Mathematics · Fireworks AI

Firefunction Reasoning on MATH-500: score, rank and what it means

Firefunction Reasoning scores 76.5 on MATH-500, placing it #290 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.

Score
76.5
0–100 scale
Rank
#290
of 600
Percentile
52th
within the index
Benchmark avg
72.5
all models
Leader
96.7
GPT-5.2 Max
Gap to leader
20.2
points

Score distribution

The chart shows the top 10 models on MATH-500, with Firefunction Reasoning highlighted. Longer bars mean higher scores.

GPT-5.2 Max 96.7
GPT-5.1 Reasoning 95.9
GPT-5.1 Pro 95.1
o4 Reasoning 94.8
Grok 4 Max 94.6
Grok 4 Reasoning 94.4
Claude Opus 4.5 Max 94.2
Claude Opus 4.5 93.7
Gemini 3 Pro Reasoning 93.3
Claude Opus 4.5 Turbo 93.2

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg 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, Firefunction Reasoning sits at #290 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 20.2 points. The median model in this index scores 76.0, which puts Firefunction Reasoning above the midpoint.

Fireworks AI models average 69.4 on this benchmark. Firefunction Reasoning is 7.1 points above that provider line, so it is a stronger result than the average Fireworks AI model.

Among mid-range models, the average is 70.0. Firefunction Reasoning is 6.5 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 76.5 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 MATH-500. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Nemotron 4 Base NVIDIA 8192 $3.29 73.0
Command R+ Chat Cohere 32768 $4.02 76.0
Firefunction Fireworks AI 16384 $8.16 72.3
Llama 3 Lambda Base Lambda 8192 $3.19 72.9
Firefunction Reasoning Fireworks AI 8192 $9.08 72.5
DBRX Databricks 65536 $3.29 73.3
Llama 3 Hyperbolic Hyperbolic 65536 $3.86 74.5

Other benchmarks for Firefunction Reasoning

BenchmarkScoreRankPercentileRelative
MMLU-Pro 73.7 359 40th
GPQA Diamond 68.2 309 49th
SWE-bench Verified 58.2 351 42th
HumanEval 77.2 306 49th
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 MATH-500?

Firefunction Reasoning scores 76.5 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.

How does Firefunction Reasoning rank on MATH-500?

It ranks #290 of 600 models, in the 52nd percentile. The leader is GPT-5.2 Max at 96.7.

Is Firefunction Reasoning good at MATH-500?

Yes, relative to the index: it is above the median and inside the top half of scores. 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 Firefunction Reasoning cost?

Firefunction Reasoning costs $9.08 per million input tokens and $42.16 per million output tokens.

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