Commonsense · Cohere

Embed 4 on HellaSwag: score, rank and what it means

Embed 4 scores 75.3 on HellaSwag, placing it #436 of 600 models in the index. The benchmark average is 81.4 and the leader is GPT-5.2 Max at 96.1.

Score
75.3
0–100 scale
Rank
#436
of 600
Percentile
27th
within the index
Benchmark avg
81.4
all models
Leader
96.1
GPT-5.2 Max
Gap to leader
20.8
points

Score distribution

The chart shows the top 10 models on HellaSwag, with Embed 4 highlighted. Longer bars mean higher scores.

GPT-5.2 Max 96.1
GPT-5.2 Turbo 95.5
GPT-5.2 Pro 95.3
Claude Opus 4.5 Pro 95.2
Claude Sonnet 4.5 Reasoning 95.2
Grok 4 Reasoning 95.2
GPT-5.2 Vision 94.6
Gemini 3 Pro Turbo 94.6
GPT-5.1 Reasoning 94.4
o4 Pro 94.4

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
2 GPT-5.2 Turbo OpenAI 1000000 $20.73 87.4
3 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
4 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
5 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
6 Grok 4 Reasoning xAI 2000000 $19.67 87.8
7 GPT-5.2 Vision OpenAI 1000000 $19.03 87.4
8 Gemini 3 Pro Turbo Google DeepMind 400000 $19.62 86.4
9 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
10 o4 Pro OpenAI 1000000 $19.37 87.3

What this score means

On HellaSwag, Embed 4 sits at #436 of 600. The leader, GPT-5.2 Max, scores 96.1, so the gap to the top is 20.8 points. The median model in this index scores 83.6, which puts Embed 4 below the midpoint.

Cohere models average 79.9 on this benchmark. Embed 4 is 4.6 points below that provider line, so it is a weaker result than the average Cohere model.

Among mid-range models, the average is 79.8. Embed 4 is 4.5 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 75.3 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 $4.79 per million input tokens, Embed 4 is above the median input price of $4.00 across the 600 models scored on HellaSwag. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Mistral Large 3 7B Mistral AI 32768 $1.44 61.1
Qwen3 8B Alibaba Cloud 8192 $1.66 61.9
GLM-4.6 7B Zhipu AI 16384 $1.52 61.8
GPT-NeoX Instruct EleutherAI 16384 $1.52 62.6
Embed 4 Cohere 32768 $4.79 63.8
Llama 3.3 Groq 8B Groq 16384 $1.17 59.5
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
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
MMMU 58.2 455 24th
Arena Elo 1286.4 437 27th

FAQ

What score does Embed 4 get on HellaSwag?

Embed 4 scores 75.3 on HellaSwag in this index. The benchmark average is 81.4 and the top score is 96.1.

How does Embed 4 rank on HellaSwag?

It ranks #436 of 600 models, in the 27th percentile. The leader is GPT-5.2 Max at 96.1.

Is Embed 4 good at HellaSwag?

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 HellaSwag score calculated?

HellaSwag is a commonsense 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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