Inception launches Mercury 3, a diffusion model for real-time AI

Inception Labs introduced Mercury 3 on October 19, 2026, as a diffusion-based language model for real-time AI applications. Instead of producing text strictly from left to right, a diffusion model refines multiple parts of an answer in parallel. Inception says Mercury 3 is more than 40 times larger than its first Mercury model, expands the context window from 260,000 to one million tokens and reaches 1,068 tokens per second on NVIDIA GPUs. The company also says it is four to eight times faster than speed-focused models such as GPT-6 Luna and Claude Haiku 5.5 while matching or exceeding them on benchmarks. Those comparisons are Inception claims, not independent proof.

The model is available through the Inception API Platform, OpenRouter, Baseten and Vercel. It is compatible with the OpenAI API, so developers can test it without rewriting an existing integration. The launch price is $0.20 per million input tokens and $0.70 per million output tokens; an introductory discount cuts those rates in half. It also offers 100 million free API tokens. Enterprise deployments can use dedicated capacity, autoscaling, compliance controls and configurable data retention.

Inception says early-access partners use Mercury 3 for browser-use agents, customer support, real-time search, voice applications, tutoring and data engineering. This matters because when an agent calls a model repeatedly, small delays accumulate. Faster generation could make interactive tools more responsive and reduce the cost of workflows requiring many model calls. A million-token context window may help with large repositories or long-running tasks, although it does not guarantee that every detail is used correctly.

Independent context calls for caution. OrcaRouter’s analysis notes that the announcement gives comparative quality claims but no numerical benchmark scores. The Known Good tracked speed, price and context, but did not assign a quality score because results across its required science, mathematics and coding tests were missing. A Reddit discussion in r/singularity showed 98 points when checked, an interest signal rather than proof of quality.

For developers, Mercury 3 makes diffusion language models a concrete option for latency-sensitive work. Tests should cover task accuracy, tool reliability, safety, total cost and behavior under sustained load. The release brings a different generation architecture into a market where speed increasingly shapes the usefulness of AI agents.