Pelicans aren't bad, a little quirky: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...

Those were via OpenRouter - here's another set I made by calling the Inception API directly: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...

I still don't understand why Google did effectively nothing with their Gemini Diffusion demo from last year.

It was really cool, and really fast, and just went quiet.

They did release a Diffusion Gemma a few months ago but I've not seen much of a splash from that one, either.

I'm very curious what the actual model size is and how the training process differed from a traditional LLM. Cool work regardless, incredible how catching up to a few months behind the cutting edge now seems like an achievable feat for new entrants, if still quite costly.

This looks noticeably more capable than previous gen. I purposely gave it a bit of a vague prompt to create an html renderer for UML diagrams, it produced impressive work. Then i asked a question, and in the third prompt I asked it to update the html which it did correctly.

Another thing that stood out, and this might just be a novelty thing, is that it picked different and less painful-to-eyes color schemes for each theme. Congrats to the team behind it.

Question (since their team has reps here and I have never used Inception via api): when you ask for a code change in your codebase (via coding agent), how much code does it need to read? How well does it handle tool calls? Since diffusion is a different way of doing things, I am curious about the code-needed for each incremental change in the codebase, say over a larger task.

I created a pelican worked wonderfully, better than old haiku model or even sonnet models, maybe slightly worse than new gen models.

But overall it's a pretty ok model, the issue with speed seems to be that ttft is still high but still not sure how high thinking burden would be, I hope it scales well will be using it a lot if it works well.

This might actually crush most other LLMs if it's price and performance actually scales well. It was as cheap as deepseek for a few tasks I tried.

Anyone know if there is a subscription for it? I might want to test it out properly without burning a few hundred dollars on it.

I'd be surprised if major labs aren't doing any diffusion experiments, and if they are, it would also be surprising if another lab is ahead of them in diffusion given just how much more compute they have at their disposal. My intuition could be way off though.

@volodia (Inception co-founder) has a comment here which is flagged/dead. That's weird; it shouldn't be. Can someone fix that?

I'm going to start using this instead of Haiku/GPT-Luna for tasks that require speed.

I'm wondering if it'll still be useful once the discount is lifted?

Hmm at least in Openrouter, deepseek v4.1 flash via together/baseten/fireworks still beat mercury 3 on latency and intelligence

Ttft is quite slow with mercury

If their benchmarks are to be believed they've made some real progress. I hope we see more open weight diffusion LLMs (currently there's diffusion gemma). Also, if diffusion LLMs are starting to come into their own that's going to be somewhat disruptive isn't it? If these are running at 10x throughput (at least) then that's a lot more tokens on the same hardware.

EDIT: just trying this out on a repo and it's actually pretty good so far. Pleasantly surprised.

Not as fast as GPT 6.1-Sol on my favorite toy math puzzle. It might be one of those things where intelligence consumes less tokens

Looks excellent, looking forward to trying this out. Feels like we're reaching a critical point where these become competitive.

I wonder given its speed how competitive it would be with the current batch of decision models like Jev et al. Seems to me for decision models you really want maximum throughput.

Damnit, I've been waiting on an open weights model like this one, still no dice :(

Are these models harder to reason about given they produce tokens non-linearly? Especially since we rely on non-latent reasoning to know what the model is "thinking".

Any comparision would make sense only if you made them - Mercury and Luna - the same training data and then the same post process, alignment. It's nice it's fast. I support it. Just saying I don't know how to judge that.

I really wish they would make these multimodal. Seems like a definite improvement over 2.5 though in my testing

100M free tokens? wow.

I wish they had a coding plan so your could kick the tires before paying API rates.

"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."

Hi HN, this is Volodymyr, Inception Co-Founder & Chief Scientist.

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.