Gemini 3 Pro Nano on SWE-bench Verified: score, rank and what it means
Gemini 3 Pro Nano scores 61.2 on SWE-bench Verified, placing it #296 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.
Score distribution
The chart shows the top 10 models on SWE-bench Verified, with Gemini 3 Pro 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 | Claude Opus 4.5 Pro | Anthropic | 200000 | $20.98 | 89.1 |
| 3 | GPT-5.2 Max | OpenAI | 1000000 | $21.39 | 89.2 |
| 4 | Claude Sonnet 4.5 Max | Anthropic | 1000000 | $19.90 | 87.2 |
| 5 | GPT-5.2 | OpenAI | 2000000 | $19.71 | 88.0 |
| 6 | GPT-5.1 Pro | OpenAI | 200000 | $19.77 | 87.8 |
| 7 | o4 Max | OpenAI | 2000000 | $19.51 | 86.2 |
| 8 | Claude Sonnet 4.5 Reasoning | Anthropic | 400000 | $19.48 | 87.7 |
| 9 | Claude Opus 4.5 Max | Anthropic | 1000000 | $20.90 | 87.8 |
| 10 | Claude Opus 4.5 Reasoning | Anthropic | 2000000 | $20.91 | 88.2 |
What this score means
On SWE-bench Verified, Gemini 3 Pro Nano sits at #296 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 20.5 points. The median model in this index scores 61.0, which puts Gemini 3 Pro Nano above the midpoint.
Google DeepMind models average 66.4 on this benchmark. Gemini 3 Pro Nano is 5.2 points below that provider line, so it is a weaker result than the average Google DeepMind model.
Among mid-range models, the average is 54.9. Gemini 3 Pro Nano is 6.3 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 61.2 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 $9.11 per million input tokens, Gemini 3 Pro Nano is above the median input price of $4.00 across the 600 models scored on SWE-bench Verified. 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 |
|---|---|---|---|---|
| Gemini 3 Flash Mini | Google DeepMind | 200000 | $10.71 | 76.0 |
| Llama 3 Anyscale Instruct | Anyscale | 200000 | $3.91 | 75.7 |
| Mixtral Base | Mistral AI | 65536 | $2.95 | 71.1 |
| Claude Sonnet 4.5 Nano | Anthropic | 8192 | $8.93 | 72.0 |
| Gemini 3 Pro Nano | Google DeepMind | 8192 | $9.11 | 73.2 |
| Firesearch Max | Fireworks AI | 8192 | $7.80 | 69.9 |
| Llama 3.3 Groq Base | Groq | 16384 | $3.24 | 73.5 |
Other benchmarks for Gemini 3 Pro Nano
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 73.8 | 357 | 41th | |
| GPQA Diamond | 69.4 | 282 | 53th | |
| HumanEval | 77.7 | 293 | 51th | |
| MATH-500 | 78.3 | 251 | 58th | |
| AIME 2025 | 64.6 | 344 | 43th | |
| LiveCodeBench | 66.6 | 260 | 57th | |
| IFEval | 82.6 | 258 | 57th | |
| TruthfulQA | 77.0 | 281 | 53th | |
| HellaSwag | 81.9 | 350 | 42th | |
| MMMU | 71.8 | 250 | 58th | |
| Arena Elo | 1367.4 | 326 | 46th |
FAQ
What score does Gemini 3 Pro Nano get on SWE-bench Verified?
Gemini 3 Pro Nano scores 61.2 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.
How does Gemini 3 Pro Nano rank on SWE-bench Verified?
It ranks #296 of 600 models, in the 51st percentile. The leader is GPT-5.2 Pro at 81.7.
Is Gemini 3 Pro Nano good at SWE-bench Verified?
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 SWE-bench Verified score calculated?
SWE-bench Verified is a coding 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 Nano cost?
Gemini 3 Pro Nano costs $9.11 per million input tokens and $48.26 per million output tokens.