Knowledge · Cohere

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

Embed 4 Nano scores 54.5 on MMLU-Pro, placing it #575 of 600 models in the index. The benchmark average is 73.5 and the leader is GPT-5.2 Pro at 91.6.

Score
54.5
0–100 scale
Rank
#575
of 600
Percentile
4th
within the index
Benchmark avg
73.5
all models
Leader
91.6
GPT-5.2 Pro
Gap to leader
37.1
points

Score distribution

The chart shows the top 10 models on MMLU-Pro, with Embed 4 Nano 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 Nano sits at #575 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 37.1 points. The median model in this index scores 76.1, which puts Embed 4 Nano below the midpoint.

Cohere models average 72.3 on this benchmark. Embed 4 Nano is 17.8 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 Nano is 17.0 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 54.5 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 $1.48 per million input tokens, Embed 4 Nano 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
Mixtral Groq 1.5B Groq 4096 $0.34 46.1
Mixtral Turbo 1.5B Together AI 4096 $0.31 47.6
Phi-4 1.5B Microsoft 8192 $0.42 48.1
StableLM 2 8B Stability AI 32768 $0.38 47.0
Embed 4 Nano Cohere 8192 $1.48 49.7
Gemma 3 1.5B Google DeepMind 32768 $0.40 48.9
SmolLM3 7B Hugging Face 16384 $0.19 44.3

Other benchmarks for Embed 4 Nano

BenchmarkScoreRankPercentileRelative
GPQA Diamond 45.6 560 7th
SWE-bench Verified 33.1 571 5th
HumanEval 55.2 540 10th
MATH-500 52.4 537 11th
AIME 2025 33.5 575 4th
LiveCodeBench 40.3 535 11th
IFEval 61.0 564 6th
TruthfulQA 55.6 570 5th
HellaSwag 69.8 537 11th
MMMU 46.2 578 4th
Arena Elo 1207.9 547 9th

FAQ

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

Embed 4 Nano scores 54.5 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.

How does Embed 4 Nano rank on MMLU-Pro?

It ranks #575 of 600 models, in the 4th percentile. The leader is GPT-5.2 Pro at 91.6.

Is Embed 4 Nano 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 Nano cost?

Embed 4 Nano costs $1.48 per million input tokens and $6.14 per million output tokens.

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