Mathematics · Cohere

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

Embed 4 scores 66.0 on MATH-500, placing it #412 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.

Score
66.0
0–100 scale
Rank
#412
of 600
Percentile
31th
within the index
Benchmark avg
72.5
all models
Leader
96.7
GPT-5.2 Max
Gap to leader
30.7
points

Score distribution

The chart shows the top 10 models on MATH-500, with Embed 4 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 sits at #412 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 30.7 points. The median model in this index scores 76.0, which puts Embed 4 below the midpoint.

Cohere models average 70.8 on this benchmark. Embed 4 is 4.8 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 is 4.0 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 66.0 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 MATH-500. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Reka Flash Mini Reka AI 16384 $5.77 65.5
SmolLM3 Instruct Hugging Face 8192 $1.69 63.6
DeepSeek-V3.2 8B DeepSeek 32768 $2.02 66.2
Sonar Nano Perplexity 4096 $4.37 61.9
Embed 4 Cohere 32768 $4.79 63.8
Hunyuan-Turbo Nano Tencent 32768 $4.65 62.7
Llama 4 Maverick 7B Meta AI 4096 $1.52 61.9

Other benchmarks for Embed 4

BenchmarkScoreRankPercentileRelative
MMLU-Pro 67.9 419 30th
GPQA Diamond 61.6 393 35th
SWE-bench Verified 51.7 408 32th
HumanEval 69.3 406 32th
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 MATH-500?

Embed 4 scores 66.0 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.

How does Embed 4 rank on MATH-500?

It ranks #412 of 600 models, in the 31st percentile. The leader is GPT-5.2 Max at 96.7.

Is Embed 4 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 cost?

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

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