Gemini Diffusion: How this generation write everything at once!

magine an AI that doesn't type word by word but instead starts with a messy blob and carves out fully coherent paragraphs-all at once. That’s Google’s Gemini Diffusion, and it’s turning heads with speeds topping 1,600 tokens per second.

In our latest video, we explore:

• How it mimics image diffusion to generate text
• Real-world demos that include instant app generation and multi-language translations
• Benchmarks where it holds its own against much bigger models
• Why it might be the key to unlocking massive context windows with blazing speed

Average Token per Second for LLMs (2024-2025)

These are typical average speeds for LLMs under standard conditions (may vary based on model size, backend hardware, and prompt complexity):

| | | | Model | | Avg. Token/sec (output) | | Notes | | | | | | GPT-4 (API) | | ~30–60 tokens/sec | | Slower, optimized for quality; GPT-4 Turbo is faster | | | | GPT-3.5 (API) | | ~60–100 tokens/sec | | Snappier than GPT-4 | | | | Claude 2 | | ~20–40 tokens/sec | | Focuses on coherence and safety | | | | Claude 3 (Opus/Sonnet) | | ~50–100 tokens/sec | | Sonnet is noticeably faster than Opus | | | | Mistral 7B (local) | | ~60–120 tokens/sec | | Blazing fast if run on good hardware | | | | Gemini 1.5 | | ~50–100 tokens/sec | | Comparable to Claude 3 in speed | | | | Gemini Diffusion (preview) | | 1000–1600 tokens/sec | | Order of magnitude faster - thanks to parallel generation | | | |