El LLM de difusión Mercury 3 de Inception ofrece 1.068 tokens por segundo y una ventana de contexto de 1M, lo que lo convierte en una opción rápida y competitiva para los desarrolladores que crean agentes y herramientas de voz.
Resumen generado con IA. Consulta el original para comprobar el contexto y la precisión.
Este es el extracto disponible del feed. Puede estar abreviado o en el idioma original.
Inception has released Mercury 3, the newest version of its diffusion language model. Mercury 3 is a diffusion LLM from Inception that writes answers in parallel, not token by token. Inception reports 1,068 tokens per second on NVIDIA GPUs and a 1M-token context window. On Inception’s own benchmark table, it leads GPT-6 Luna and Claude Haiku 5.5 on 8 of 12 tests. For developers building agents, voice bots and search tools, this is a speed-first model with competitive scores. TL;DR Size: Parameters not disclosed. Inception says it is more than 40x larger than Mercury 1. Context is 1M tokens. Runs on: Inception-served NVIDIA GPUs (exact GPU not disclosed). API only, no open weights or quantized builds. Performance: Leads 8 of 12 benchmarks vs GPT-6 Luna and Haiku 5.5 (self-reported, medium settings). Best: SWE-Bench Atlas 51.08, about 18 points ahead of both rivals. Worst: DeepSWE v1 46.31, 5.31 points behind Haiku 5.5 (51.62). What is Mercury 3? Mercury 3 is Inception’s fourth Mercury release, after Mercury 1, Mercury 2 and Mercury 2.5. Mercury 1 launched in 2025. Mercury 2.5 added tunable reasoning and a 260K context window, per Inception’s launch announcement. 1/4 Today, we’re releasing Mercury 3. Mercury 3 is by far our most powerful model and the most intelligent diffusion LLM ever released. It outperforms GPT-6 Luna and Haiku 5.5 on quality while running at 1000+ tok/sec. pic.twitter.com/TYevDl43Lm— Stefano Ermon (@StefanoErmon) October 19, 2026 How does a diffusion LLM work? Autoregressive models write one token after another. Each new token waits for the previous one. A diffusion LLM starts from a rough draft of the full answer. It then refines every position over a few passes in parallel. Fewer sequential steps means faster output on the same GPUs. The open question was whether this approach holds up as models grow larger. Does diffusion scale? Here Inception’s answer is yes. Mercury 3 has more than 40x the total parameters of Mercury 1 (m
Una ayuda automática para ordenar, basada en fuentes, palabras clave y entidades, en una escala de 1–12. Alta: 9–12; media: 6–8; baja: 1–5. No mide urgencia, verifica noticias ni representa respaldo editorial.
Voxtral 3 de Mistral unifica las capacidades de transcripción de voz a texto, síntesis de voz y agentes de voz en una única API, simplificando el desarrollo de aplicaciones de voz en tiempo real.
llm-d 1.0 de Google optimiza la inferencia de IA a gran escala en Kubernetes, resolviendo los problemas de equilibrado de carga para cargas de trabajo de agentes autoalojados y aprendizaje por refuerzo.
La nueva herramienta de simulación generativa de NVIDIA transforma la formación en cirugía clínica al crear escenarios realistas impulsados por IA que mejoran la educación médica y la pericia quirúrgica.