Gemini 3 Pro Nano on LiveCodeBench: score, rank and what it means
Gemini 3 Pro Nano scores 66.6 on LiveCodeBench, placing it #260 of 600 models in the index. The benchmark average is 61.0 and the leader is Claude Sonnet 4.5 Reasoning at 85.1.
Score distribution
The chart shows the top 10 models on LiveCodeBench, with Gemini 3 Pro Nano highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | Claude Sonnet 4.5 Reasoning | Anthropic | 400000 | $19.48 | 87.7 |
| 2 | Grok 4 Reasoning | xAI | 2000000 | $19.67 | 87.8 |
| 3 | Claude Opus 4.5 | Anthropic | 400000 | $19.42 | 87.7 |
| 4 | GPT-5.2 Max | OpenAI | 1000000 | $21.39 | 89.2 |
| 5 | Grok 4 Turbo | xAI | 1000000 | $19.21 | 86.7 |
| 6 | GPT-5.1 Turbo | OpenAI | 2000000 | $19.19 | 86.1 |
| 7 | GPT-5.2 Pro | OpenAI | 2000000 | $21.32 | 89.6 |
| 8 | GPT-5.2 Reasoning | OpenAI | 1000000 | $20.98 | 87.5 |
| 9 | Claude Opus 4.5 Pro | Anthropic | 200000 | $20.98 | 89.1 |
| 10 | Gemini 3 Pro Max | Google DeepMind | 2000000 | $20.57 | 87.9 |
What this score means
On LiveCodeBench, Gemini 3 Pro Nano sits at #260 of 600. The leader, Claude Sonnet 4.5 Reasoning, scores 85.1, so the gap to the top is 18.5 points. The median model in this index scores 64.7, which puts Gemini 3 Pro Nano above the midpoint.
Google DeepMind models average 70.8 on this benchmark. Gemini 3 Pro Nano is 4.2 points below that provider line, so it is a weaker result than the average Google DeepMind model.
Among mid-range models, the average is 58.3. Gemini 3 Pro Nano is 8.3 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 66.6 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 $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 LiveCodeBench. 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 3 Replicate Base | Replicate | 65536 | $3.15 | 72.1 |
| Llama 3.3 Groq Base | Groq | 16384 | $3.24 | 73.5 |
| Llama 3.1 | Meta AI | 200000 | $4.16 | 76.3 |
| Fusion Reasoning | OpenRouter | 65536 | $8.59 | 72.2 |
| Gemini 3 Pro Nano | Google DeepMind | 8192 | $9.11 | 73.2 |
| Reka Flash Max | Reka AI | 32768 | $11.17 | 75.9 |
| Grok 4 Nano | xAI | 16384 | $8.97 | 72.7 |
Other benchmarks for Gemini 3 Pro Nano
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 73.8 | 357 | 41th | |
| GPQA Diamond | 69.4 | 282 | 53th | |
| SWE-bench Verified | 61.2 | 296 | 51th | |
| HumanEval | 77.7 | 293 | 51th | |
| MATH-500 | 78.3 | 251 | 58th | |
| AIME 2025 | 64.6 | 344 | 43th | |
| 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 LiveCodeBench?
Gemini 3 Pro Nano scores 66.6 on LiveCodeBench in this index. The benchmark average is 61.0 and the top score is 85.1.
How does Gemini 3 Pro Nano rank on LiveCodeBench?
It ranks #260 of 600 models, in the 57th percentile. The leader is Claude Sonnet 4.5 Reasoning at 85.1.
Is Gemini 3 Pro Nano good at LiveCodeBench?
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 LiveCodeBench score calculated?
LiveCodeBench 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.