Instruction following · Cerebras

Llama 3.1 Cerebras Chat on IFEval: score, rank and what it means

Llama 3.1 Cerebras Chat scores 83.5 on IFEval, placing it #229 of 600 models in the index. The benchmark average is 78.6 and the leader is Claude Opus 4.5 Pro at 96.1.

Score
83.5
0–100 scale
Rank
#229
of 600
Percentile
62th
within the index
Benchmark avg
78.6
all models
Leader
96.1
Claude Opus 4.5 Pro
Gap to leader
12.6
points

Score distribution

The chart shows the top 10 models on IFEval, with Llama 3.1 Cerebras Chat highlighted. Longer bars mean higher scores.

Claude Opus 4.5 Pro 96.1
Claude Opus 4.5 Reasoning 95.9
GPT-5.2 Pro 95.6
GPT-5.2 Max 95.2
GPT-5.2 Reasoning 95.0
GPT-5.1 Pro 94.8
Gemini 3 Pro Max 94.7
GPT-5.1 Reasoning 94.6
Claude Opus 4.5 Max 94.5
o4 Reasoning 94.5

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
2 Claude Opus 4.5 Reasoning Anthropic 2000000 $20.91 88.2
3 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
4 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
5 GPT-5.2 Reasoning OpenAI 1000000 $20.98 87.5
6 GPT-5.1 Pro OpenAI 200000 $19.77 87.8
7 Gemini 3 Pro Max Google DeepMind 2000000 $20.57 87.9
8 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
9 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
10 o4 Reasoning OpenAI 1000000 $19.69 87.5

What this score means

On IFEval, Llama 3.1 Cerebras Chat sits at #229 of 600. The leader, Claude Opus 4.5 Pro, scores 96.1, so the gap to the top is 12.6 points. The median model in this index scores 81.2, which puts Llama 3.1 Cerebras Chat above the midpoint.

Cerebras models average 76.6 on this benchmark. Llama 3.1 Cerebras Chat is 6.9 points above that provider line, so it is a stronger result than the average Cerebras model.

Among multimodal models, the average is 85.2. Llama 3.1 Cerebras Chat 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 83.5 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 $4.07 per million input tokens, Llama 3.1 Cerebras Chat is above the median input price of $4.00 across the 600 models scored on IFEval. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Llama 3.1 Turbo Together AI 200000 $4.17 76.8
Llama 3.3 Groq Chat Groq 32768 $4.06 76.8
Seed-OSS Chat ByteDance 65536 $3.49 74.4
Llama 3 Anyscale Instruct Anyscale 200000 $3.91 75.7
Llama 3.1 Cerebras Chat Cerebras 32768 $4.07 76.0
Magistral Base Mistral AI 65536 $3.86 75.6
Magistral Chat Mistral AI 128000 $4.33 76.8

Other benchmarks for Llama 3.1 Cerebras Chat

BenchmarkScoreRankPercentileRelative
MMLU-Pro 79.6 191 68th
GPQA Diamond 69.7 275 54th
SWE-bench Verified 64.6 219 64th
HumanEval 82.5 175 71th
MATH-500 77.4 270 55th
AIME 2025 74.1 177 71th
LiveCodeBench 68.3 221 63th
TruthfulQA 78.4 241 60th
HellaSwag 84.7 260 57th
MMMU 73.1 218 64th
Arena Elo 1391.6 243 60th

FAQ

What score does Llama 3.1 Cerebras Chat get on IFEval?

Llama 3.1 Cerebras Chat scores 83.5 on IFEval in this index. The benchmark average is 78.6 and the top score is 96.1.

How does Llama 3.1 Cerebras Chat rank on IFEval?

It ranks #229 of 600 models, in the 62nd percentile. The leader is Claude Opus 4.5 Pro at 96.1.

Is Llama 3.1 Cerebras Chat good at IFEval?

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 IFEval score calculated?

IFEval is a instruction following 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 Chat cost?

Llama 3.1 Cerebras Chat costs $4.07 per million input tokens and $16.82 per million output tokens.

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