With Gemini diffusion you can 'vibe code' so fast that it feels almost instant:

Excited to share what my team has been working on lately - Gemini diffusion! We bring diffusion to language modeling, yielding more power and blazing speeds! 🚀🚀🚀 Gemini diffusion is especially strong at coding. In this example the model generates at 2000 tokens/sec, including overheads like tokenization, prefill, safety filters etc.

Lightning speeds are not the only advantage. Unlike autoregressive models that are restricted to generating one token at a time, diffusion can do non-causal reasoning within the generation. Take this example: "What is (√(81) * (2/3))^2 + (15 - 3) / (2^2)). First provide the answer and then derive the solution." This is a very hard prompt for AR models because they can't reason about the solution before generating it, but diffusion models can reason non-causally to get it right (ans: 39). GPT-4o fails this problem:

A similar one inspired by the 'Sparks of AGI paper' by @SebastienBubeck et al: "How many primes are there between 150 and 250? The first thing you should output is the total number, then print the exact list inside [ ] brackets." (ans: 18) GPT-4o fails this one too:

For more details, and to get access, see here:

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