Reasoning · Fireworks AI

Firefunction Nano on GPQA Diamond: score, rank and what it means

Firefunction Nano scores 52.5 on GPQA Diamond, placing it #494 of 600 models in the index. The benchmark average is 65.6 and the leader is GPT-5.2 Reasoning at 87.0.

Score
52.5
0–100 scale
Rank
#494
of 600
Percentile
18th
within the index
Benchmark avg
65.6
all models
Leader
87.0
GPT-5.2 Reasoning
Gap to leader
34.5
points

Score distribution

The chart shows the top 10 models on GPQA Diamond, with Firefunction Nano highlighted. Longer bars mean higher scores.

GPT-5.2 Reasoning 87.0
GPT-5.1 Max 86.4
Claude Opus 4.5 Pro 86.0
GPT-5.1 Reasoning 85.6
Claude Sonnet 4.5 Reasoning 85.2
GPT-5.2 84.9
o4 Turbo 84.8
Claude Opus 4.5 Max 84.6
Grok 4 Turbo 84.5
Claude Sonnet 4.5 Max 84.4

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Reasoning OpenAI 1000000 $20.98 87.5
2 GPT-5.1 Max OpenAI 2000000 $20.50 87.9
3 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
4 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
5 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
6 GPT-5.2 OpenAI 2000000 $19.71 88.0
7 o4 Turbo OpenAI 2000000 $19.34 86.6
8 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
9 Grok 4 Turbo xAI 1000000 $19.21 86.7
10 Claude Sonnet 4.5 Max Anthropic 1000000 $19.90 87.2

What this score means

On GPQA Diamond, Firefunction Nano sits at #494 of 600. The leader, GPT-5.2 Reasoning, scores 87.0, so the gap to the top is 34.5 points. The median model in this index scores 68.8, which puts Firefunction Nano below the midpoint.

Fireworks AI models average 63.3 on this benchmark. Firefunction Nano is 10.8 points below that provider line, so it is a weaker result than the average Fireworks AI model.

Among mid-range models, the average is 62.9. Firefunction Nano is 10.4 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 52.5 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 $3.07 per million input tokens, Firefunction Nano is below the median input price of $4.00 across the 600 models scored on GPQA Diamond. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Magistral 8B Mistral AI 4096 $1.33 60.0
Llama 3 DeepInfra 8B DeepInfra 8192 $1.05 57.4
Phi-4 7B Microsoft 8192 $0.91 56.8
Firesearch Nano Fireworks AI 8192 $2.49 55.6
Firefunction Nano Fireworks AI 16384 $3.07 58.1
Llama 3 Lambda 7B Lambda 8192 $0.84 55.4
Embed 4 Mini Cohere 4096 $2.48 55.8

Other benchmarks for Firefunction Nano

BenchmarkScoreRankPercentileRelative
MMLU-Pro 64.0 470 22th
SWE-bench Verified 42.3 499 17th
HumanEval 62.4 464 23th
MATH-500 61.3 450 25th
AIME 2025 45.3 496 17th
LiveCodeBench 46.9 476 21th
IFEval 69.4 469 22th
TruthfulQA 65.5 453 25th
HellaSwag 73.8 458 24th
MMMU 56.1 486 19th
Arena Elo 1265.6 467 22th

FAQ

What score does Firefunction Nano get on GPQA Diamond?

Firefunction Nano scores 52.5 on GPQA Diamond in this index. The benchmark average is 65.6 and the top score is 87.0.

How does Firefunction Nano rank on GPQA Diamond?

It ranks #494 of 600 models, in the 18th percentile. The leader is GPT-5.2 Reasoning at 87.0.

Is Firefunction Nano good at GPQA Diamond?

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 GPQA Diamond score calculated?

GPQA Diamond is a reasoning 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 Nano cost?

Firefunction Nano costs $3.07 per million input tokens and $12.50 per million output tokens.

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