Inception releases Mercury 3, a diffusion LLM it says beats GPT-6 Luna and Haiku 5.5 at 1,000+ tokens/s with 1M context; $0.20/$0.70 per 1M tokens, half off at launch

Why it matters

【Key points】

Inception, a company building diffusion language models, released Mercury 3 on Oct 19. Co-founder and Stanford professor Stefano Ermon announced it on X at 16:42 UTC, calling it the most capable diffusion LLM released so far. Autoregressive models write one token at a time; diffusion models draft and refine a whole passage in parallel, which makes them fast.

Inception says Mercury 3 matches or beats GPT-6 Luna and Claude Haiku 5.5 across a range of benchmarks while running 4–8x faster, at 1,068 tokens per second on NVIDIA GPUs. Context grows to 1M tokens from 260K in Mercury 2.5. The model has over 40x the total parameters of Mercury 1, with about 3x the benchmark intelligence of Mercury 1 mini. Pricing is $0.20 / $0.70 per million input / output tokens, half price at launch ($0.10 / $0.35), with cached input 90% off.

It is available now on Inception's API platform and through OpenRouter, Baseten and Vercel, and is OpenAI API compatible. Early partner TinyFish says Mercury 3 completed 50% of 50 live Mind2Web web-navigation tasks, well ahead of GPT-6.1 Sol.

【Verifiable】

Inception blog: https://www.inceptionlabs.ai/blog/introducing-mercury-3

Stefano Ermon's post: https://x.com/StefanoErmon/status/2112222911218291051

【Our read】

1. The real claim is that diffusion scales. If independent tests confirm quality on par with mainstream small models, diffusion stops being the fast-but-weak option and becomes a real rival architecture for low-latency work.

2. The comparisons are Inception's own picks of benchmarks and rivals, without full setups. Watch for independent evals such as Artificial Analysis, and for reliability on long tasks and tool use.

3. Voice, live support and browser agents wait on the model at every step, so speed turns straight into user experience. That is where Mercury 3 is likeliest to win first.