Kimi K3 is a large language model reported to have 2.8T parameters and roughly 1.45 TB of expert weights. Recent coverage highlights commercial and engineering use: Moonshot plans to leverage Kimi K3 toward $2 billion annualized sales, Cognition post‑trained it to produce the SWE‑2 coding model, and users measured it running ~1 token/s on an M5 Max MacBook Pro with four SSDs.
Report: China narrows AI gap with U.S.The report says China has been rapidly closing its AI lead over the U.S. in model capabilities and research despite U.S. export restrictions on advanced chips and equipment. Experts describe the U.S.–China model gap as narrowing, noting Chinese efforts such as Moonshot AI’s Kimi K3.
Open Chinese models narrow gap with frontier modelsA Mozilla report finds that the performance gap between Chinese open-weight models and US frontier models has narrowed to roughly 4.4 months. It highlights Moonshot AI's Kimi K3 scoring three points behind Anthropic's Fable 5 on a composite index while costing about 30% as much, suggesting open models are preferable for most routine workloads.
CrofAI exposed as OpenRouter wrapper with big markupsResearchers allege CrofAI (NahCrofAI) was an OpenRouter wrapper that silently routed requests to cheaper/weaker models while charging up to 20x markups. The operator denied then backtracked and ultimately wiped the service; examples include routing Kimi K3 requests to GLM models, per the exposé.
Do tech reports matter for PhD appsA Reddit post asks whether tech reports for large models (not arXiv papers) — e.g. Kimi K3, DeepSeek, Gemini, Mistral Leanstral — carry similar weight to a first‑author A* paper in PhD applications, seeking community input.
Terminal Bench v4 ranks GLM-5.3 topA shared Terminal Bench v4 ranking shows GLM-5.3 leading at 41.9%, followed by GLM-5.3-Flash (32.8%) and DSV4.1-Flash (26.8%). Qwen3.8-27B is the only smaller model with notable results. The list reflects community perceptions of open vs closed models.
Moonshot Aims for $2B Annualized Sales in 2026Moonshot AI aims to lift annualized revenue to $2 billion by year-end leveraging its Kimi K3 model, intensifying competition with peers like Anthropic and Z.AI.
Cognition Unveils SWE-2 Coding ModelCognition launched SWE-2, a coding model scoring 50.0 on FrontierCode 1.1 Main—within one point of Fable 5.1 while 64% cheaper; it was post-trained from the 2.8T-parameter Kimi K3 and uses single-run RL across reasoning levels to improve cost-performance.
Kimi K3 runs at ~1 token/s on MacBook Pro with 4 SSDsA report shows Kimi K3 (2.8T parameters, 1.45 TB of expert weights) running on an M5 Max MacBook Pro (128 GB) with experts streamed from four SSDs; a 512-token answer achieved ~1.00 token/s steady decode. Measurement date is 2026-09-08. Related projects mentioned are Deltafin and ARGODRIVE.
The A.I. Daily