Reasoning · Google DeepMind

Gemini 2.5 Pro Nano on GPQA Diamond: score, rank and what it means

Gemini 2.5 Pro Nano scores 65.4 on GPQA Diamond, placing it #353 of 600 models in the index. The benchmark average is 65.6 and the leader is GPT-5.2 Reasoning at 87.0.

Score
65.4
0–100 scale
Rank
#353
of 600
Percentile
41th
within the index
Benchmark avg
65.6
all models
Leader
87.0
GPT-5.2 Reasoning
Gap to leader
21.6
points

Score distribution

The chart shows the top 10 models on GPQA Diamond, with Gemini 2.5 Pro 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, Gemini 2.5 Pro Nano sits at #353 of 600. The leader, GPT-5.2 Reasoning, scores 87.0, so the gap to the top is 21.6 points. The median model in this index scores 68.8, which puts Gemini 2.5 Pro Nano below the midpoint.

Google DeepMind models average 73.4 on this benchmark. Gemini 2.5 Pro Nano is 8.0 points below that provider line, so it is a weaker result than the average Google DeepMind model.

Among mid-range models, the average is 62.9. Gemini 2.5 Pro Nano is 2.5 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 65.4 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 $7.28 per million input tokens, Gemini 2.5 Pro Nano is above 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
Arctic Chat Snowflake 16384 $2.70 69.8
Llama 3.1 Turbo Base Together AI 16384 $3.30 73.0
Llama 3.3 Groq Base Groq 16384 $3.24 73.5
Phi-3.5 Instruct Microsoft 16384 $2.67 70.1
Gemini 2.5 Pro Nano Google DeepMind 32768 $7.28 69.2
Arctic Snowflake 8192 $2.72 68.8
Molmo Allen Institute for AI 32768 $2.49 68.5

Other benchmarks for Gemini 2.5 Pro Nano

BenchmarkScoreRankPercentileRelative
MMLU-Pro 71.4 385 36th
SWE-bench Verified 54.3 390 35th
HumanEval 71.1 387 36th
MATH-500 74.9 327 46th
AIME 2025 63.7 360 40th
LiveCodeBench 59.2 376 37th
IFEval 79.9 334 44th
TruthfulQA 74.4 343 43th
HellaSwag 80.7 375 38th
MMMU 65.9 374 38th
Arena Elo 1349.7 364 39th

FAQ

What score does Gemini 2.5 Pro Nano get on GPQA Diamond?

Gemini 2.5 Pro Nano scores 65.4 on GPQA Diamond in this index. The benchmark average is 65.6 and the top score is 87.0.

How does Gemini 2.5 Pro Nano rank on GPQA Diamond?

It ranks #353 of 600 models, in the 41st percentile. The leader is GPT-5.2 Reasoning at 87.0.

Is Gemini 2.5 Pro 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 Gemini 2.5 Pro Nano cost?

Gemini 2.5 Pro Nano costs $7.28 per million input tokens and $28.44 per million output tokens.

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