Mercury 3: Diffusion at scale
The developers of Mercury 3 have released a new diffusion-based large language model that claims to outperform current industry leaders in speed and efficiency. The model is designed for latency-sensitive applications like real-time tutoring and autonomous web agents.
Why it matters
It challenges the dominance of autoregressive models in AI, suggesting that diffusion-based architectures can scale effectively for commercial use.
Today we’re introducing Mercury 3, the most capable diffusion-based LLM released to date. Across a range of benchmarks, Mercury 3 outperforms GPT-6 Luna and Claude Haiku 5.5, while running 4–8x faster at 1,000+ tokens per second.
Mercury 3 demonstrates that text diffusion scales. It’s more than 40x larger than Mercury 1 and is vastly more intelligent, but just as fast.
Early-access partners already use Mercury 3 for voice and chat support, real-time tutoring, data engineering, and web navigation:
Quality: On par or better than frontier speed-optimized models like GPT-6 Luna and Claude Haiku 5.5.
Speed: 1,068 tokens per second on NVIDIA GPUs.
Context: 1M tokens, up from 260K for Mercury 2.5
Price: $0.20 per million input and $0.70 per million output tokens.
At launch, Mercury 3 is 50% off at $0.10 per 1M input and $0.35 per 1M output. Cached tokens are 90% off the input price.
Get smarter about the news
Sign up free for a feed built around what you actually care about, Dive Deeper research on any story, and the full text of every article.
Create free accountAlready have an account? Sign in