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New MMI benchmark reveals low multimodal capabilities in frontier LLMs

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced the Modality Maturity Index (MMI), a new benchmark designed to evaluate the multimodal capabilities of large language models across five modalities: text, image, audio, video, and documents. The MMI benchmark includes 893 questions that require models to process multiple input modalities and generate responses incorporating various output formats. Initial testing on five frontier multimodal models revealed low Modality Presence Scores (MPS), with Claude Opus 4.6 scoring 15.6 and GPT-5.4 scoring 34.9, indicating significant limitations in generating the expected output modalities. AI

IMPACT This benchmark could drive improvements in multimodal AI by highlighting current limitations in model output generation across various formats.

RANK_REASON The cluster contains an academic paper introducing a new benchmark for evaluating AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New MMI benchmark reveals low multimodal capabilities in frontier LLMs

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The cluster contains an academic paper introducing a new benchmark for evaluating AI models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Rohit Patel, Dieuwke Hupkes, Sloan Strader ·

    Modality Maturity Index: A benchmark for assessing multimodal capabilities of omni models

    arXiv:2608.26317v1 Announce Type: cross Abstract: Frontier language models are increasingly marketed as omni systems that can perceive and respond across modalities. Existing evaluation frameworks, however, focus almost exclusively on bimodal understanding, typically text plus on…

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