Reasoning · Fireworks AI

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

Firesearch Nano scores 52.6 on GPQA Diamond, placing it #493 of 600 models in the index. The benchmark average is 65.6 and the leader is GPT-5.2 Reasoning at 87.0.

Score
52.6
0–100 scale
Rank
#493
of 600
Percentile
18th
within the index
Benchmark avg
65.6
all models
Leader
87.0
GPT-5.2 Reasoning
Gap to leader
34.4
points

Score distribution

The chart shows the top 10 models on GPQA Diamond, with Firesearch 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, Firesearch Nano sits at #493 of 600. The leader, GPT-5.2 Reasoning, scores 87.0, so the gap to the top is 34.4 points. The median model in this index scores 68.8, which puts Firesearch Nano below the midpoint.

Fireworks AI models average 63.3 on this benchmark. Firesearch Nano is 10.7 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. Firesearch Nano is 10.3 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 52.6 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 GPQA Diamond. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Phi-4 8B Microsoft 32768 $0.96 54.9
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

Other benchmarks for Firesearch Nano

BenchmarkScoreRankPercentileRelative
MMLU-Pro 63.0 488 19th
SWE-bench Verified 40.0 521 13th
HumanEval 58.9 513 15th
MATH-500 52.9 533 11th
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 GPQA Diamond?

Firesearch Nano scores 52.6 on GPQA Diamond in this index. The benchmark average is 65.6 and the top score is 87.0.

How does Firesearch Nano rank on GPQA Diamond?

It ranks #493 of 600 models, in the 18th percentile. The leader is GPT-5.2 Reasoning at 87.0.

Is Firesearch 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 Firesearch Nano cost?

Firesearch Nano costs $2.49 per million input tokens and $11.16 per million output tokens.

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