Commonsense · Cerebras

Llama 3.1 Cerebras Instruct on HellaSwag: score, rank and what it means

Llama 3.1 Cerebras Instruct scores 87.7 on HellaSwag, placing it #154 of 600 models in the index. The benchmark average is 81.4 and the leader is GPT-5.2 Max at 96.1.

Score
87.7
0–100 scale
Rank
#154
of 600
Percentile
74th
within the index
Benchmark avg
81.4
all models
Leader
96.1
GPT-5.2 Max
Gap to leader
8.4
points

Score distribution

The chart shows the top 10 models on HellaSwag, with Llama 3.1 Cerebras Instruct 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, Llama 3.1 Cerebras Instruct sits at #154 of 600. The leader, GPT-5.2 Max, scores 96.1, so the gap to the top is 8.4 points. The median model in this index scores 83.6, which puts Llama 3.1 Cerebras Instruct above the midpoint.

Cerebras models average 79.4 on this benchmark. Llama 3.1 Cerebras Instruct is 8.3 points above that provider line, so it is a stronger result than the average Cerebras model.

Among multimodal models, the average is 86.8. Llama 3.1 Cerebras Instruct is 0.9 points above that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.

Read a 87.7 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 $4.11 per million input tokens, Llama 3.1 Cerebras Instruct 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
MiniMax-M2 Base MiniMax 65536 $4.20 76.4
MiniMax-M2 Chat MiniMax 200000 $5.02 80.2
ERNIE 4.5 Max Baidu 128000 $14.41 80.6
Hunyuan-Large Instruct Tencent 65536 $4.39 78.2
Llama 3.1 Cerebras Instruct Cerebras 65536 $4.11 76.1
o4 Mini OpenAI 200000 $11.92 77.7
Claude 3.7 Sonnet Anthropic 200000 $14.91 81.6

Other benchmarks for Llama 3.1 Cerebras Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 78.3 232 61th
GPQA Diamond 72.1 218 64th
SWE-bench Verified 65.9 196 67th
HumanEval 81.3 205 66th
MATH-500 80.9 193 68th
AIME 2025 69.2 276 54th
LiveCodeBench 67.1 246 59th
IFEval 85.8 165 73th
TruthfulQA 77.2 274 54th
MMMU 71.3 264 56th
Arena Elo 1388.4 256 57th

FAQ

What score does Llama 3.1 Cerebras Instruct get on HellaSwag?

Llama 3.1 Cerebras Instruct scores 87.7 on HellaSwag in this index. The benchmark average is 81.4 and the top score is 96.1.

How does Llama 3.1 Cerebras Instruct rank on HellaSwag?

It ranks #154 of 600 models, in the 74th percentile. The leader is GPT-5.2 Max at 96.1.

Is Llama 3.1 Cerebras Instruct good at HellaSwag?

Yes, relative to the index: it is above the median and inside the top half of scores. 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 Llama 3.1 Cerebras Instruct cost?

Llama 3.1 Cerebras Instruct costs $4.11 per million input tokens and $20.72 per million output tokens.

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