Knowledge · Cohere

Embed 4 Pro on MMLU-Pro: score, rank and what it means

Embed 4 Pro scores 69.3 on MMLU-Pro, placing it #404 of 600 models in the index. The benchmark average is 73.5 and the leader is GPT-5.2 Pro at 91.6.

Score
69.3
0–100 scale
Rank
#404
of 600
Percentile
33th
within the index
Benchmark avg
73.5
all models
Leader
91.6
GPT-5.2 Pro
Gap to leader
22.3
points

Score distribution

The chart shows the top 10 models on MMLU-Pro, with Embed 4 Pro 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, Embed 4 Pro sits at #404 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 22.3 points. The median model in this index scores 76.1, which puts Embed 4 Pro below the midpoint.

Cohere models average 72.3 on this benchmark. Embed 4 Pro is 3.0 points below that provider line, so it is a weaker result than the average Cohere model.

Among mid-range models, the average is 71.5. Embed 4 Pro is 2.2 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 69.3 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 $5.05 per million input tokens, Embed 4 Pro 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
Llama 4 Maverick 8B Meta AI 4096 $1.64 63.4
Arctic Base Snowflake 32768 $2.27 67.5
DeepSeek-R1 7B DeepSeek 32768 $1.79 63.0
Firefunction Mini Fireworks AI 32768 $4.63 63.2
Embed 4 Pro Cohere 4096 $5.05 63.5
SmolLM3 Hugging Face 8192 $1.71 63.1
GLM-4.6 8B Zhipu AI 8192 $1.69 63.1

Other benchmarks for Embed 4 Pro

BenchmarkScoreRankPercentileRelative
GPQA Diamond 61.3 397 34th
SWE-bench Verified 52.0 405 33th
HumanEval 68.0 414 31th
MATH-500 63.8 427 29th
AIME 2025 53.2 431 28th
LiveCodeBench 52.2 426 29th
IFEval 72.8 419 30th
TruthfulQA 66.8 433 28th
HellaSwag 78.6 397 34th
MMMU 60.5 423 30th
Arena Elo 1316.6 399 34th

FAQ

What score does Embed 4 Pro get on MMLU-Pro?

Embed 4 Pro scores 69.3 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.

How does Embed 4 Pro rank on MMLU-Pro?

It ranks #404 of 600 models, in the 33rd percentile. The leader is GPT-5.2 Pro at 91.6.

Is Embed 4 Pro 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 Embed 4 Pro cost?

Embed 4 Pro costs $5.05 per million input tokens and $19.40 per million output tokens.

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