Firesearch Nano on MATH-500: score, rank and what it means
Firesearch Nano scores 52.9 on MATH-500, placing it #533 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.
Score distribution
The chart shows the top 10 models on MATH-500, with Firesearch Nano 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-5.1 Reasoning | OpenAI | 400000 | $20.27 | 88.3 |
| 3 | GPT-5.1 Pro | OpenAI | 200000 | $19.77 | 87.8 |
| 4 | o4 Reasoning | OpenAI | 1000000 | $19.69 | 87.5 |
| 5 | Grok 4 Max | xAI | 2000000 | $19.70 | 86.6 |
| 6 | Grok 4 Reasoning | xAI | 2000000 | $19.67 | 87.8 |
| 7 | Claude Opus 4.5 Max | Anthropic | 1000000 | $20.90 | 87.8 |
| 8 | Claude Opus 4.5 | Anthropic | 400000 | $19.42 | 87.7 |
| 9 | Gemini 3 Pro Reasoning | Google DeepMind | 200000 | $19.69 | 87.5 |
| 10 | Claude Opus 4.5 Turbo | Anthropic | 2000000 | $19.57 | 85.7 |
What this score means
On MATH-500, Firesearch Nano sits at #533 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 43.8 points. The median model in this index scores 76.0, which puts Firesearch Nano below the midpoint.
Fireworks AI models average 69.4 on this benchmark. Firesearch Nano is 16.5 points below that provider line, so it is a weaker result than the average Fireworks AI model.
Among mid-range models, the average is 70.0. Firesearch Nano is 17.1 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 52.9 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 MATH-500. 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 |
|---|---|---|---|---|
| Yi-Large 8B | 01.AI | 32768 | $0.88 | 56.1 |
| MiniMax-M2 1.5B | MiniMax | 16384 | $0.84 | 54.6 |
| GLM-4.5 1.5B | Zhipu AI | 16384 | $0.65 | 53.3 |
| Kimi K1.5 1.5B | Moonshot AI | 4096 | $0.75 | 53.2 |
| Firesearch Nano | Fireworks AI | 8192 | $2.49 | 55.6 |
| Magistral 1.5B | Mistral AI | 4096 | $0.67 | 52.7 |
| Arctic 8B | Snowflake | 8192 | $0.65 | 52.4 |
Other benchmarks for Firesearch Nano
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 63.0 | 488 | 19th | |
| GPQA Diamond | 52.6 | 493 | 18th | |
| SWE-bench Verified | 40.0 | 521 | 13th | |
| HumanEval | 58.9 | 513 | 15th | |
| 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 MATH-500?
Firesearch Nano scores 52.9 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.
How does Firesearch Nano rank on MATH-500?
It ranks #533 of 600 models, in the 11th percentile. The leader is GPT-5.2 Max at 96.7.
Is Firesearch Nano good at MATH-500?
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 MATH-500 score calculated?
MATH-500 is a mathematics 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.