New ModelsOpen weights
Kimi K3 open weights
Moonshot releases Kimi K3's full open-weight checkpoints — 2.8T parameters, the largest open model ever
Two weeks after the API launch, Moonshot published Kimi K3's full checkpoints, model code, and technical report: 2.8T total parameters with 104B active (16 of 896 experts), native vision, a 1M-token context window, and roughly 1.56TB of MXFP4 weights. The report details KDA linear attention, attention residuals, NoPE, and a claimed 2.5x scaling-efficiency jump over K2. On the show, Elie Bakouch called it public building blocks scaled superbly, and Baseten's Philip Kiely described serving it day-zero on eight GB300s. The custom license requires branding above 100M MAU or $20M monthly revenue and a signed agreement for model-as-a-service providers — every provider lists the identical $3/$15 price.
2.8T / 104B total / active parameters1.56TB MXFP4 weights — eight GB300s to serve2.5x claimed scaling efficiency over Kimi K2
New ModelsOpen weights
Kimi K3
Moonshot's Kimi K3 — 2.8T parameters — launches its API live mid-show, with full open weights following July 27
Kimi K3 went from rumor to released API in the middle of the ThursdAI broadcast: a 2.8-trillion-parameter MoE (16 of 896 experts active, ~60-75B per LDJ's estimate) with Kimi Delta Attention and attention residuals for roughly 2.5x the scaling efficiency of K2 (Moonshot's technical report later confirmed ~104B active), native vision, a 1M-token context window, and pricing around half of Opus 4.8 or GPT-5.6 Sol. It debuted #1 on the Frontend Code Arena above Claude Fable 5 and #3 on Artificial Analysis's Intelligence Index; demand forced Moonshot to pause new API subscriptions. The full weights shipped July 27 under a bespoke open-weight (not OSI) Kimi K3 license — the first open 3T-class model.
2.8T total parameters (16 of 896 experts active)1M context window (tokens)#3 / #1 AA Intelligence Index / Frontend Code Arena debut
