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# Inception Labs unveils Mercury 2 diffusion LLM with reasoning
- URL: https://www.testingcatalog.com/inception-labs-unveils-mercury-2-diffusion-llm-with-reasoning/
- Published: 2026-02-25T08:21:26.000Z
- Updated: 2026-02-25T08:21:26.000Z
- Description: Inception Labs introduces Mercury 2, a diffusion-based LLM designed for high-speed, multi-step reasoning tasks with 128K context window.
- Author: Alexey Shabanov
- Tags: Latest AI News, AI Announcements

Inception Labs is positioning Mercury 2 as a reasoning-focused model aimed at production systems where latency accumulates across multi-step agent loops, retrieval pipelines, and large-scale extraction jobs. The company’s perspective is that modern AI work is no longer a single prompt and response, making left-to-right token generation the bottleneck that users notice.

> Mercury 2 is live 🚀🚀  
>  
> The world’s first reasoning diffusion LLM, delivering 5x faster performance than leading speed-optimized LLMs.  
>  
> Watching the team turn years of research into a real product never gets old, and I’m incredibly proud of what we’ve built.  
>  
> We’re just getting… [pic.twitter.com/McrQG4PFLZ](https://t.co/McrQG4PFLZ?ref=testingcatalog.com)
> 
> — Stefano Ermon (@StefanoErmon) [February 24, 2026](https://twitter.com/StefanoErmon/status/2026340720064520670?ref%5Fsrc=twsrc%5Etfw&ref=testingcatalog.com)

Inception states that Mercury 2 employs diffusion-style text generation instead of autoregressive decoding. According to their description, the model generates and refines many tokens in parallel over a small number of steps, then converges on the final output. The company argues that this approach shifts the usual tradeoff where stronger reasoning requires more test-time compute, which directly increases latency and cost.

💡

Test Mercury 2 on [Inception Chat](https://chat.inceptionlabs.ai/?ref=testingcatalog.com)

In the announcement, Inception lists Mercury 2 at 1,009 tokens per second on NVIDIA Blackwell GPUs, featuring a 128K context window, tunable reasoning, native tool use, and schema-aligned JSON output. Pricing is presented as $0.25 per million input tokens and $0.75 per million output tokens. The company also claims OpenAI API compatibility to support drop-in adoption without major rewrites.

The post also includes throughput comparisons and benchmark-style figures, along with partner quotes focused on lower latency for transcript cleanup and faster automation-style workloads. Inception Labs is building its lineup around diffusion LLMs and presents its team as having contributed to widely used ML techniques and systems work.

[Source](https://www.inceptionlabs.ai/blog/introducing-mercury-2?ref=testingcatalog.com)