Multimodal · Google DeepMind

Gemma 3 7B on MMMU: score, rank and what it means

Gemma 3 7B scores 56.8 on MMMU, placing it #474 of 600 models in the index. The benchmark average is 67.1 and the leader is GPT-5.2 Max at 86.9.

Score
56.8
0–100 scale
Rank
#474
of 600
Percentile
21th
within the index
Benchmark avg
67.1
all models
Leader
86.9
GPT-5.2 Max
Gap to leader
30.1
points

Score distribution

The chart shows the top 10 models on MMMU, with Gemma 3 7B highlighted. Longer bars mean higher scores.

GPT-5.2 Max 86.9
GPT-5.1 Reasoning 86.2
GPT-5.2 85.3
Claude Sonnet 4.5 Reasoning 85.2
GPT-5.1 Max 85.1
Grok 4 Pro 84.9
Claude Opus 4.5 Reasoning 84.6
Claude Sonnet 4.5 Pro 84.5
GPT-5.2 Pro 84.4
GPT-5.2 Vision 84.4

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
2 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
3 GPT-5.2 OpenAI 2000000 $19.71 88.0
4 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
5 GPT-5.1 Max OpenAI 2000000 $20.50 87.9
6 Grok 4 Pro xAI 1000000 $19.00 85.4
7 Claude Opus 4.5 Reasoning Anthropic 2000000 $20.91 88.2
8 Claude Sonnet 4.5 Pro Anthropic 1000000 $18.73 86.7
9 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
10 GPT-5.2 Vision OpenAI 1000000 $19.03 87.4

What this score means

On MMMU, Gemma 3 7B sits at #474 of 600. The leader, GPT-5.2 Max, scores 86.9, so the gap to the top is 30.1 points. The median model in this index scores 70.0, which puts Gemma 3 7B below the midpoint.

Google DeepMind models average 74.9 on this benchmark. Gemma 3 7B is 18.1 points below that provider line, so it is a weaker result than the average Google DeepMind model.

Among small open-weight models, the average is 52.4. Gemma 3 7B is 4.4 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 56.8 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 $0.90 per million input tokens, Gemma 3 7B is below the median input price of $4.00 across the 600 models scored on MMMU. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Llama 3 Modal 8B Modal 32768 $0.98 56.6
Yi-Lightning 7B 01.AI 32768 $1.00 55.9
Zephyr Chat Hugging Face 4096 $1.49 61.3
GPT-NeoX Base EleutherAI 16384 $1.22 59.7
Gemma 3 7B Google DeepMind 32768 $0.90 55.9
Llama 3 Anyscale 8B Anyscale 32768 $0.97 57.3
Llama 3.1 Turbo 7B Together AI 8192 $0.98 56.4

Other benchmarks for Gemma 3 7B

BenchmarkScoreRankPercentileRelative
MMLU-Pro 60.8 514 14th
GPQA Diamond 54.0 482 20th
SWE-bench Verified 38.8 529 12th
HumanEval 61.2 482 20th
MATH-500 56.2 505 16th
AIME 2025 47.7 471 22th
LiveCodeBench 42.8 519 14th
IFEval 65.6 527 12th
TruthfulQA 59.3 538 10th
HellaSwag 72.2 491 18th
Arena Elo 1236.8 519 14th

FAQ

What score does Gemma 3 7B get on MMMU?

Gemma 3 7B scores 56.8 on MMMU in this index. The benchmark average is 67.1 and the top score is 86.9.

How does Gemma 3 7B rank on MMMU?

It ranks #474 of 600 models, in the 21st percentile. The leader is GPT-5.2 Max at 86.9.

Is Gemma 3 7B good at MMMU?

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 MMMU score calculated?

MMMU is a multimodal 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 7B cost?

Gemma 3 7B costs $0.90 per million input tokens and $4.55 per million output tokens.

Related