Firefunction Reasoning on HumanEval: score, rank and what it means
Firefunction Reasoning scores 77.2 on HumanEval, placing it #306 of 600 models in the index. The benchmark average is 74.0 and the leader is GPT-5.2 Pro at 96.8.
Score distribution
The chart shows the top 10 models on HumanEval, with Firefunction Reasoning 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 | Gemini 3 Pro Pro | Google DeepMind | 200000 | $19.80 | 87.3 |
| 3 | o4 Max | OpenAI | 2000000 | $19.51 | 86.2 |
| 4 | Grok 4 Reasoning | xAI | 2000000 | $19.67 | 87.8 |
| 5 | Claude Opus 4.5 | Anthropic | 400000 | $19.42 | 87.7 |
| 6 | GPT-5.2 Max | OpenAI | 1000000 | $21.39 | 89.2 |
| 7 | Claude Opus 4.5 Pro | Anthropic | 200000 | $20.98 | 89.1 |
| 8 | Grok 4 Max | xAI | 2000000 | $19.70 | 86.6 |
| 9 | GPT-5.2 Turbo | OpenAI | 1000000 | $20.73 | 87.4 |
| 10 | GPT-5.1 Reasoning | OpenAI | 400000 | $20.27 | 88.3 |
What this score means
On HumanEval, Firefunction Reasoning sits at #306 of 600. The leader, GPT-5.2 Pro, scores 96.8, so the gap to the top is 19.6 points. The median model in this index scores 77.4, which puts Firefunction Reasoning below the midpoint.
Fireworks AI models average 71.5 on this benchmark. Firefunction Reasoning is 5.7 points above that provider line, so it is a stronger result than the average Fireworks AI model.
Among mid-range models, the average is 71.5. Firefunction Reasoning is 5.7 points above that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.
Read a 77.2 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 $9.08 per million input tokens, Firefunction Reasoning is above the median input price of $4.00 across the 600 models scored on HumanEval. 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 |
|---|---|---|---|---|
| Auto Reasoning | OpenRouter | 65536 | $8.39 | 71.5 |
| Grok 3 Mini | xAI | 200000 | $9.67 | 73.9 |
| Llama 3 DeepInfra Chat | DeepInfra | 200000 | $3.74 | 75.0 |
| OLMo 2 | Allen Institute for AI | 65536 | $2.99 | 71.0 |
| Firefunction Reasoning | Fireworks AI | 8192 | $9.08 | 72.5 |
| Yi-Large Instruct | 01.AI | 32768 | $3.48 | 73.4 |
| QwQ Base | Alibaba Cloud | 65536 | $3.68 | 74.1 |
Other benchmarks for Firefunction Reasoning
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 73.7 | 359 | 40th | |
| GPQA Diamond | 68.2 | 309 | 49th | |
| SWE-bench Verified | 58.2 | 351 | 42th | |
| MATH-500 | 76.5 | 290 | 52th | |
| AIME 2025 | 71.4 | 232 | 61th | |
| LiveCodeBench | 63.7 | 326 | 46th | |
| IFEval | 81.1 | 306 | 49th | |
| TruthfulQA | 77.5 | 268 | 55th | |
| HellaSwag | 83.1 | 316 | 47th | |
| MMMU | 67.3 | 354 | 41th | |
| Arena Elo | 1359.1 | 346 | 42th |
FAQ
What score does Firefunction Reasoning get on HumanEval?
Firefunction Reasoning scores 77.2 on HumanEval in this index. The benchmark average is 74.0 and the top score is 96.8.
How does Firefunction Reasoning rank on HumanEval?
It ranks #306 of 600 models, in the 49th percentile. The leader is GPT-5.2 Pro at 96.8.
Is Firefunction Reasoning good at HumanEval?
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 HumanEval score calculated?
HumanEval 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 Firefunction Reasoning cost?
Firefunction Reasoning costs $9.08 per million input tokens and $42.16 per million output tokens.