Knowledge · Replicate

Llama 3 Replicate Chat on MMLU-Pro: score, rank and what it means

Llama 3 Replicate Chat scores 77.5 on MMLU-Pro, placing it #261 of 600 models in the index. The benchmark average is 73.5 and the leader is GPT-5.2 Pro at 91.6.

Score
77.5
0–100 scale
Rank
#261
of 600
Percentile
57th
within the index
Benchmark avg
73.5
all models
Leader
91.6
GPT-5.2 Pro
Gap to leader
14.1
points

Score distribution

The chart shows the top 10 models on MMLU-Pro, with Llama 3 Replicate Chat 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 Replicate Chat sits at #261 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 14.1 points. The median model in this index scores 76.1, which puts Llama 3 Replicate Chat above the midpoint.

Replicate models average 68.9 on this benchmark. Llama 3 Replicate Chat is 8.6 points above that provider line, so it is a stronger result than the average Replicate model.

Among multimodal models, the average is 80.0. Llama 3 Replicate Chat is 2.5 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 77.5 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 $3.74 per million input tokens, Llama 3 Replicate Chat is below 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
Llama 3 Anyscale Anyscale 65536 $3.94 74.7
Llama 3 Anyscale Chat Anyscale 65536 $3.67 74.6
Llama 3 DeepInfra DeepInfra 128000 $3.67 74.0
Llama 3 Novita Instruct Novita AI 32768 $3.94 75.6
Llama 3 Replicate Chat Replicate 200000 $3.74 74.7
Mistral Small 3 Base Mistral AI 16384 $3.19 72.7
QwQ Base Alibaba Cloud 65536 $3.68 74.1

Other benchmarks for Llama 3 Replicate Chat

BenchmarkScoreRankPercentileRelative
GPQA Diamond 67.3 325 46th
SWE-bench Verified 65.5 205 66th
HumanEval 79.6 245 59th
MATH-500 76.0 300 50th
AIME 2025 66.4 324 46th
LiveCodeBench 70.3 185 69th
IFEval 82.4 261 57th
TruthfulQA 79.7 194 68th
HellaSwag 84.7 259 57th
MMMU 72.4 233 61th
Arena Elo 1395.7 224 63th

FAQ

What score does Llama 3 Replicate Chat get on MMLU-Pro?

Llama 3 Replicate Chat scores 77.5 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.

How does Llama 3 Replicate Chat rank on MMLU-Pro?

It ranks #261 of 600 models, in the 57th percentile. The leader is GPT-5.2 Pro at 91.6.

Is Llama 3 Replicate Chat 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 Replicate Chat cost?

Llama 3 Replicate Chat costs $3.74 per million input tokens and $17.46 per million output tokens.

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