Nemotron Nano Instruct on GPQA Diamond: score, rank and what it means
Nemotron Nano Instruct scores 64.7 on GPQA Diamond, placing it #366 of 600 models in the index. The benchmark average is 65.6 and the leader is GPT-5.2 Reasoning at 87.0.
Score distribution
The chart shows the top 10 models on GPQA Diamond, with Nemotron Nano Instruct highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | GPT-5.2 Reasoning | OpenAI | 1000000 | $20.98 | 87.5 |
| 2 | GPT-5.1 Max | OpenAI | 2000000 | $20.50 | 87.9 |
| 3 | Claude Opus 4.5 Pro | Anthropic | 200000 | $20.98 | 89.1 |
| 4 | GPT-5.1 Reasoning | OpenAI | 400000 | $20.27 | 88.3 |
| 5 | Claude Sonnet 4.5 Reasoning | Anthropic | 400000 | $19.48 | 87.7 |
| 6 | GPT-5.2 | OpenAI | 2000000 | $19.71 | 88.0 |
| 7 | o4 Turbo | OpenAI | 2000000 | $19.34 | 86.6 |
| 8 | Claude Opus 4.5 Max | Anthropic | 1000000 | $20.90 | 87.8 |
| 9 | Grok 4 Turbo | xAI | 1000000 | $19.21 | 86.7 |
| 10 | Claude Sonnet 4.5 Max | Anthropic | 1000000 | $19.90 | 87.2 |
What this score means
On GPQA Diamond, Nemotron Nano Instruct sits at #366 of 600. The leader, GPT-5.2 Reasoning, scores 87.0, so the gap to the top is 22.3 points. The median model in this index scores 68.8, which puts Nemotron Nano Instruct below the midpoint.
NVIDIA models average 57.5 on this benchmark. Nemotron Nano Instruct is 7.2 points above that provider line, so it is a stronger result than the average NVIDIA model.
Among open-weight models, the average is 63.6. Nemotron Nano Instruct is 1.1 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 64.7 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 $2.33 per million input tokens, Nemotron Nano Instruct is below the median input price of $4.00 across the 600 models scored on GPQA Diamond. 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 |
|---|---|---|---|---|
| Phi-3.5 Chat | Microsoft | 16384 | $2.64 | 69.0 |
| Phi-4 Base | Microsoft | 32768 | $3.15 | 71.2 |
| o3 Nano | OpenAI | 32768 | $7.78 | 70.6 |
| Gemma 3 Base | Google DeepMind | 65536 | $3.09 | 71.1 |
| Nemotron Nano Instruct | NVIDIA | 32768 | $2.33 | 67.4 |
| Arctic Instruct | Snowflake | 65536 | $2.83 | 70.6 |
| Nova Pro Mini | Amazon | 16384 | $7.74 | 70.6 |
Other benchmarks for Nemotron Nano Instruct
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 72.6 | 371 | 38th | |
| SWE-bench Verified | 54.5 | 388 | 35th | |
| HumanEval | 72.9 | 375 | 38th | |
| MATH-500 | 67.4 | 404 | 33th | |
| AIME 2025 | 58.6 | 396 | 34th | |
| LiveCodeBench | 57.1 | 395 | 34th | |
| IFEval | 75.2 | 396 | 34th | |
| TruthfulQA | 73.0 | 369 | 39th | |
| HellaSwag | 79.7 | 384 | 36th | |
| MMMU | 66.2 | 368 | 39th | |
| Arena Elo | 1331.2 | 387 | 36th |
FAQ
What score does Nemotron Nano Instruct get on GPQA Diamond?
Nemotron Nano Instruct scores 64.7 on GPQA Diamond in this index. The benchmark average is 65.6 and the top score is 87.0.
How does Nemotron Nano Instruct rank on GPQA Diamond?
It ranks #366 of 600 models, in the 39th percentile. The leader is GPT-5.2 Reasoning at 87.0.
Is Nemotron Nano Instruct good at GPQA Diamond?
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 GPQA Diamond score calculated?
GPQA Diamond is a reasoning 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 Nemotron Nano Instruct cost?
Nemotron Nano Instruct costs $2.33 per million input tokens and $11.92 per million output tokens.