Mercury 2

A second Inception blog post by Joyce Chen positions Mercury 2 as "the first reasoning model fast enough to pick up the phone," targeting real-time voice applications. It argues that GPT 5.x, Claude Sonnet, and Gemini Pro fail to meet sub-500ms latency requirements for voice agents when running reasoning chain-of-thoug The post contextualizes Mercury 2's launch against the imminent sunsetting of GPT-4.1, which the author identifies as the de facto default model for most production voice agents as of April 2025 due to its balance of instruction-following, tool-calling, and realtime latency. Inception positions Mercury 2 as the first r

Inception published a first-party blog post officially launching Mercury 2, the company's diffusion-based reasoning large language model. The post, authored by CEO Stefano Ermon, introduces Mercury 2 as the fastest reasoning LLM, built on parallel-refinement diffusion decoding rather than token-by-token autoregressive The blog frames Mercury 2's release as a meaningful step in moving diffusion LLM technology from research into production, arguing that diffusion's parallel-token generation fundamentally changes the intelligence-versus-latency trade-off by enabling reasoning-grade quality within real-time latency budgets. Compared wit

Artificial Analysis has published an independent intelligence, performance, and price profile for Inception's Mercury 2, a proprietary model released in February 2026. The third-party evaluation scores Mercury 2 at 15 on the Artificial Analysis Intelligence Index v4.2 — above the comparable-model median of 12 — and mea The benchmark page classifies Mercury 2 as a reasoning-capable model (noting a non-reasoning variant may also exist) with text-in/text-out modalities and a 128K token context window. Notably, Artificial Analysis's measured speed of 794.5 tokens per second is lower than Inception's vendor-reported 1,009 tokens per secon