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LLMs match multimodal embeddings in text-to-image retrieval

By PulseAugur Editorial · [2 sources] ·

A new study compares the effectiveness of frontier Large Language Models (LLMs) against natively multimodal embedding models for text-to-image retrieval. The research found that models like GPT-4.1 and Claude Sonnet 4.6 perform comparably to Google's Gemini Embedding 2 on the Flickr30k dataset. While LLMs show strong visual understanding, precomputed multimodal embeddings are more suitable for applications requiring low latency. AI

IMPACT Frontier LLMs demonstrate competitive zero-shot ranking capabilities, potentially reducing the need for specialized multimodal embedding models in certain applications.

RANK_REASON The cluster contains an academic paper presenting a comparison of AI model capabilities on a specific task.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLMs match multimodal embeddings in text-to-image retrieval

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Archan Dutta, Vyanktesh Kanungo ·

    Can Frontier LLMs Match Natively Multimodal Embeddings? A Comparison on Hard-Negative Text-to-Image Retrieval

    arXiv:2608.11343v1 Announce Type: new Abstract: Multimodal retrieval and classification across different types of media, spanning text, images,video and audio, has traditionally relied on dual-encoder models that align visual and textual representations through contrastive learni…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Vyanktesh Kanungo ·

    Can Frontier LLMs Match Natively Multimodal Embeddings? A Comparison on Hard-Negative Text-to-Image Retrieval

    Multimodal retrieval and classification across different types of media, spanning text, images,video and audio, has traditionally relied on dual-encoder models that align visual and textual representations through contrastive learning. The March 2026 release of Gemini Embedding 2…

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