Firesearch Reasoning on HellaSwag: score, rank and what it means
Firesearch Reasoning scores 81.8 on HellaSwag, placing it #354 of 600 models in the index. The benchmark average is 81.4 and the leader is GPT-5.2 Max at 96.1.
Score distribution
The chart shows the top 10 models on HellaSwag, with Firesearch Reasoning highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | GPT-5.2 Max | OpenAI | 1000000 | $21.39 | 89.2 |
| 2 | GPT-5.2 Turbo | OpenAI | 1000000 | $20.73 | 87.4 |
| 3 | GPT-5.2 Pro | OpenAI | 2000000 | $21.32 | 89.6 |
| 4 | Claude Opus 4.5 Pro | Anthropic | 200000 | $20.98 | 89.1 |
| 5 | Claude Sonnet 4.5 Reasoning | Anthropic | 400000 | $19.48 | 87.7 |
| 6 | Grok 4 Reasoning | xAI | 2000000 | $19.67 | 87.8 |
| 7 | GPT-5.2 Vision | OpenAI | 1000000 | $19.03 | 87.4 |
| 8 | Gemini 3 Pro Turbo | Google DeepMind | 400000 | $19.62 | 86.4 |
| 9 | GPT-5.1 Reasoning | OpenAI | 400000 | $20.27 | 88.3 |
| 10 | o4 Pro | OpenAI | 1000000 | $19.37 | 87.3 |
What this score means
On HellaSwag, Firesearch Reasoning sits at #354 of 600. The leader, GPT-5.2 Max, scores 96.1, so the gap to the top is 14.3 points. The median model in this index scores 83.6, which puts Firesearch Reasoning below the midpoint.
Fireworks AI models average 80.3 on this benchmark. Firesearch Reasoning is 1.5 points above that provider line, so it is a stronger result than the average Fireworks AI model.
Among mid-range models, the average is 79.8. Firesearch Reasoning is 2.0 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 81.8 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 $7.74 per million input tokens, Firesearch Reasoning is above the median input price of $4.00 across the 600 models scored on HellaSwag. 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 |
|---|---|---|---|---|
| Gemini 3 Pro Nano | Google DeepMind | 8192 | $9.11 | 73.2 |
| Llama 3.1 Base | Meta AI | 32768 | $3.39 | 72.2 |
| Qwen3-Max Nano | Alibaba Cloud | 65536 | $8.31 | 71.7 |
| Yi-Lightning Base | 01.AI | 16384 | $3.31 | 72.7 |
| Firesearch Reasoning | Fireworks AI | 32768 | $7.74 | 69.0 |
| Sonar Mini | Perplexity | 65536 | $6.52 | 67.9 |
| Auto Pro | OpenRouter | 8192 | $8.41 | 71.2 |
Other benchmarks for Firesearch Reasoning
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 72.3 | 381 | 37th | |
| GPQA Diamond | 63.1 | 382 | 36th | |
| SWE-bench Verified | 55.7 | 378 | 37th | |
| HumanEval | 71.9 | 380 | 37th | |
| MATH-500 | 70.9 | 380 | 37th | |
| AIME 2025 | 62.8 | 367 | 39th | |
| LiveCodeBench | 63.9 | 321 | 47th | |
| IFEval | 77.3 | 376 | 37th | |
| TruthfulQA | 71.9 | 382 | 36th | |
| MMMU | 66.9 | 361 | 40th | |
| Arena Elo | 1364.8 | 332 | 45th |
FAQ
What score does Firesearch Reasoning get on HellaSwag?
Firesearch Reasoning scores 81.8 on HellaSwag in this index. The benchmark average is 81.4 and the top score is 96.1.
How does Firesearch Reasoning rank on HellaSwag?
It ranks #354 of 600 models, in the 41st percentile. The leader is GPT-5.2 Max at 96.1.
Is Firesearch Reasoning good at HellaSwag?
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 HellaSwag score calculated?
HellaSwag is a commonsense 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 Reasoning cost?
Firesearch Reasoning costs $7.74 per million input tokens and $33.47 per million output tokens.