Nemotron 4 7B: pricing, context window and benchmarks
Nemotron 4 7B is a small open-weight model from NVIDIA. It offers a 16,384-token context window, costs $1.05 per million input tokens and $4.86 per million output tokens, and averages 56.9 across the index's benchmark suite.
Pricing
Nemotron 4 7B costs $1.05 per million input tokens and $4.86 per million output tokens. A blended 3:1 input-to-output mix works out to about $2.00 per million tokens. Cached input is listed at $0.12 per million tokens.
| Item | Price | Notes |
|---|---|---|
| Input | $1.05 | per 1M tokens |
| Output | $4.86 | per 1M tokens |
| Cached input | $0.12 | per 1M tokens |
| Blended 3:1 | $2.00 | estimate per 1M tokens |
| 1M in + 1M out | $5.91 | 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 | 60.5 | 523 | 13th | |
| GPQA Diamond | 51.4 | 505 | 16th | |
| SWE-bench Verified | 45.4 | 463 | 23th | |
| HumanEval | 58.1 | 522 | 13th | |
| MATH-500 | 58.9 | 478 | 20th | |
| AIME 2025 | 44.2 | 507 | 16th | |
| LiveCodeBench | 47.9 | 467 | 22th | |
| IFEval | 69.3 | 470 | 22th | |
| TruthfulQA | 63.7 | 477 | 21th | |
| HellaSwag | 73.1 | 479 | 20th | |
| MMMU | 53.8 | 512 | 15th | |
| Arena Elo | 1244.0 | 499 | 17th |
What Nemotron 4 7B is good for
Nemotron 4 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 $1.05 per million input tokens it can handle a large share of production traffic.
Strengths
- Input price of $1.05 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 16,384 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 |
|---|---|---|---|---|
| Codestral 7B | Mistral AI | 32768 | $1.05 | 57.0 |
| Llama 3 Baseten 8B | Baseten | 4096 | $1.02 | 57.0 |
| Llama 3 Replicate 8B | Replicate | 32768 | $1.00 | 57.0 |
| DeepSeek-V3.2 1.5B | DeepSeek | 4096 | $1.02 | 56.9 |
| Nemotron 4 7B | NVIDIA | 16384 | $1.05 | 56.9 |
| Fusion Nano | OpenRouter | 8192 | $2.96 | 56.8 |
FAQ
How much does Nemotron 4 7B cost?
Nemotron 4 7B is listed at $1.05 per million input tokens and $4.86 per million output tokens. Cached input is $0.12 per million tokens. A blended 3:1 input-to-output mix is about $2.00 per million tokens.
What is the context window of Nemotron 4 7B?
Nemotron 4 7B has a context window of 16,384 tokens and can emit up to 4,096 output tokens per request in this index.
Is Nemotron 4 7B open weights?
Yes. Nemotron 4 7B is listed as an open-weight model under a open weights licence.
How does Nemotron 4 7B score on benchmarks?
Nemotron 4 7B averages 56.9 across the scored benchmarks in this index, ranking #491 of 600 models. Its strongest relative result is Arena Elo at 1244.0.
What is Nemotron 4 7B best for?
Nemotron 4 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 $1.05 per million input tokens it can handle a large share of production traffic.