Nemotron Nano Instruct on MATH-500: score, rank and what it means
Nemotron Nano Instruct scores 67.4 on MATH-500, placing it #404 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.
Score distribution
The chart shows the top 10 models on MATH-500, with Nemotron Nano Instruct highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | GPT-5.2 Max | OpenAI | 1000000 | $21.39 | 89.2 |
| 2 | GPT-5.1 Reasoning | OpenAI | 400000 | $20.27 | 88.3 |
| 3 | GPT-5.1 Pro | OpenAI | 200000 | $19.77 | 87.8 |
| 4 | o4 Reasoning | OpenAI | 1000000 | $19.69 | 87.5 |
| 5 | Grok 4 Max | xAI | 2000000 | $19.70 | 86.6 |
| 6 | Grok 4 Reasoning | xAI | 2000000 | $19.67 | 87.8 |
| 7 | Claude Opus 4.5 Max | Anthropic | 1000000 | $20.90 | 87.8 |
| 8 | Claude Opus 4.5 | Anthropic | 400000 | $19.42 | 87.7 |
| 9 | Gemini 3 Pro Reasoning | Google DeepMind | 200000 | $19.69 | 87.5 |
| 10 | Claude Opus 4.5 Turbo | Anthropic | 2000000 | $19.57 | 85.7 |
What this score means
On MATH-500, Nemotron Nano Instruct sits at #404 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 29.3 points. The median model in this index scores 76.0, which puts Nemotron Nano Instruct below the midpoint.
NVIDIA models average 63.9 on this benchmark. Nemotron Nano Instruct is 3.5 points above that provider line, so it is a stronger result than the average NVIDIA model.
Among open-weight models, the average is 70.8. Nemotron Nano Instruct is 3.4 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 67.4 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 $2.33 per million input tokens, Nemotron Nano Instruct is below the median input price of $4.00 across the 600 models scored on MATH-500. 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 |
|---|---|---|---|---|
| SmolLM3 | Hugging Face | 8192 | $1.71 | 63.1 |
| StableLM 2 Chat | Stability AI | 8192 | $1.79 | 64.6 |
| Nemotron Nano Chat | NVIDIA | 32768 | $2.21 | 66.2 |
| StableLM 2 Instruct | Stability AI | 16384 | $1.93 | 65.2 |
| Nemotron Nano Instruct | NVIDIA | 32768 | $2.33 | 67.4 |
| Nova Lite Mini | Amazon | 16384 | $5.43 | 65.0 |
| Embed 4 Reasoning | Cohere | 4096 | $5.50 | 65.3 |
Other benchmarks for Nemotron Nano Instruct
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 72.6 | 371 | 38th | |
| GPQA Diamond | 64.7 | 366 | 39th | |
| SWE-bench Verified | 54.5 | 388 | 35th | |
| HumanEval | 72.9 | 375 | 38th | |
| 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 MATH-500?
Nemotron Nano Instruct scores 67.4 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.
How does Nemotron Nano Instruct rank on MATH-500?
It ranks #404 of 600 models, in the 33rd percentile. The leader is GPT-5.2 Max at 96.7.
Is Nemotron Nano Instruct good at MATH-500?
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 MATH-500 score calculated?
MATH-500 is a mathematics 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.