Knowledge · Cohere

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

Embed 4 scores 67.9 on MMLU-Pro, placing it #419 of 600 models in the index. The benchmark average is 73.5 and the leader is GPT-5.2 Pro at 91.6.

Score
67.9
0–100 scale
Rank
#419
of 600
Percentile
30th
within the index
Benchmark avg
73.5
all models
Leader
91.6
GPT-5.2 Pro
Gap to leader
23.7
points

Score distribution

The chart shows the top 10 models on MMLU-Pro, with Embed 4 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 sits at #419 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 23.7 points. The median model in this index scores 76.1, which puts Embed 4 below the midpoint.

Cohere models average 72.3 on this benchmark. Embed 4 is 4.4 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 is 3.6 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 67.9 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 $4.79 per million input tokens, Embed 4 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
Kimi K1.5 8B Moonshot AI 8192 $1.54 62.2
Nova Lite Mini Amazon 16384 $5.43 65.0
Reka Flash Mini Reka AI 16384 $5.77 65.5
Nemotron Nano Base NVIDIA 8192 $1.85 65.0
Embed 4 Cohere 32768 $4.79 63.8
GPT-NeoX Instruct EleutherAI 16384 $1.52 62.6
StableLM 2 Base Stability AI 8192 $1.49 61.9

Other benchmarks for Embed 4

BenchmarkScoreRankPercentileRelative
GPQA Diamond 61.6 393 35th
SWE-bench Verified 51.7 408 32th
HumanEval 69.3 406 32th
MATH-500 66.0 412 31th
AIME 2025 54.1 422 30th
LiveCodeBench 54.3 411 32th
IFEval 73.5 410 32th
TruthfulQA 69.9 401 33th
HellaSwag 75.3 436 27th
MMMU 58.2 455 24th
Arena Elo 1286.4 437 27th

FAQ

What score does Embed 4 get on MMLU-Pro?

Embed 4 scores 67.9 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.

How does Embed 4 rank on MMLU-Pro?

It ranks #419 of 600 models, in the 30th percentile. The leader is GPT-5.2 Pro at 91.6.

Is Embed 4 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 cost?

Embed 4 costs $4.79 per million input tokens and $20.86 per million output tokens.

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