Mathematics · Cerebras

Llama 3.1 Cerebras Instruct on MATH-500: score, rank and what it means

Llama 3.1 Cerebras Instruct scores 80.9 on MATH-500, placing it #193 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.

Score
80.9
0–100 scale
Rank
#193
of 600
Percentile
68th
within the index
Benchmark avg
72.5
all models
Leader
96.7
GPT-5.2 Max
Gap to leader
15.8
points

Score distribution

The chart shows the top 10 models on MATH-500, with Llama 3.1 Cerebras Instruct 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, Llama 3.1 Cerebras Instruct sits at #193 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 15.8 points. The median model in this index scores 76.0, which puts Llama 3.1 Cerebras Instruct above the midpoint.

Cerebras models average 68.5 on this benchmark. Llama 3.1 Cerebras Instruct is 12.4 points above that provider line, so it is a stronger result than the average Cerebras model.

Among multimodal models, the average is 81.3. Llama 3.1 Cerebras Instruct is 0.4 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 80.9 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 MATH-500. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
DeepSeek-Coder Instruct DeepSeek 32768 $4.34 77.7
Nova Pro Turbo Amazon 128000 $13.18 78.4
Reka Core Turbo Reka AI 32768 $12.58 78.4
Llama 4 Scout Meta AI 200000 $4.90 79.3
Llama 3.1 Cerebras Instruct Cerebras 65536 $4.11 76.1
Kimi K2 Base Moonshot AI 65536 $4.82 79.2
MiniMax-Text Instruct MiniMax 128000 $4.59 77.7

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
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 MATH-500?

Llama 3.1 Cerebras Instruct scores 80.9 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.

How does Llama 3.1 Cerebras Instruct rank on MATH-500?

It ranks #193 of 600 models, in the 68th percentile. The leader is GPT-5.2 Max at 96.7.

Is Llama 3.1 Cerebras Instruct good at MATH-500?

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 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 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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