Hacker News: volodia

New comment by volodia in "Mercury 3: Diffusion at scale"

volodia — Mon, 19 Oct 2026 19:26:27 +0000

We're not publicly announcing the model size right now, but we can compare against other models with a similar intelligence -- what we see is a big boost in speed for the same level of capabilities.

New comment by volodia in "Mercury 3: Diffusion at scale"

volodia — Mon, 19 Oct 2026 19:08:11 +0000

We are going to be sharing additional information like task completion times in the near future as the model undergoes additional benchmarking by third parties. We also plan to share data on specific use cases where the model traces new points of the Pareto frontier and and speed cost.

For a preview of the latter, check out our analysis of the Mercury Voice model: https://www.inceptionlabs.ai/blog/introducing-mercury-voice

New comment by volodia in "Mercury 3: Diffusion at scale"

volodia — Mon, 19 Oct 2026 19:05:04 +0000

Not at the moment, but we are looking to share more information about the model in the future via technical blog posts and technical reports.

New comment by volodia in "Mercury 3: Diffusion at scale"

volodia — Mon, 19 Oct 2026 19:04:00 +0000

This is an area that we're really excited about. We have internal prototypes, and if you're interested in trying it out as an early tester, please reach out to hello@inceptionlabs.ai

New comment by volodia in "Mercury 3: Diffusion at scale"

volodia — Mon, 19 Oct 2026 19:03:08 +0000

We have internal prototypes, so please reach out to hello@inceptionlabs.ai if you're interested in early access

New comment by volodia in "Mercury 3: Diffusion at scale"

volodia — Mon, 19 Oct 2026 19:02:00 +0000

We might not be able to precisely explain the 6% difference on that specific benchmark, but overall we have found the model to be Haiku- or Luna-level in our own daily usage. That means that it won't yet replace Opus or Fable as the primary driver of a harness, but we have used it successfully for subagents, including implementing kernel optimization ideas produced by a larger model (and those improvements have gone into production -- a form of RSI).

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.

New comment by volodia in "Mercury 3: Diffusion at scale"

volodia — Mon, 19 Oct 2026 18:56:03 +0000

We've been happy with how performance has scaled with batch size. The model runs in our API at reasonable costs, which is also what allows us to serve the model with competitive prices ($0.10 per 1M input and $0.35 per 1M output at launch).

New comment by volodia in "Mercury 3: Diffusion at scale"

volodia — Mon, 19 Oct 2026 18:51:58 +0000

We're not releasing the weights, but the model is available using a standard API, and it integrates with your favorite harnesses.

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.

New comment by volodia in "Mercury 3: Diffusion at scale"

volodia — Mon, 19 Oct 2026 17:37:43 +0000

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.

How to build a diffusion language model

volodia — Sun, 30 Aug 2026 23:41:32 +0000

Article URL: https://kuleshov-group.github.io/blog/blog/2026/how-to-build-a-diffusion-language-model/

Comments URL: https://news.ycombinator.com/item?id=49503956

Points: 184

# Comments: 20

Next-Edit in Kilo, Powered by Inception Diffusion LLMs

volodia — Mon, 29 Jun 2026 22:02:21 +0000

Article URL: https://blog.kilo.ai/p/announcing-next-edit-in-kilo-powered-by-inception

Comments URL: https://news.ycombinator.com/item?id=48725881

Points: 2

# Comments: 0

Mercury 2 on PinchBench: Diffusion LLM benchmarked on real OpenClaw agent tasks

volodia — Wed, 25 Mar 2026 18:24:52 +0000

Article URL: https://www.inceptionlabs.ai/blog/mercury-2-on-pinchbench

Comments URL: https://news.ycombinator.com/item?id=47521254

Points: 2

# Comments: 0

New comment by volodia in "Mercury 2: Fast reasoning LLM powered by diffusion"

volodia — Thu, 26 Feb 2026 20:23:41 +0000

Thank you for the detailed feedback! I shared this already with the team.

New comment by volodia in "Mercury 2: Fast reasoning LLM powered by diffusion"

volodia — Wed, 25 Feb 2026 03:15:33 +0000

This looks like an inference glitch that we are working on fixing, thank you for flagging.

New comment by volodia in "Mercury 2: Fast reasoning LLM powered by diffusion"

volodia — Wed, 25 Feb 2026 02:48:03 +0000

There are many ways to do it, but the simplest approach is block diffusion: https://m-arriola.com/bd3lms/

There are also more advanced approaches, for example FlexMDM, which essentially predicts length of the "canvas" as it "paints tokens" on it.

New comment by volodia in "Mercury 2: Fast reasoning LLM powered by diffusion"

volodia — Wed, 25 Feb 2026 02:37:32 +0000

Would love to hear about your experience. Send us an email.

New comment by volodia in "Mercury 2: Fast reasoning LLM powered by diffusion"

volodia — Wed, 25 Feb 2026 02:36:29 +0000

Not imminently, but hard to predict where the field will go

New comment by volodia in "Mercury 2: Fast reasoning LLM powered by diffusion"

volodia — Wed, 25 Feb 2026 02:26:48 +0000

There are few: fast agents, deep research, real-time voice, coding. The other thing is that when you have a fast reasoning model, you spend more effort on thinking in the same latency budget, which pushed up quality.

New comment by volodia in "Mercury 2: Fast reasoning LLM powered by diffusion"

volodia — Wed, 25 Feb 2026 02:21:09 +0000

We agree! In fact, there is an emerging class of models aimed at fast agentic iteration (think of Composer, the Flash versions of proprietary and open models). We position Mercury 2 as a strong model in this category.

New comment by volodia in "Mercury 2: Fast reasoning LLM powered by diffusion"

volodia — Wed, 25 Feb 2026 02:17:55 +0000

That is also our view! We see Mercury 2 as enabling very fast iteration for agentic tasks. A single shot at a problem might be less accurate, but because the model has a shorter execution time, it enables users to iterate much more quickly.