Firesearch Nano on SWE-bench Verified: score, rank and what it means
Firesearch Nano scores 40.0 on SWE-bench Verified, placing it #521 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 Firesearch Nano 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, Firesearch Nano sits at #521 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 41.7 points. The median model in this index scores 61.0, which puts Firesearch Nano below the midpoint.
Fireworks AI models average 55.4 on this benchmark. Firesearch Nano is 15.4 points below that provider line, so it is a weaker result than the average Fireworks AI model.
Among mid-range models, the average is 54.9. Firesearch Nano is 14.9 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 40.0 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 $2.49 per million input tokens, Firesearch Nano 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 |
|---|---|---|---|---|
| Llama 4 Maverick 1.5B | Meta AI | 8192 | $0.93 | 55.2 |
| Yi-Large 8B | 01.AI | 32768 | $0.88 | 56.1 |
| Magistral 1.5B | Mistral AI | 4096 | $0.67 | 52.7 |
| Llama 3 Novita 8B | Novita AI | 16384 | $0.99 | 55.5 |
| Firesearch Nano | Fireworks AI | 8192 | $2.49 | 55.6 |
| Kimi K2 1.5B | Moonshot AI | 4096 | $0.99 | 56.8 |
| Phi-4 8B | Microsoft | 32768 | $0.96 | 54.9 |
Other benchmarks for Firesearch Nano
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 63.0 | 488 | 19th | |
| GPQA Diamond | 52.6 | 493 | 18th | |
| HumanEval | 58.9 | 513 | 15th | |
| MATH-500 | 52.9 | 533 | 11th | |
| AIME 2025 | 45.7 | 492 | 18th | |
| LiveCodeBench | 41.5 | 529 | 12th | |
| IFEval | 66.5 | 509 | 15th | |
| TruthfulQA | 62.6 | 495 | 18th | |
| HellaSwag | 73.2 | 475 | 21th | |
| MMMU | 54.8 | 500 | 17th | |
| Arena Elo | 1229.9 | 526 | 12th |
FAQ
What score does Firesearch Nano get on SWE-bench Verified?
Firesearch Nano scores 40.0 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.
How does Firesearch Nano rank on SWE-bench Verified?
It ranks #521 of 600 models, in the 13th percentile. The leader is GPT-5.2 Pro at 81.7.
Is Firesearch Nano 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 Firesearch Nano cost?
Firesearch Nano costs $2.49 per million input tokens and $11.16 per million output tokens.