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MMMU-Pro

A bird's-eye view of the benchmark: what it measures and every AI IQ chart built on it.

About MMMU-Pro

MMMU-Pro evaluates multimodal academic reasoning across text, diagrams, charts, and images. The efficiency view compares source-backed scores with effective cost and connects explicit reasoning/nonreasoning siblings where available.

MMMU-Pro Cost Efficiency
MMMU-Pro Cost Efficiency
X = Artificial Analysis reported cost per task (log). Y = MMMU-Pro score. Each line connects one model's published reasoning-effort levels from the shared configuration dataset; models with one available level remain standalone points. Color = provider.

How to read this chart

Each line uses the shared model-configuration dataset to compare source-matched reasoning-effort levels on MMMU-Pro. Every point shows the score and AA cost per task for that exact configuration. Canonical score bars and bell curves remain one point per model.

Data sources
Artificial AnalysisArtificial Analysis model leaderboard
MMMU-Pro Benchmark Scores
MMMU-Pro Benchmark Scores
Each model's MMMU-Pro score. Color = provider.

How to read this chart

Bars rank models by the source-backed benchmark value used for this chart. Longer bars indicate higher published scores.

Data sources
Artificial AnalysisArtificial Analysis model leaderboard
MMMU-Pro vs Effective Cost
MMMU-Pro vs Effective Cost
X = effective cost (log). Y = MMMU-Pro score. Color = provider.
Controls:
MMMU-Pro1:1Cost

How to read this chart

Each point is a public model. The chart compares MMMU-Pro score against Effective Cost (per 1M I/O Tokens), with color showing the model provider.

Data sources
Artificial AnalysisARC PrizeVals.aiArtificial Analysis model leaderboard

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