Knowledge · Cohere

Command R+ 7B on MMLU-Pro: score, rank and what it means

Command R+ 7B scores 61.5 on MMLU-Pro, placing it #507 of 600 models in the index. The benchmark average is 73.5 and the leader is GPT-5.2 Pro at 91.6.

Score
61.5
0–100 scale
Rank
#507
of 600
Percentile
16th
within the index
Benchmark avg
73.5
all models
Leader
91.6
GPT-5.2 Pro
Gap to leader
30.1
points

Score distribution

The chart shows the top 10 models on MMLU-Pro, with Command R+ 7B 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, Command R+ 7B sits at #507 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 30.1 points. The median model in this index scores 76.1, which puts Command R+ 7B below the midpoint.

Cohere models average 72.3 on this benchmark. Command R+ 7B is 10.8 points below that provider line, so it is a weaker result than the average Cohere model.

Among small open-weight models, the average is 59.8. Command R+ 7B is 1.7 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 61.5 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 MMLU-Pro. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Yi-Lightning 7B 01.AI 32768 $1.00 55.9
Phi-4 8B Microsoft 32768 $0.96 54.9
Mixtral 7B Mistral AI 16384 $0.70 54.1
DeepSeek-Coder 7B DeepSeek 32768 $1.14 57.3
Command R+ 7B Cohere 8192 $0.95 56.2
Kimi K1.5 1.5B Moonshot AI 4096 $0.75 53.2
Llama 3 Novita 8B Novita AI 16384 $0.99 55.5

Other benchmarks for Command R+ 7B

BenchmarkScoreRankPercentileRelative
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
MMMU 53.3 516 14th
Arena Elo 1243.3 501 17th

FAQ

What score does Command R+ 7B get on MMLU-Pro?

Command R+ 7B scores 61.5 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.

How does Command R+ 7B rank on MMLU-Pro?

It ranks #507 of 600 models, in the 16th percentile. The leader is GPT-5.2 Pro at 91.6.

Is Command R+ 7B 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 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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