SmolLM3 1.5B: pricing, context window and benchmarks
SmolLM3 1.5B is a small open-weight model from Hugging Face. It offers a 8,192-token context window, costs $0.04 per million input tokens and $0.17 per million output tokens, and averages 36.8 across the index's benchmark suite.
Pricing
SmolLM3 1.5B costs $0.04 per million input tokens and $0.17 per million output tokens. A blended 3:1 input-to-output mix works out to about $0.07 per million tokens. Cached input is listed at $0.00 per million tokens.
| Item | Price | Notes |
|---|---|---|
| Input | $0.04 | per 1M tokens |
| Output | $0.17 | per 1M tokens |
| Cached input | $0.00 | per 1M tokens |
| Blended 3:1 | $0.07 | estimate per 1M tokens |
| 1M in + 1M out | $0.21 | 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 | 46.7 | 599 | 1th | |
| GPQA Diamond | 31.9 | 599 | 1th | |
| SWE-bench Verified | 18.4 | 599 | 1th | |
| HumanEval | 39.8 | 597 | 1th | |
| MATH-500 | 37.7 | 596 | 1th | |
| AIME 2025 | 21.7 | 597 | 1th | |
| LiveCodeBench | 20.0 | 600 | 1th | |
| IFEval | 50.2 | 599 | 1th | |
| TruthfulQA | 44.2 | 600 | 1th | |
| HellaSwag | 58.1 | 599 | 1th | |
| MMMU | 35.8 | 599 | 1th | |
| Arena Elo | 1103.0 | 599 | 1th |
What SmolLM3 1.5B is good for
SmolLM3 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 $0.040 per million input tokens it can handle a large share of production traffic.
Strengths
- Input price of $0.040 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 |
|---|---|---|---|---|
| Pythia 7B | EleutherAI | 8192 | $0.10 | 40.7 |
| StableLM 2 1.5B | Stability AI | 16384 | $0.13 | 38.3 |
| Zephyr 1.5B | Hugging Face | 32768 | $0.10 | 38.0 |
| SmolLM3 1.5B | Hugging Face | 8192 | $0.04 | 36.8 |
| GPT-NeoX 1.5B | EleutherAI | 32768 | $0.06 | 36.4 |
| Pythia 1.5B | EleutherAI | 4096 | $0.10 | 35.8 |
FAQ
How much does SmolLM3 1.5B cost?
SmolLM3 1.5B is listed at $0.040 per million input tokens and $0.17 per million output tokens. Cached input is $0.004 per million tokens. A blended 3:1 input-to-output mix is about $0.073 per million tokens.
What is the context window of SmolLM3 1.5B?
SmolLM3 1.5B has a context window of 8,192 tokens and can emit up to 4,096 output tokens per request in this index.
Is SmolLM3 1.5B open weights?
Yes. SmolLM3 1.5B is listed as an open-weight model under a open weights licence.
How does SmolLM3 1.5B score on benchmarks?
SmolLM3 1.5B averages 36.8 across the scored benchmarks in this index, ranking #598 of 600 models. Its strongest relative result is Arena Elo at 1103.0.
What is SmolLM3 1.5B best for?
SmolLM3 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 $0.040 per million input tokens it can handle a large share of production traffic.