Inception model releases

Palo Alto AI startup (Inception Labs) building commercial-scale diffusion large language models (dLLMs), which generate and refine many tokens in parallel rather than left-to-right for much higher throughput and lower latency. Founded by Stefano Ermon (Stanford, a co-inventor of diffusion models) with Aditya Grover (UCLA) and Volodymyr Kuleshov (Cornell); ships the Mercury family of fast reasoning/coding dLLMs. Backed by Menlo Ventures, Mayfield, M12, Snowflake Ventures, Databricks, and Innovation Endeavors. This page collects the lab's model releases, lifecycle events, source links, and model metadata in one crawlable record.

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Aug 31, 2026

Mercury 2.5 Preview

Inception

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Mercury 2.5 Preview

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InceptionProprietary

The latest diffusion large language model (dLLM) from Inception, released Aug 31, 2026. Instead of generating tokens sequentially, Mercury 2.5 produces and refines many tokens in parallel, reaching ~1,107 tokens/sec on standard GPUs. Inception positions it as the fastest reasoning LLM, reporting a 10+ point intelligence gain over Mercury 2 and quality comparable to cost-optimized frontier models (GPT-5.6 Luna Low, Gemini 3.5 Flash-Lite, Claude Haiku 4.5). It supports tunable reasoning levels, parallel tool calls, and schema-aligned JSON output, and targets latency-sensitive production workloads such as search agents, voice pipelines, and coding subagents. 260K-token context, up to 65,536 output tokens; API-only (proprietary) via Inception and OpenRouter (inception/mercury-2.5-preview). List pricing is $0.20/$0.75 per Mtok input/output (a launch promo ran at $0.04/$0.15). Architecture recorded as unknown: it is a diffusion LM rather than a standard dense/MoE autoregressive transformer.

—Undisc.260K ctxAug 31, 2026