Vals MMMU (MMMU)
We show this table for reference; we do not rank on it.
Multimodal Multi-task Benchmark
MMMU score on MMMU — September 1, 2026
We mirror the published mmmu score view for MMMU. Claude Fable 5.1 leads the public snapshot at 90.64%, followed by Claude Opus 5 (89.88%) and Claude Fable 5 (89.31%). We do not use these results to rank models overall.
Claude Fable 5.1
Anthropic
Claude Opus 5
Anthropic
Claude Fable 5
Anthropic
93 modelsMultimodal & GroundedCurrentDisplay onlyUpdated September 1, 2026
MMMU score table (93 models)
ScoreHow MMMU is shown here
BenchLM mirrors the public Vals AI MMMU leaderboard captured from https://www.vals.ai/benchmarks/mmmu and updated by Vals on September 1, 2026. The snapshot preserves overall scores, uncertainty, latency, cost-per-test metadata, and task-level scores where Vals publishes them.
MMMU is display only on BenchLM. Vals proprietary or Vals-hosted aggregate views are useful context, but BenchLM does not use them as weighted ranking inputs or as a replacement for benchmark-native source records.
Snapshot
The published MMMU snapshot places Claude Fable 5.1 first at 90.64%. The third row is 1.33 points behind. The broader top-10 range is 2.43 points, so many of the published results sit in a relatively narrow band.
93 models have been evaluated on MMMU. The benchmark falls in the Multimodal & Grounded category. We keep external benchmark mirrors separate from the weighted global scoring system, so these results remain source-specific evidence. MMMU is currently displayed for reference but excluded from the scoring formula, so it does not directly affect overall rankings.
About MMMU
Year
2026
Tasks
Multimodal academic task suite
Format
Accuracy score
Difficulty
Multimodal college-level reasoning
BenchLM mirrors the public Vals AI MMMU leaderboard as display-only external evidence. The captured snapshot preserves overall scores, task-level scores where Vals publishes them, uncertainty, latency, and cost-per-test metadata. It is excluded from BenchLM weighted rankings.
Freshness and provenance
Version
MMMU 2026
Refresh cadence
Quarterly
Staleness state
Current
Question availability
Public benchmark set
BenchLM uses freshness metadata to decide whether a benchmark should still be treated as a strong differentiator, a benchmark to watch, or a display-only reference. For the full scoring policy, see the BenchLM methodology page.
Questions
What does MMMU measure?
Multimodal Multi-task Benchmark
Which model leads the published MMMU snapshot?
Claude Fable 5.1 currently leads the published MMMU snapshot with 90.64% mmmu score. BenchLM shows this benchmark for display only and does not use it in overall rankings.
How many models are evaluated on MMMU?
The September 1, 2026 snapshot contains 93 AI models.
Know when it’s worth switching models
The model to choose, the cheaper alternative, and the release we would wait on.
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