Knowledge · OpenRouter

Auto on MMLU-Pro: score, rank and what it means

Auto scores 73.6 on MMLU-Pro, placing it #362 of 600 models in the index. The benchmark average is 73.5 and the leader is GPT-5.2 Pro at 91.6.

Score
73.6
0–100 scale
Rank
#362
of 600
Percentile
40th
within the index
Benchmark avg
73.5
all models
Leader
91.6
GPT-5.2 Pro
Gap to leader
18.0
points

Score distribution

The chart shows the top 10 models on MMLU-Pro, with Auto 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, Auto sits at #362 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 18.0 points. The median model in this index scores 76.1, which puts Auto below the midpoint.

OpenRouter models average 72.7 on this benchmark. Auto is 0.9 points above that provider line, so it is a stronger result than the average OpenRouter model.

Among mid-range models, the average is 71.5. Auto is 2.1 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 73.6 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 $7.53 per million input tokens, Auto 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
Sonar Pro Mini Perplexity 8192 $8.02 70.3
Firefunction Reasoning Fireworks AI 8192 $9.08 72.5
OLMo 2 Chat Allen Institute for AI 16384 $2.94 70.6
Arctic Chat Snowflake 16384 $2.70 69.8
Auto OpenRouter 32768 $7.53 70.2
Llama 3 Baseten Base Baseten 16384 $3.16 72.4
Mixtral Groq Chat Groq 8192 $3.09 72.0

Other benchmarks for Auto

BenchmarkScoreRankPercentileRelative
GPQA Diamond 63.9 378 37th
SWE-bench Verified 58.1 355 41th
HumanEval 75.2 340 43th
MATH-500 74.1 343 43th
AIME 2025 67.0 313 48th
LiveCodeBench 59.0 378 37th
IFEval 77.3 375 38th
TruthfulQA 73.8 357 41th
HellaSwag 81.1 364 39th
MMMU 69.6 314 48th
Arena Elo 1341.2 378 37th

FAQ

What score does Auto get on MMLU-Pro?

Auto scores 73.6 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.

How does Auto rank on MMLU-Pro?

It ranks #362 of 600 models, in the 40th percentile. The leader is GPT-5.2 Pro at 91.6.

Is Auto good at MMLU-Pro?

It is below the median for this benchmark, so test it carefully before relying on it for this capability. 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 Auto cost?

Auto costs $7.53 per million input tokens and $29.19 per million output tokens.

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