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ClosedGoogle DeepMind/10 trustVerified
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Gemini Diffusion

Google's experimental text diffusion model generating ~1,479 tokens per second.

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About

Gemini Diffusion, unveiled at Google I/O on May 20, 2025, is Google DeepMind's research foray into diffusion-based language modeling. Instead of generating tokens left to right, it starts from noise and iteratively denoises whole blocks of text in parallel, which also enables mid-generation self-correction of uncertain positions.

DeepMind reported speeds of 1,479 tokens per second (up to 2,000 on code tasks), with benchmark performance roughly matching Gemini 2.0 Flash-Lite: near-identical HumanEval and MBPP scores, slightly ahead on LiveCodeBench, but behind on GPQA Diamond and multilingual MMLU. It launched as an experimental demo with an early-access waitlist and no arXiv paper; the later open-weights DiffusionGemma brought the research to downloadable form.

Specs

Context window

Pricing

Free quota

API access

Weights

Closed — API only

Released

May 20, 2025

Benchmarks

Provider-reported unless a source says otherwise. Benchmarks are marketing-adjacent by nature — treat them as directional.

Use cases

fast-text-generationcode-completionlow-latency-inference

Trust score — 9/10

/10 trustVerified

Scored 9/10 (verified tier, metrics-v1). Evidence: Verified via vendor site/docs/GitHub and reputable coverage, wave 81; Trending score 88/100 from researcher assessment.

Sources