning costs $17.05 per million input tokens and $66.77 per million output tokens.