Nemotron 4 1.5B: pricing, context window and benchmarks
Nemotron 4 1.5B is a small open-weight model from NVIDIA. It offers a 32,768-token context window, costs $0.49 per million input tokens and $2.67 per million output tokens, and averages 50.8 across the index's benchmark suite.
Pricing
Nemotron 4 1.5B costs $0.49 per million input tokens and $2.67 per million output tokens. A blended 3:1 input-to-output mix works out to about $1.03 per million tokens. Cached input is listed at $0.06 per million tokens.
| Item | Price | Notes |
|---|---|---|
| Input | $0.49 | per 1M tokens |
| Output | $2.67 | per 1M tokens |
| Cached input | $0.06 | per 1M tokens |
| Blended 3:1 | $1.03 | estimate per 1M tokens |
| 1M in + 1M out | $3.16 | 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 | 57.3 | 558 | 7th | |
| GPQA Diamond | 46.0 | 559 | 7th | |
| SWE-bench Verified | 35.4 | 555 | 8th | |
| HumanEval | 55.0 | 544 | 9th | |
| MATH-500 | 50.7 | 551 | 8th | |
| AIME 2025 | 40.9 | 538 | 10th | |
| LiveCodeBench | 37.6 | 553 | 8th | |
| IFEval | 60.9 | 565 | 6th | |
| TruthfulQA | 56.1 | 566 | 6th | |
| HellaSwag | 68.1 | 557 | 7th | |
| MMMU | 50.9 | 545 | 9th | |
| Arena Elo | 1201.8 | 550 | 8th |
What Nemotron 4 1.5B is good for
Nemotron 4 1.5B 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 $0.49 per million input tokens it can handle a large share of production traffic.
Strengths
- Input price of $0.49 per million tokens is at or below the index median.
- Open weights make self-hosting, fine-tuning and data-residency review possible.
Trade-offs
- 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-3.5 8B | Microsoft | 16384 | $0.59 | 51.2 |
| DeepSeek-Coder 1.5B | DeepSeek | 16384 | $0.58 | 51.0 |
| Nemotron 4 1.5B | NVIDIA | 32768 | $0.49 | 50.8 |
| Llama 3.1 1.5B | Meta AI | 4096 | $0.53 | 50.7 |
| Hunyuan-Large 1.5B | Tencent | 8192 | $0.54 | 50.5 |
| Phi-3.5 7B | Microsoft | 4096 | $0.50 | 50.5 |
FAQ
How much does Nemotron 4 1.5B cost?
Nemotron 4 1.5B is listed at $0.49 per million input tokens and $2.67 per million output tokens. Cached input is $0.061 per million tokens. A blended 3:1 input-to-output mix is about $1.03 per million tokens.
What is the context window of Nemotron 4 1.5B?
Nemotron 4 1.5B has a context window of 32,768 tokens and can emit up to 8,192 output tokens per request in this index.
Is Nemotron 4 1.5B open weights?
Yes. Nemotron 4 1.5B is listed as an open-weight model under a open weights licence.
How does Nemotron 4 1.5B score on benchmarks?
Nemotron 4 1.5B averages 50.8 across the scored benchmarks in this index, ranking #548 of 600 models. Its strongest relative result is Arena Elo at 1201.8.
What is Nemotron 4 1.5B best for?
Nemotron 4 1.5B 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 $0.49 per million input tokens it can handle a large share of production traffic.