Inception Launches Mercury 3, Setting a New Pareto Frontier in Speed and Intelligence

• Mercury 3 is the most capable diffusion LLM ever released. Across a range of benchmarks, Mercury 3 outperforms GPT-6 Luna and Claude Haiku 5.5 while running 4–8x faster.

• More than 40 times the size of Mercury 1, Mercury 3 runs at comparable speeds over 1,000 tokens per second, challenging the assumption that greater intelligence requires slower inference.

• Early customers are already running Mercury 3 in browser use agents, interactive education, real-time search agents, and customer support.

REDWOOD CITY, Calif., October 19, 2026--Inception, the company behind the first commercial diffusion large language models (dLLMs), today announced Mercury 3, the most capable dLLM ever released. Mercury 3 demonstrates that dLLMs can scale without sacrificing speed, with quality on par or better than frontier speed-optimized models like GPT-6 Luna and Claude Haiku 5.5. It does this while maintaining inference speeds above 1,000 tokens per second.

Mercury 3 is Inception's fourth model release since launching Mercury 1 in June 2025, and each release has added capability without giving up speed. Mercury 2 introduced reasoning to diffusion models, Mercury 2.5 added tunable reasoning and a 260,000 token context window, and Mercury 3 delivers the largest jump in intelligence of any Mercury release and expands the context window to 1 million tokens.

Developers have faced a persistent trade-off when choosing AI models: smaller, faster models for latency-sensitive applications or more capable models that take longer to respond. Mercury 3 offers both. It outperforms GPT-6 Luna and Claude Haiku 5.5 on a broad range of standard benchmarks while keeping the speed of earlier Mercury models.

"Since we launched Mercury 1, the question we've heard most is whether diffusion can scale. Mercury 3 is over 40 times larger, significantly more capable, and still runs at over 1,000 tokens per second," said Stefano Ermon, co-founder of Inception. "We're showing that the limits to scaling diffusion models are compute and data, not fundamental algorithmic barriers. We've already started training our next model, and we expect to continue pushing intelligence forward without sacrificing speed."

When Inception introduced Mercury 1, diffusion LLMs remained largely an academic pursuit, with early models limited to simple tasks like code autocomplete. Unlike traditional autoregressive models, which generate text sequentially, one token at a time, diffusion models can generate multiple tokens in parallel, offering a fundamentally different path to faster inference. The central question was whether this parallel generation advantage could be preserved as diffusion models scaled to higher levels of intelligence and more demanding tasks.