Multimodal · Cohere

Command R+ 7B on MMMU: score, rank and what it means

Command R+ 7B scores 53.3 on MMMU, placing it #516 of 600 models in the index. The benchmark average is 67.1 and the leader is GPT-5.2 Max at 86.9.

Score
53.3
0–100 scale
Rank
#516
of 600
Percentile
14th
within the index
Benchmark avg
67.1
all models
Leader
86.9
GPT-5.2 Max
Gap to leader
33.6
points

Score distribution

The chart shows the top 10 models on MMMU, with Command R+ 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, Command R+ 7B sits at #516 of 600. The leader, GPT-5.2 Max, scores 86.9, so the gap to the top is 33.6 points. The median model in this index scores 70.0, which puts Command R+ 7B below the midpoint.

Cohere models average 65.1 on this benchmark. Command R+ 7B is 11.8 points below that provider line, so it is a weaker result than the average Cohere model.

Among small open-weight models, the average is 52.4. Command R+ 7B is 0.9 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 53.3 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 $0.95 per million input tokens, Command R+ 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
Nemotron 4 7B NVIDIA 16384 $1.05 56.9
GLM-4.6 1.5B Zhipu AI 4096 $0.92 54.7
Mixtral Turbo 8B Together AI 4096 $0.75 54.1
MiniMax-Text 7B MiniMax 16384 $1.10 57.7
Command R+ 7B Cohere 8192 $0.95 56.2
DeepSeek-Coder 7B DeepSeek 32768 $1.14 57.3
Gemma 3 8B Google DeepMind 16384 $0.96 56.5

Other benchmarks for Command R+ 7B

BenchmarkScoreRankPercentileRelative
MMLU-Pro 61.5 507 16th
GPQA Diamond 49.7 528 12th
SWE-bench Verified 43.9 480 20th
HumanEval 58.6 515 14th
MATH-500 59.0 476 21th
AIME 2025 44.9 498 17th
LiveCodeBench 44.8 500 17th
IFEval 67.9 490 18th
TruthfulQA 63.7 474 21th
HellaSwag 71.1 517 14th
Arena Elo 1243.3 501 17th

FAQ

What score does Command R+ 7B get on MMMU?

Command R+ 7B scores 53.3 on MMMU in this index. The benchmark average is 67.1 and the top score is 86.9.

How does Command R+ 7B rank on MMMU?

It ranks #516 of 600 models, in the 14th percentile. The leader is GPT-5.2 Max at 86.9.

Is Command R+ 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 Command R+ 7B cost?

Command R+ 7B costs $0.95 per million input tokens and $4.04 per million output tokens.

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