Knowledge · Cerebras

Llama 3.1 Cerebras Instruct on MMLU-Pro: score, rank and what it means

Llama 3.1 Cerebras Instruct scores 78.3 on MMLU-Pro, placing it #232 of 600 models in the index. The benchmark average is 73.5 and the leader is GPT-5.2 Pro at 91.6.

Score
78.3
0–100 scale
Rank
#232
of 600
Percentile
61th
within the index
Benchmark avg
73.5
all models
Leader
91.6
GPT-5.2 Pro
Gap to leader
13.3
points

Score distribution

The chart shows the top 10 models on MMLU-Pro, with Llama 3.1 Cerebras Instruct highlighted. Longer bars mean higher scores.

GPT-5.2 Pro 91.6
GPT-5.1 Max 91.3
Claude Opus 4.5 Pro 91.2
Claude Opus 4.5 Reasoning 90.7
Gemini 3 Pro Reasoning 90.1
GPT-5.1 Reasoning 90.0
GPT-5.2 89.9
Claude Opus 4.5 Max 89.5
GPT-5.2 Vision 89.5
Gemini 3 Pro Max 89.5

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
2 GPT-5.1 Max OpenAI 2000000 $20.50 87.9
3 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
4 Claude Opus 4.5 Reasoning Anthropic 2000000 $20.91 88.2
5 Gemini 3 Pro Reasoning Google DeepMind 200000 $19.69 87.5
6 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
7 GPT-5.2 OpenAI 2000000 $19.71 88.0
8 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
9 GPT-5.2 Vision OpenAI 1000000 $19.03 87.4
10 Gemini 3 Pro Max Google DeepMind 2000000 $20.57 87.9

What this score means

On MMLU-Pro, Llama 3.1 Cerebras Instruct sits at #232 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 13.3 points. The median model in this index scores 76.1, which puts Llama 3.1 Cerebras Instruct above the midpoint.

Cerebras models average 70.9 on this benchmark. Llama 3.1 Cerebras Instruct is 7.4 points above that provider line, so it is a stronger result than the average Cerebras model.

Among multimodal models, the average is 80.0. Llama 3.1 Cerebras Instruct is 1.7 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 78.3 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 $4.11 per million input tokens, Llama 3.1 Cerebras Instruct is above the median input price of $4.00 across the 600 models scored on MMLU-Pro. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Codestral Instruct Mistral AI 65536 $4.01 75.1
DeepSeek-Coder DeepSeek 32768 $4.55 78.1
Phi-4 Chat Microsoft 65536 $3.66 74.7
Yi-Lightning Instruct 01.AI 128000 $4.16 76.5
Llama 3.1 Cerebras Instruct Cerebras 65536 $4.11 76.1
Mistral Small 3 Instruct Mistral AI 32768 $3.97 75.1
QwQ Alibaba Cloud 128000 $4.57 78.6

Other benchmarks for Llama 3.1 Cerebras Instruct

BenchmarkScoreRankPercentileRelative
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
HellaSwag 87.7 154 74th
MMMU 71.3 264 56th
Arena Elo 1388.4 256 57th

FAQ

What score does Llama 3.1 Cerebras Instruct get on MMLU-Pro?

Llama 3.1 Cerebras Instruct scores 78.3 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.

How does Llama 3.1 Cerebras Instruct rank on MMLU-Pro?

It ranks #232 of 600 models, in the 61st percentile. The leader is GPT-5.2 Pro at 91.6.

Is Llama 3.1 Cerebras Instruct good at MMLU-Pro?

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 MMLU-Pro score calculated?

MMLU-Pro is a knowledge 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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