Reasoning · Google DeepMind

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

Gemini 2.5 Pro Vision scores 78.0 on GPQA Diamond, placing it #86 of 600 models in the index. The benchmark average is 65.6 and the leader is GPT-5.2 Reasoning at 87.0.

Score
78.0
0–100 scale
Rank
#86
of 600
Percentile
86th
within the index
Benchmark avg
65.6
all models
Leader
87.0
GPT-5.2 Reasoning
Gap to leader
9.0
points

Score distribution

The chart shows the top 10 models on GPQA Diamond, with Gemini 2.5 Pro Vision 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 Vision sits at #86 of 600. The leader, GPT-5.2 Reasoning, scores 87.0, so the gap to the top is 9.0 points. The median model in this index scores 68.8, which puts Gemini 2.5 Pro Vision above the midpoint.

Google DeepMind models average 73.4 on this benchmark. Gemini 2.5 Pro Vision is 4.6 points above that provider line, so it is a stronger result than the average Google DeepMind model.

Among multimodal models, the average is 73.5. Gemini 2.5 Pro Vision is 4.5 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 78.0 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 $14.91 per million input tokens, Gemini 2.5 Pro Vision 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
Doubao 1.5 Pro Reasoning ByteDance 128000 $14.56 81.6
Kimi K1.5 Chat Moonshot AI 128000 $5.11 80.2
Command A Turbo Cohere 32768 $13.53 79.8
DeepSeek-V3.2 Chat DeepSeek 1000000 $5.80 82.5
Gemini 2.5 Pro Vision Google DeepMind 200000 $14.91 82.0
Reka Core Pro Reka AI 65536 $13.44 79.9
Qwen3-Coder Alibaba Cloud 200000 $4.94 79.5

Other benchmarks for Gemini 2.5 Pro Vision

BenchmarkScoreRankPercentileRelative
MMLU-Pro 83.9 84 86th
SWE-bench Verified 72.4 71 88th
HumanEval 85.8 102 83th
MATH-500 82.9 147 76th
AIME 2025 80.0 88 85th
LiveCodeBench 76.0 83 86th
IFEval 90.2 66 89th
TruthfulQA 84.1 82 86th
HellaSwag 90.8 66 89th
MMMU 78.2 91 85th
Arena Elo 1451.3 70 88th

FAQ

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

Gemini 2.5 Pro Vision scores 78.0 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 Vision rank on GPQA Diamond?

It ranks #86 of 600 models, in the 86th percentile. The leader is GPT-5.2 Reasoning at 87.0.

Is Gemini 2.5 Pro Vision good at GPQA Diamond?

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 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 Vision cost?

Gemini 2.5 Pro Vision costs $14.91 per million input tokens and $71.00 per million output tokens.

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