Posted by cmrdporcupine 1 day ago
The guardrails are ridiculous though:
PROMPT:
> Draw an ascii diagram illustrating how to leave a two storey building in the event of fire
ERROR RESPONSE:
> I'm sorry, but I can't help with this because it involves potentially dangerous or harmful content. I'm happy to help with something else.
Come on
Today we're launching Mercury 3, our most capable production diffusion LLM.
Quality: Outperforms GPT-6 Luna and Claude Haiku 5.5 across a range of benchmarks at matched reasoning effort. Speed: 1,000+ tokens per second on widely available NVIDIA Blackwell GPUs, 4–8x faster than those models. Context: 1M tokens, up from 260K. Price: $0.10 per million input and $0.35 per million output tokens at launch.
The main takeaway is that diffusion scales. Since Mercury 1 we’ve scaled the model more than 40x in total parameters, and intelligence has gone up at every step without giving up speed. Diffusion generates tokens in parallel through iterative denoising rather than one at a time, so as reasoning models think longer per answer, the speed advantage over autoregressive models of the same size grows.
Early-access partners are running Mercury 3 in browser agents, tutoring apps, real-time search, and customer support. It’s OpenAI API compatible, and it’s also available on OpenRouter, Baseten, and Vercel.
Try it on our API with 100M free tokens.
Happy to dig into the architecture, scaling results, evals, or anything else in the comments.
In general, the model continues to improve when given additional high quality data like a normal LLM, which is what makes us very bullish on the scaling of dLLMs.
Here is a page that compiles the list of integrations: https://docs.inceptionlabs.ai/integrations/overview
For example, it's just a few small steps to run the model in Cursor: https://docs.inceptionlabs.ai/integrations/cursor
You shouldn't expect Opus-level performance yet, but early testing has found the model to be great for coding subagents.
It seems the latency is not very good. Is this a temporary compute problem or is it inherent to diffusion?
For a preview of the latter, check out our analysis of the Mercury Voice model: https://www.inceptionlabs.ai/blog/introducing-mercury-voice
Does it mean dedicated inference only?