Nemotron Nano 7B: pricing, context window and benchmarks
Nemotron Nano 7B is a small open-weight model from NVIDIA. It offers a 8,192-token context window, costs $0.35 per million input tokens and $1.30 per million output tokens, and averages 47.5 across the index's benchmark suite.
Pricing
Nemotron Nano 7B costs $0.35 per million input tokens and $1.30 per million output tokens. A blended 3:1 input-to-output mix works out to about $0.59 per million tokens. Cached input is listed at $0.03 per million tokens.
| Item | Price | Notes |
|---|---|---|
| Input | $0.35 | per 1M tokens |
| Output | $1.30 | per 1M tokens |
| Cached input | $0.03 | per 1M tokens |
| Blended 3:1 | $0.59 | estimate per 1M tokens |
| 1M in + 1M out | $1.65 | estimate |
Benchmarks
Scores below are modelled estimates on a 0–100 scale. Use them to compare relative position, not as official vendor results.
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 56.9 | 561 | 7th | |
| GPQA Diamond | 41.7 | 580 | 3th | |
| SWE-bench Verified | 31.0 | 576 | 4th | |
| HumanEval | 49.5 | 576 | 4th | |
| MATH-500 | 48.7 | 570 | 5th | |
| AIME 2025 | 31.3 | 580 | 3th | |
| LiveCodeBench | 32.3 | 579 | 4th | |
| IFEval | 60.7 | 569 | 5th | |
| TruthfulQA | 56.6 | 558 | 7th | |
| HellaSwag | 67.6 | 560 | 7th | |
| MMMU | 46.7 | 575 | 4th | |
| Arena Elo | 1180.8 | 575 | 4th |
What Nemotron Nano 7B is good for
Nemotron Nano 7B is a small model. That makes it a good fit for classification, extraction, routing, summarization and other high-volume jobs where latency and cost dominate. It is not the right pick for open-ended frontier reasoning, but at $0.35 per million input tokens it can handle a large share of production traffic.
Strengths
- Input price of $0.35 per million tokens is at or below the index median.
- Open weights make self-hosting, fine-tuning and data-residency review possible.
Trade-offs
- Context window of 8,192 tokens is small for long-document work.
- Average benchmark score is below the index median; test it on your own task.
- Less reliable on open-ended reasoning and complex multi-step instructions.
How it compares
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| Phi-4 1.5B | Microsoft | 8192 | $0.42 | 48.1 |
| Mixtral 1.5B | Mistral AI | 16384 | $0.36 | 47.7 |
| Mixtral Turbo 1.5B | Together AI | 4096 | $0.31 | 47.6 |
| Nemotron Nano 7B | NVIDIA | 8192 | $0.35 | 47.5 |
| DBRX 1.5B | Databricks | 16384 | $0.32 | 47.0 |
| StableLM 2 8B | Stability AI | 32768 | $0.38 | 47.0 |
FAQ
How much does Nemotron Nano 7B cost?
Nemotron Nano 7B is listed at $0.35 per million input tokens and $1.30 per million output tokens. Cached input is $0.030 per million tokens. A blended 3:1 input-to-output mix is about $0.59 per million tokens.
What is the context window of Nemotron Nano 7B?
Nemotron Nano 7B has a context window of 8,192 tokens and can emit up to 8,192 output tokens per request in this index.
Is Nemotron Nano 7B open weights?
Yes. Nemotron Nano 7B is listed as an open-weight model under a open weights licence.
How does Nemotron Nano 7B score on benchmarks?
Nemotron Nano 7B averages 47.5 across the scored benchmarks in this index, ranking #577 of 600 models. Its strongest relative result is Arena Elo at 1180.8.
What is Nemotron Nano 7B best for?
Nemotron Nano 7B is a small model. That makes it a good fit for classification, extraction, routing, summarization and other high-volume jobs where latency and cost dominate. It is not the right pick for open-ended frontier reasoning, but at $0.35 per million input tokens it can handle a large share of production traffic.