Commonsense · Cohere

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

Embed 4 Turbo scores 77.1 on HellaSwag, placing it #415 of 600 models in the index. The benchmark average is 81.4 and the leader is GPT-5.2 Max at 96.1.

Score
77.1
0–100 scale
Rank
#415
of 600
Percentile
31th
within the index
Benchmark avg
81.4
all models
Leader
96.1
GPT-5.2 Max
Gap to leader
19.0
points

Score distribution

The chart shows the top 10 models on HellaSwag, with Embed 4 Turbo 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 Turbo sits at #415 of 600. The leader, GPT-5.2 Max, scores 96.1, so the gap to the top is 19.0 points. The median model in this index scores 83.6, which puts Embed 4 Turbo below the midpoint.

Cohere models average 79.9 on this benchmark. Embed 4 Turbo is 2.8 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 Turbo is 2.7 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 77.1 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 $5.13 per million input tokens, Embed 4 Turbo 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
DeepSeek-R1 7B DeepSeek 32768 $1.79 63.0
Firesearch Mini Fireworks AI 32768 $3.94 61.5
StableLM 2 Stability AI 8192 $1.91 65.0
Command A Nano Cohere 8192 $5.90 66.3
Embed 4 Turbo Cohere 4096 $5.13 64.0
Qwen3 7B Alibaba Cloud 32768 $1.37 59.8
Embed 4 Max Cohere 8192 $5.68 66.1

Other benchmarks for Embed 4 Turbo

BenchmarkScoreRankPercentileRelative
MMLU-Pro 70.6 392 35th
GPQA Diamond 58.4 422 30th
SWE-bench Verified 50.7 415 31th
HumanEval 70.0 392 35th
MATH-500 68.7 391 35th
AIME 2025 53.1 432 28th
LiveCodeBench 55.0 405 33th
IFEval 73.4 411 32th
TruthfulQA 67.6 421 30th
MMMU 59.5 433 28th
Arena Elo 1324.1 394 34th

FAQ

What score does Embed 4 Turbo get on HellaSwag?

Embed 4 Turbo scores 77.1 on HellaSwag in this index. The benchmark average is 81.4 and the top score is 96.1.

How does Embed 4 Turbo rank on HellaSwag?

It ranks #415 of 600 models, in the 31st percentile. The leader is GPT-5.2 Max at 96.1.

Is Embed 4 Turbo 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 Turbo cost?

Embed 4 Turbo costs $5.13 per million input tokens and $22.04 per million output tokens.

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