Gemini 3 Flash Nano on MMLU-Pro: score, rank and what it means
Gemini 3 Flash Nano scores 71.4 on MMLU-Pro, placing it #386 of 600 models in the index. The benchmark average is 73.5 and the leader is GPT-5.2 Pro at 91.6.
Score distribution
The chart shows the top 10 models on MMLU-Pro, with Gemini 3 Flash Nano highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | GPT-5.2 Pro | OpenAI | 2000000 | $21.32 | 89.6 |
| 2 | GPT-5.1 Max | OpenAI | 2000000 | $20.50 | 87.9 |
| 3 | Claude Opus 4.5 Pro | Anthropic | 200000 | $20.98 | 89.1 |
| 4 | Claude Opus 4.5 Reasoning | Anthropic | 2000000 | $20.91 | 88.2 |
| 5 | Gemini 3 Pro Reasoning | Google DeepMind | 200000 | $19.69 | 87.5 |
| 6 | GPT-5.1 Reasoning | OpenAI | 400000 | $20.27 | 88.3 |
| 7 | GPT-5.2 | OpenAI | 2000000 | $19.71 | 88.0 |
| 8 | Claude Opus 4.5 Max | Anthropic | 1000000 | $20.90 | 87.8 |
| 9 | GPT-5.2 Vision | OpenAI | 1000000 | $19.03 | 87.4 |
| 10 | Gemini 3 Pro Max | Google DeepMind | 2000000 | $20.57 | 87.9 |
What this score means
On MMLU-Pro, Gemini 3 Flash Nano sits at #386 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 20.2 points. The median model in this index scores 76.1, which puts Gemini 3 Flash Nano below the midpoint.
Google DeepMind models average 80.4 on this benchmark. Gemini 3 Flash Nano is 9.0 points below that provider line, so it is a weaker result than the average Google DeepMind model.
Among mid-range models, the average is 71.5. Gemini 3 Flash Nano is 0.1 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 71.4 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 $7.56 per million input tokens, Gemini 3 Flash Nano is above the median input price of $4.00 across the 600 models scored on MMLU-Pro. 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 |
|---|---|---|---|---|
| Firesearch Max | Fireworks AI | 8192 | $7.80 | 69.9 |
| Grok 3 Nano | xAI | 8192 | $7.12 | 67.8 |
| Firesearch | Fireworks AI | 32768 | $7.03 | 68.5 |
| Gemini 2.5 Pro Nano | Google DeepMind | 32768 | $7.28 | 69.2 |
| Gemini 3 Flash Nano | Google DeepMind | 32768 | $7.56 | 69.4 |
| StableLM 2 | Stability AI | 8192 | $1.91 | 65.0 |
| Sonar Mini | Perplexity | 65536 | $6.52 | 67.9 |
Other benchmarks for Gemini 3 Flash Nano
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| GPQA Diamond | 62.9 | 384 | 36th | |
| SWE-bench Verified | 55.7 | 379 | 37th | |
| HumanEval | 73.0 | 372 | 38th | |
| MATH-500 | 75.0 | 323 | 46th | |
| AIME 2025 | 62.9 | 365 | 39th | |
| LiveCodeBench | 60.3 | 364 | 39th | |
| IFEval | 77.5 | 371 | 38th | |
| TruthfulQA | 72.0 | 379 | 37th | |
| HellaSwag | 83.4 | 308 | 49th | |
| MMMU | 69.5 | 316 | 47th | |
| Arena Elo | 1348.8 | 367 | 39th |
FAQ
What score does Gemini 3 Flash Nano get on MMLU-Pro?
Gemini 3 Flash Nano scores 71.4 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.
How does Gemini 3 Flash Nano rank on MMLU-Pro?
It ranks #386 of 600 models, in the 36th percentile. The leader is GPT-5.2 Pro at 91.6.
Is Gemini 3 Flash Nano good at MMLU-Pro?
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 MMLU-Pro score calculated?
MMLU-Pro is a knowledge 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 Nano cost?
Gemini 3 Flash Nano costs $7.56 per million input tokens and $41.55 per million output tokens.