Knowledge · Google DeepMind

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

Gemma 3 scores 75.1 on MMLU-Pro, placing it #330 of 600 models in the index. The benchmark average is 73.5 and the leader is GPT-5.2 Pro at 91.6.

Score
75.1
0–100 scale
Rank
#330
of 600
Percentile
45th
within the index
Benchmark avg
73.5
all models
Leader
91.6
GPT-5.2 Pro
Gap to leader
16.5
points

Score distribution

The chart shows the top 10 models on MMLU-Pro, with Gemma 3 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, Gemma 3 sits at #330 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 16.5 points. The median model in this index scores 76.1, which puts Gemma 3 below the midpoint.

Google DeepMind models average 80.4 on this benchmark. Gemma 3 is 5.3 points below that provider line, so it is a weaker result than the average Google DeepMind model.

Among multimodal models, the average is 80.0. Gemma 3 is 4.9 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 75.1 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 $3.68 per million input tokens, Gemma 3 is below 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
DBRX Chat Databricks 65536 $3.07 71.7
Gemma 3 Base Google DeepMind 65536 $3.09 71.1
Llama 3.1 Base Meta AI 32768 $3.39 72.2
Fusion Max OpenRouter 65536 $9.14 72.5
Gemma 3 Google DeepMind 65536 $3.68 73.6
Mixtral Groq Groq 16384 $3.37 73.1
Claude 3.7 Sonnet Mini Anthropic 32768 $9.74 73.0

Other benchmarks for Gemma 3

BenchmarkScoreRankPercentileRelative
GPQA Diamond 71.5 230 62th
SWE-bench Verified 60.0 328 45th
HumanEval 76.6 315 48th
MATH-500 75.7 309 49th
AIME 2025 69.2 274 54th
LiveCodeBench 65.4 285 53th
IFEval 82.0 279 54th
TruthfulQA 78.6 232 61th
HellaSwag 83.7 290 52th
MMMU 72.1 239 60th
Arena Elo 1371.5 312 48th

FAQ

What score does Gemma 3 get on MMLU-Pro?

Gemma 3 scores 75.1 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.

How does Gemma 3 rank on MMLU-Pro?

It ranks #330 of 600 models, in the 45th percentile. The leader is GPT-5.2 Pro at 91.6.

Is Gemma 3 good at MMLU-Pro?

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 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 Gemma 3 cost?

Gemma 3 costs $3.68 per million input tokens and $14.67 per million output tokens.

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