Nemotron Nano Instruct on MMLU-Pro: score, rank and what it means
Nemotron Nano Instruct scores 72.6 on MMLU-Pro, placing it #371 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 Nemotron Nano Instruct 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, Nemotron Nano Instruct sits at #371 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 19.0 points. The median model in this index scores 76.1, which puts Nemotron Nano Instruct below the midpoint.
NVIDIA models average 67.0 on this benchmark. Nemotron Nano Instruct is 5.6 points above that provider line, so it is a stronger result than the average NVIDIA model.
Among open-weight models, the average is 72.1. Nemotron Nano Instruct is 0.5 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 72.6 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 $2.33 per million input tokens, Nemotron Nano Instruct is below 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 |
|---|---|---|---|---|
| Hunyuan-Turbo Mini | Tencent | 8192 | $6.59 | 68.3 |
| Llama 3 Replicate Base | Replicate | 65536 | $3.15 | 72.1 |
| Mixtral Turbo Instruct | Together AI | 32768 | $3.16 | 71.9 |
| Firesearch Pro | Fireworks AI | 32768 | $7.67 | 70.4 |
| Nemotron Nano Instruct | NVIDIA | 32768 | $2.33 | 67.4 |
| Reka Core Mini | Reka AI | 8192 | $7.48 | 70.1 |
| Claude 3.7 Sonnet Nano | Anthropic | 65536 | $6.93 | 68.9 |
Other benchmarks for Nemotron Nano Instruct
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| GPQA Diamond | 64.7 | 366 | 39th | |
| 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 MMLU-Pro?
Nemotron Nano Instruct scores 72.6 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.
How does Nemotron Nano Instruct rank on MMLU-Pro?
It ranks #371 of 600 models, in the 38th percentile. The leader is GPT-5.2 Pro at 91.6.
Is Nemotron Nano Instruct 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 Nemotron Nano Instruct cost?
Nemotron Nano Instruct costs $2.33 per million input tokens and $11.92 per million output tokens.