Knowledge · Cohere

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

Command R+ Chat scores 80.9 on MMLU-Pro, placing it #149 of 600 models in the index. The benchmark average is 73.5 and the leader is GPT-5.2 Pro at 91.6.

Score
80.9
0–100 scale
Rank
#149
of 600
Percentile
75th
within the index
Benchmark avg
73.5
all models
Leader
91.6
GPT-5.2 Pro
Gap to leader
10.7
points

Score distribution

The chart shows the top 10 models on MMLU-Pro, with Command R+ Chat 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+ Chat sits at #149 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 10.7 points. The median model in this index scores 76.1, which puts Command R+ Chat above the midpoint.

Cohere models average 72.3 on this benchmark. Command R+ Chat is 8.6 points above that provider line, so it is a stronger result than the average Cohere model.

Among multimodal models, the average is 80.0. Command R+ Chat 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 80.9 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 $4.02 per million input tokens, Command R+ Chat 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
Doubao 1.5 Pro Max ByteDance 400000 $14.93 81.2
Sonar Pro Perplexity 65536 $11.25 77.1
DeepSeek-R1 Base DeepSeek 400000 $5.05 80.1
Nova Pro Pro Amazon 200000 $13.03 79.5
Command R+ Chat Cohere 32768 $4.02 76.0
Hunyuan-Turbo Pro Tencent 65536 $12.00 77.6
Qwen3-Max Mini Alibaba Cloud 128000 $11.13 76.5

Other benchmarks for Command R+ Chat

BenchmarkScoreRankPercentileRelative
GPQA Diamond 70.0 261 57th
SWE-bench Verified 66.7 175 71th
HumanEval 82.5 172 71th
MATH-500 76.7 287 52th
AIME 2025 72.3 210 65th
LiveCodeBench 71.1 167 72th
IFEval 82.0 277 54th
TruthfulQA 79.5 200 67th
HellaSwag 84.9 249 59th
MMMU 69.8 309 49th
Arena Elo 1401.9 204 66th

FAQ

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

Command R+ Chat scores 80.9 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.

How does Command R+ Chat rank on MMLU-Pro?

It ranks #149 of 600 models, in the 75th percentile. The leader is GPT-5.2 Pro at 91.6.

Is Command R+ Chat 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 Command R+ Chat cost?

Command R+ Chat costs $4.02 per million input tokens and $15.33 per million output tokens.

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