Multimodal · Cohere

Embed 4 Mini on MMMU: score, rank and what it means

Embed 4 Mini scores 51.8 on MMMU, placing it #533 of 600 models in the index. The benchmark average is 67.1 and the leader is GPT-5.2 Max at 86.9.

Score
51.8
0–100 scale
Rank
#533
of 600
Percentile
11th
within the index
Benchmark avg
67.1
all models
Leader
86.9
GPT-5.2 Max
Gap to leader
35.1
points

Score distribution

The chart shows the top 10 models on MMMU, with Embed 4 Mini 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, Embed 4 Mini sits at #533 of 600. The leader, GPT-5.2 Max, scores 86.9, so the gap to the top is 35.1 points. The median model in this index scores 70.0, which puts Embed 4 Mini below the midpoint.

Cohere models average 65.1 on this benchmark. Embed 4 Mini is 13.3 points below that provider line, so it is a weaker result than the average Cohere model.

Among mid-range models, the average is 65.0. Embed 4 Mini is 13.2 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 51.8 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 $2.48 per million input tokens, Embed 4 Mini 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
Mixtral 8B Mistral AI 8192 $0.91 55.4
Qwen3 1.5B Alibaba Cloud 4096 $0.84 53.5
Kimi K1.5 1.5B Moonshot AI 4096 $0.75 53.2
DeepSeek-Coder 1.5B DeepSeek 16384 $0.58 51.0
Embed 4 Mini Cohere 4096 $2.48 55.8
Seed-OSS 7B ByteDance 8192 $0.85 55.5
Command R+ 1.5B Cohere 32768 $0.51 50.0

Other benchmarks for Embed 4 Mini

BenchmarkScoreRankPercentileRelative
MMLU-Pro 63.2 486 19th
GPQA Diamond 52.3 496 17th
SWE-bench Verified 43.4 485 19th
HumanEval 60.4 497 17th
MATH-500 54.6 523 13th
AIME 2025 43.9 508 15th
LiveCodeBench 42.9 517 14th
IFEval 67.4 495 18th
TruthfulQA 60.5 524 13th
HellaSwag 73.0 482 20th
Arena Elo 1233.2 524 13th

FAQ

What score does Embed 4 Mini get on MMMU?

Embed 4 Mini scores 51.8 on MMMU in this index. The benchmark average is 67.1 and the top score is 86.9.

How does Embed 4 Mini rank on MMMU?

It ranks #533 of 600 models, in the 11th percentile. The leader is GPT-5.2 Max at 86.9.

Is Embed 4 Mini 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 Embed 4 Mini cost?

Embed 4 Mini costs $2.48 per million input tokens and $9.98 per million output tokens.

Related