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multimodal benchmark

GDPval-MM Leaderboard

GDPval-MM is the multimodal variant of the GDPval benchmark, evaluating AI model performance on real-world economically valuable tasks that require processing and generating multimodal content including documents, slides, diagrams, spreadsheets, images, and other professional deliverables across diverse industries.

Updated Aug 17, 2026

Models3
Model coverage3
MetricScore
EvidenceB

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GDPval-MM Ranking

Higher score ranks better on this benchmark.

3 rows
Columns

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Rank
Model
Score
Percentile
Participants
Evidence
Evaluated
Rank01ModelOPGPT-5.5OpenAIScore84.9%Percentile100.0%Participants3EvidenceCEvaluatedAug 17, 2026
Rank02ModelOPGPT-5.5 ProOpenAIScore82.3%Percentile50.0%Participants3EvidenceCEvaluatedAug 17, 2026
Rank03ModelMIMiniMax M2.5MiniMaxScore59.0%Percentile0.0%Participants3EvidenceCEvaluatedAug 17, 2026

GDPval-MM Highlights

The leading models and scores on this benchmark.

Rank #1GPT-5.584.9%Rank #2GPT-5.5 Pro82.3%Rank #3MiniMax M2.559.0%

GDPval-MM Score Distribution

A closer view of the leading scores on this benchmark.

GDPval-MM

The Top AI Models for GDPval-MM

The first five results on this benchmark, with official price and output speed added where the model identity can be matched.

Ranking basisThis gdpval-mm AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.

  1. 01
    OP
    GPT-5.5OpenAI
    Score
    84.9%
    Price
    $5.0 input / $30 output per 1M tokens

    Strengths

    • Ranks #1 of 3 compared models
    • 100th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures GDPval-MM, not total model capability
  2. 02
    OP
    GPT-5.5 ProOpenAI
    Score
    82.3%
    Price
    $30 input / $180 output per 1M tokens

    Strengths

    • Ranks #2 of 3 compared models
    • 50th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures GDPval-MM, not total model capability
  3. 03
    MI
    MiniMax M2.5MiniMax
    Score
    59.0%
    Price
    $0.30 input / $1.2 output per 1M tokens
    Speed
    Up to 100 tok/s via MiniMax

    Strengths

    • Ranks #3 of 3 compared models
    • 0th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures GDPval-MM, not total model capability

Selection summary

Best AI Models for GDPval-MM

GPT-5.5 currently leads GDPval-MM with 84.9%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price and runtime.

Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.

Benchmark rank #1GPT-5.584.9% · $5.0 input / $30 output per 1M tokensBenchmark rank #2GPT-5.5 Pro82.3% · $30 input / $180 output per 1M tokensBenchmark rank #3MiniMax M2.559.0% · $0.30 input / $1.2 output per 1M tokens

What is GDPval-MM?

What GDPval-MM measures and how its scores work.

GDPval-MM is the multimodal variant of the GDPval benchmark, evaluating AI model performance on real-world economically valuable tasks that require processing and generating multimodal content including documents, slides, diagrams, spreadsheets, images, and other professional deliverables across diverse industries.

Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.

Family
GDPval-MM
Modality
multimodal
Primary category
multimodal
Score direction
higher
LLMBoard eligible
Yes
Evaluation key
gdpval-mm|llm-stats-current

Benchmark scores retain their original unit. Overall score eligibility is shown separately.

FAQ

Common questions about GDPval-MM.

Which model scores highest on GDPval-MM?

GPT-5.5 is currently ranked first with 84.9%.

What does GDPval-MM measure?

GDPval-MM is the multimodal variant of the GDPval benchmark, evaluating AI model performance on real-world economically valuable tasks that require processing and generating multimodal content including documents, slides, diagrams, spreadsheets, images, and other professional deliverables across diverse industries.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

3 model results are currently shown.

Does this benchmark affect the overall score?

Yes. This benchmark can contribute to the current LLMBoard capability score.