Mathematics · Cohere

Embed 4 Nano on MATH-500: score, rank and what it means

Embed 4 Nano scores 52.4 on MATH-500, placing it #537 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.

Score
52.4
0–100 scale
Rank
#537
of 600
Percentile
11th
within the index
Benchmark avg
72.5
all models
Leader
96.7
GPT-5.2 Max
Gap to leader
44.3
points

Score distribution

The chart shows the top 10 models on MATH-500, with Embed 4 Nano highlighted. Longer bars mean higher scores.

GPT-5.2 Max 96.7
GPT-5.1 Reasoning 95.9
GPT-5.1 Pro 95.1
o4 Reasoning 94.8
Grok 4 Max 94.6
Grok 4 Reasoning 94.4
Claude Opus 4.5 Max 94.2
Claude Opus 4.5 93.7
Gemini 3 Pro Reasoning 93.3
Claude Opus 4.5 Turbo 93.2

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-5.1 Pro OpenAI 200000 $19.77 87.8
4 o4 Reasoning OpenAI 1000000 $19.69 87.5
5 Grok 4 Max xAI 2000000 $19.70 86.6
6 Grok 4 Reasoning xAI 2000000 $19.67 87.8
7 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
8 Claude Opus 4.5 Anthropic 400000 $19.42 87.7
9 Gemini 3 Pro Reasoning Google DeepMind 200000 $19.69 87.5
10 Claude Opus 4.5 Turbo Anthropic 2000000 $19.57 85.7

What this score means

On MATH-500, Embed 4 Nano sits at #537 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 44.3 points. The median model in this index scores 76.0, which puts Embed 4 Nano below the midpoint.

Cohere models average 70.8 on this benchmark. Embed 4 Nano is 18.4 points below that provider line, so it is a weaker result than the average Cohere model.

Among mid-range models, the average is 70.0. Embed 4 Nano is 17.6 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 52.4 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 MATH-500. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Firesearch Nano Fireworks AI 8192 $2.49 55.6
Magistral 1.5B Mistral AI 4096 $0.67 52.7
Arctic 8B Snowflake 8192 $0.65 52.4
Mixtral Turbo 8B Together AI 4096 $0.75 54.1
Embed 4 Nano Cohere 8192 $1.48 49.7
Phi-3.5 8B Microsoft 16384 $0.59 51.2
Command R+ 1.5B Cohere 32768 $0.51 50.0

Other benchmarks for Embed 4 Nano

BenchmarkScoreRankPercentileRelative
MMLU-Pro 54.5 575 4th
GPQA Diamond 45.6 560 7th
SWE-bench Verified 33.1 571 5th
HumanEval 55.2 540 10th
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 MATH-500?

Embed 4 Nano scores 52.4 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.

How does Embed 4 Nano rank on MATH-500?

It ranks #537 of 600 models, in the 11th percentile. The leader is GPT-5.2 Max at 96.7.

Is Embed 4 Nano good at MATH-500?

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 MATH-500 score calculated?

MATH-500 is a mathematics 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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