Firesearch Mini on SWE-bench Verified: score, rank and what it means
Firesearch Mini scores 48.8 on SWE-bench Verified, placing it #432 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 Mini 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 Mini sits at #432 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 32.9 points. The median model in this index scores 61.0, which puts Firesearch Mini below the midpoint.
Fireworks AI models average 55.4 on this benchmark. Firesearch Mini is 6.6 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 Mini is 6.1 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 48.8 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 $3.94 per million input tokens, Firesearch Mini 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 |
|---|---|---|---|---|
| Mistral Large 3 7B | Mistral AI | 32768 | $1.44 | 61.1 |
| Zephyr Chat | Hugging Face | 4096 | $1.49 | 61.3 |
| Mistral Large 3 8B | Mistral AI | 16384 | $1.62 | 61.6 |
| Auto Mini | OpenRouter | 4096 | $4.24 | 61.6 |
| Firesearch Mini | Fireworks AI | 32768 | $3.94 | 61.5 |
| Reka Flash Nano | Reka AI | 16384 | $3.93 | 61.0 |
| GLM-4.5 8B | Zhipu AI | 32768 | $1.46 | 61.3 |
Other benchmarks for Firesearch Mini
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 67.5 | 424 | 29th | |
| GPQA Diamond | 55.7 | 452 | 25th | |
| HumanEval | 65.5 | 435 | 28th | |
| MATH-500 | 59.6 | 471 | 22th | |
| AIME 2025 | 50.8 | 453 | 25th | |
| LiveCodeBench | 50.9 | 440 | 27th | |
| IFEval | 72.8 | 420 | 30th | |
| TruthfulQA | 66.5 | 436 | 27th | |
| HellaSwag | 77.2 | 412 | 31th | |
| MMMU | 61.1 | 419 | 30th | |
| Arena Elo | 1280.3 | 445 | 26th |
FAQ
What score does Firesearch Mini get on SWE-bench Verified?
Firesearch Mini scores 48.8 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.
How does Firesearch Mini rank on SWE-bench Verified?
It ranks #432 of 600 models, in the 28th percentile. The leader is GPT-5.2 Pro at 81.7.
Is Firesearch Mini 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 Mini cost?
Firesearch Mini costs $3.94 per million input tokens and $16.76 per million output tokens.