SmolLM3 Instruct on SWE-bench Verified: score, rank and what it means
SmolLM3 Instruct scores 50.6 on SWE-bench Verified, placing it #417 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.
Score distribution
The chart shows the top 10 models on SWE-bench Verified, with SmolLM3 Instruct highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | GPT-5.2 Pro | OpenAI | 2000000 | $21.32 | 89.6 |
| 2 | Claude Opus 4.5 Pro | Anthropic | 200000 | $20.98 | 89.1 |
| 3 | GPT-5.2 Max | OpenAI | 1000000 | $21.39 | 89.2 |
| 4 | Claude Sonnet 4.5 Max | Anthropic | 1000000 | $19.90 | 87.2 |
| 5 | GPT-5.2 | OpenAI | 2000000 | $19.71 | 88.0 |
| 6 | GPT-5.1 Pro | OpenAI | 200000 | $19.77 | 87.8 |
| 7 | o4 Max | OpenAI | 2000000 | $19.51 | 86.2 |
| 8 | Claude Sonnet 4.5 Reasoning | Anthropic | 400000 | $19.48 | 87.7 |
| 9 | Claude Opus 4.5 Max | Anthropic | 1000000 | $20.90 | 87.8 |
| 10 | Claude Opus 4.5 Reasoning | Anthropic | 2000000 | $20.91 | 88.2 |
What this score means
On SWE-bench Verified, SmolLM3 Instruct sits at #417 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 31.1 points. The median model in this index scores 61.0, which puts SmolLM3 Instruct below the midpoint.
Hugging Face models average 37.7 on this benchmark. SmolLM3 Instruct is 12.9 points above that provider line, so it is a stronger result than the average Hugging Face model.
Among open-weight models, the average is 55.5. SmolLM3 Instruct is 4.9 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 50.6 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 $1.69 per million input tokens, SmolLM3 Instruct is below the median input price of $4.00 across the 600 models scored on SWE-bench Verified. 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 |
|---|---|---|---|---|
| Qwen3 8B | Alibaba Cloud | 8192 | $1.66 | 61.9 |
| Kimi K2 8B | Moonshot AI | 8192 | $1.86 | 64.7 |
| Embed 4 Turbo | Cohere | 4096 | $5.13 | 64.0 |
| Reka Flash Mini | Reka AI | 16384 | $5.77 | 65.5 |
| SmolLM3 Instruct | Hugging Face | 8192 | $1.69 | 63.6 |
| GPT-NeoX Instruct | EleutherAI | 16384 | $1.52 | 62.6 |
| Zephyr | Hugging Face | 8192 | $1.61 | 62.8 |
Other benchmarks for SmolLM3 Instruct
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 67.3 | 428 | 29th | |
| GPQA Diamond | 58.4 | 423 | 30th | |
| HumanEval | 69.2 | 408 | 32th | |
| MATH-500 | 67.0 | 409 | 32th | |
| AIME 2025 | 54.4 | 419 | 30th | |
| LiveCodeBench | 56.1 | 397 | 34th | |
| IFEval | 74.0 | 406 | 32th | |
| TruthfulQA | 66.5 | 438 | 27th | |
| HellaSwag | 75.1 | 440 | 27th | |
| MMMU | 60.5 | 425 | 29th | |
| Arena Elo | 1297.8 | 423 | 30th |
FAQ
What score does SmolLM3 Instruct get on SWE-bench Verified?
SmolLM3 Instruct scores 50.6 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.
How does SmolLM3 Instruct rank on SWE-bench Verified?
It ranks #417 of 600 models, in the 31st percentile. The leader is GPT-5.2 Pro at 81.7.
Is SmolLM3 Instruct good at SWE-bench Verified?
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 SWE-bench Verified score calculated?
SWE-bench Verified is a coding 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 SmolLM3 Instruct cost?
SmolLM3 Instruct costs $1.69 per million input tokens and $8.67 per million output tokens.