Knowledge · Google DeepMind

Gemini 3 Flash on MMLU-Pro: score, rank and what it means

Gemini 3 Flash scores 83.7 on MMLU-Pro, placing it #87 of 600 models in the index. The benchmark average is 73.5 and the leader is GPT-5.2 Pro at 91.6.

Score
83.7
0–100 scale
Rank
#87
of 600
Percentile
86th
within the index
Benchmark avg
73.5
all models
Leader
91.6
GPT-5.2 Pro
Gap to leader
7.9
points

Score distribution

The chart shows the top 10 models on MMLU-Pro, with Gemini 3 Flash highlighted. Longer bars mean higher scores.

GPT-5.2 Pro 91.6
GPT-5.1 Max 91.3
Claude Opus 4.5 Pro 91.2
Claude Opus 4.5 Reasoning 90.7
Gemini 3 Pro Reasoning 90.1
GPT-5.1 Reasoning 90.0
GPT-5.2 89.9
Claude Opus 4.5 Max 89.5
GPT-5.2 Vision 89.5
Gemini 3 Pro Max 89.5

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
2 GPT-5.1 Max OpenAI 2000000 $20.50 87.9
3 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
4 Claude Opus 4.5 Reasoning Anthropic 2000000 $20.91 88.2
5 Gemini 3 Pro Reasoning Google DeepMind 200000 $19.69 87.5
6 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
7 GPT-5.2 OpenAI 2000000 $19.71 88.0
8 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
9 GPT-5.2 Vision OpenAI 1000000 $19.03 87.4
10 Gemini 3 Pro Max Google DeepMind 2000000 $20.57 87.9

What this score means

On MMLU-Pro, Gemini 3 Flash sits at #87 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 7.9 points. The median model in this index scores 76.1, which puts Gemini 3 Flash above the midpoint.

Google DeepMind models average 80.4 on this benchmark. Gemini 3 Flash is 3.3 points above that provider line, so it is a stronger result than the average Google DeepMind model.

Among multimodal models, the average is 80.0. Gemini 3 Flash is 3.7 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 83.7 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 $15.67 per million input tokens, Gemini 3 Flash is above the median input price of $4.00 across the 600 models scored on MMLU-Pro. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Qwen3-Max Alibaba Cloud 400000 $17.37 84.4
Gemini 2.5 Pro Vision Google DeepMind 200000 $14.91 82.0
DeepSeek-R1 DeepSeek 128000 $5.83 82.8
ERNIE 4.5 Baidu 65536 $13.44 80.0
Gemini 3 Flash Google DeepMind 128000 $15.67 83.6
Grok 3 Turbo xAI 128000 $15.97 83.1
Qwen3-Coder Alibaba Cloud 200000 $4.94 79.5

Other benchmarks for Gemini 3 Flash

BenchmarkScoreRankPercentileRelative
GPQA Diamond 80.3 52 91th
SWE-bench Verified 72.5 66 89th
HumanEval 86.2 100 83th
MATH-500 89.0 59 90th
AIME 2025 84.5 44 93th
LiveCodeBench 79.3 51 92th
IFEval 91.0 54 91th
TruthfulQA 84.8 66 89th
HellaSwag 89.8 91 85th
MMMU 78.2 92 85th
Arena Elo 1450.7 71 88th

FAQ

What score does Gemini 3 Flash get on MMLU-Pro?

Gemini 3 Flash scores 83.7 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.

How does Gemini 3 Flash rank on MMLU-Pro?

It ranks #87 of 600 models, in the 86th percentile. The leader is GPT-5.2 Pro at 91.6.

Is Gemini 3 Flash good at MMLU-Pro?

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 MMLU-Pro score calculated?

MMLU-Pro is a knowledge 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 3 Flash cost?

Gemini 3 Flash costs $15.67 per million input tokens and $70.15 per million output tokens.

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