@GoogleDeepMind: At 740M parameters, it’s competitive across benchmarks – even outperforming some specialist models more than twice its …

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multimodal small-model gemma google-deepmind open-weights search

Summary

Google DeepMind unveiled a 740M-parameter multimodal model that is competitive across benchmarks, outperforming some specialist models more than twice its size, enabling developers to add multimodal search features like finding video moments via voice memo and pair it with Gemma 4 for private on-device use.

At 740M parameters, it’s competitive across benchmarks – even outperforming some specialist models more than twice its size. Developers can use it to add multimodal search to their apps – like finding moments in a video using a voice memo – or pair it with Gemma 4 for private, https://t.co/qSClXJIdNQ
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Cached at: 10/06/26, 05:22 PM

At 740M parameters, it’s competitive across benchmarks – even outperforming some specialist models more than twice its size.

Developers can use it to add multimodal search to their apps – like finding moments in a video using a voice memo – or pair it with Gemma 4 for private, https://t.co/qSClXJIdNQ

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