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Saturday

  1. Shao MengSelected

    Moonshot AI Releases Kimi K3.1 Pro: A 3T-Class Open-Weight Model Strengthening Coding, 3D and Finance Agents

    Moonshot AI has released Kimi K3.1 Pro, calling it the strongest 3T-class open-weight model, featuring a 1M-token context and multimodal capabilities. It simultaneously launched a Highspeed version that is about 2–3× faster, with full weights planned to be open-sourced before 2026-11-04. The model scores 65.7% on Terminal-Bench 4.0, above GPT-6 Astra's 58.2% and slightly below Claude Opus 5.5's 66.4%; it scores 92.7% on BenchCAD with tools and 64.4% on OfficeQA Pro v2. The open-weight version removes cyber-related data from both mid-training and RL, and its xdev-bench-hard score drops from 90.5% to 81.0%.

    Moonshot AI has released Kimi K3.1 Pro, calling it the strongest 3T-class open-weight model, featuring a 1M-token context and multimodal capabilities. It simultaneously launched a Highspeed version that is about 2–3× faster, with full weights planned to be open-sourced before 2026-11-04. The model scores 65.7% on Terminal-Bench 4.0, above GPT-6 Astra's 58.2% and slightly below Claude Opus 5.5's 66.4%; it scores 92.7% on BenchCAD with tools and 64.4% on OfficeQA Pro v2. The open-weight version re

    Matched fieldsSummary

    #hot-signal#Model releases#Agents#English text is being prepared.
  2. Kimi.aiSelected

    Kimi releases K3.1 Pro open-weight model, 2.8T total parameters, 1M token context

    Kimi releases K3.1 Pro, its strongest 3T-class open-weight model, using a MoE architecture with 2.8T total parameters and 104B activated parameters, supporting a 1M token context and multimodality. Officially it is claimed to match the strongest closed-source models on coding, agent, and knowledge benchmarks, and to support full-project game development in Unreal, Unity, and Godot as well as parametric 3D modeling in Blender and CAD, with hallucinations in financial scenarios reduced by 65%. K3.1 Pro and K3.1 Pro Highspeed, which is 2–3 times faster, are already live on the Kimi official website, Kimi Work, Kimi Code, and the Kimi API, with open weights planned for release on November 4, 2026.

    Kimi releases K3.1 Pro, its strongest 3T-class open-weight model, using a MoE architecture with 2.8T total parameters and 104B activated parameters, supporting a 1M token context and multimodality. Officially it is claimed to match the strongest closed-source models on coding, agent, and knowledge benchmarks, and to support full-project game development in Unreal, Unity, and Godot as well as parametric 3D modeling in Blender and CAD, with hallucinations in financial scenarios reduced by 65%. K3.

    Matched fieldsSummary、Body

    #company#Model releases#Open-source ecosystem#Agents
  3. Ayi AI NotesSelected

    Kimi Releases 2.8T Flagship K3.1 Pro, Tops SWE Marathon with a Score of 51.7

    Kimi has released its multimodal flagship model K3.1 Pro, with 2.8T total parameters and 104B active parameters (MoE), and a standard 1M-token context window. The author says it tops the SWE Marathon leaderboard with a score of 51.7, overtaking Opus 5.5 and GPT-6 Astra, and the company has also promised to open-source the full weights by November 4. K3.1 Pro and K3.1 Pro Highspeed, which is 2–3× faster, are now live on the Kimi web app, Kimi Work, Kimi Code and the Kimi API.

    Matched fieldsSummary、Body

    #hot-signal#Model releases#Agents#English text is being prepared.
  4. Testing CatalogSelected

    Kimi Releases K3.1 Pro: A 2.8T Total-Parameter, 104B Active MoE Open-Source Model

    Kimi officially released Kimi K3.1 Pro, a 3T-class open-weight model built on a MoE architecture with 2.8T total parameters and 104B active parameters, supporting a 1M-token context and multimodality. The company says it performs comparably to leading models such as Opus 5.5 and GPT-6 Astra on benchmarks while costing significantly less, and that hallucinations in financial modeling scenarios dropped by 65%. Kimi K3.1 Pro and K3.1 Pro Highspeed, which is 2–3× faster, are now available on the Kimi platform, Kimi Work, Kimi Code, and the Kimi API, with open weights to be released on November 4.

    Kimi officially released Kimi K3.1 Pro, a 3T-class open-weight model built on a MoE architecture with 2.8T total parameters and 104B active parameters, supporting a 1M-token context and multimodality. The company says it performs comparably to leading models such as Opus 5.5 and GPT-6 Astra on benchmarks while costing significantly less, and that hallucinations in financial modeling scenarios dropped by 65%. Kimi K3.1 Pro and K3.1 Pro Highspeed, which is 2–3× faster, are now available on the Kim

    Matched fieldsSummary

    #hot-signal#Model releases#Open-source ecosystem#Multimodal
  5. OpenRouterSelected

    Kimi K3.1 Pro launches on OpenRouter, 1M context, 2.8T parameter MoE open-weight model

    Kimi K3.1 Pro is now live on OpenRouter; it is a 1M-context multimodal open-weight model post-trained from Kimi K3, targeting long-horizon coding and agent tasks, priced at $2 per million input tokens and $10 per million output tokens. According to Kimi_Moonshot, the model is a 3T-class open-weight model with 2.8T total parameters and 104B activated parameters (MoE), excelling at full-project coding in Unreal, Unity, and Godot and at parametric 3D modeling in Blender and CAD, with financial modeling hallucinations reduced by 65%, and it is said to match the strongest closed-source models on coding, agent, and knowledge benchmarks. Kimi K3.1 Pro and the 2–3x faster K3.1 Pro Highspeed are now available on the Kimi platform, Kimi Work, Kimi Code, and Kimi API, with weights to be opened on November 4, 2026.

    Kimi K3.1 Pro is now live on OpenRouter; it is a 1M-context multimodal open-weight model post-trained from Kimi K3, targeting long-horizon coding and agent tasks, priced at $2 per million input tokens and $10 per million output tokens. According to Kimi Moonshot, the model is a 3T-class open-weight model with 2.8T total parameters and 104B activated parameters (MoE), excelling at full-project coding in Unreal, Unity, and Godot and at parametric 3D modeling in Blender and CAD, with financial mode

    Matched fieldsSummary、Body

    #hot-signal#Product updates#Open-source ecosystem#Agents
  6. ArenaSelected

    Kimi K3.1 Pro released, rises to 8th on Code Arena WebDev

    Moonshot AI released Kimi K3.1 Pro, which ranks 8th on Code Arena WebDev with 1,692 points, matching Claude Opus-5 (Max) while costing 70% less, and rising 9 places from Kimi K3 (Max)'s 17th. The model is priced at $2/$10 per million input/output tokens, with a median cost of $1.23 per task, 29% lower than Kimi K3, and its weights will be opened on November 4. Kimi officially calls it a 3T-class open-weight model, featuring a 1M-token context, a 2.8T total parameter/104B active parameter MoE multimodal architecture, and a 65% reduction in hallucination rate.

    Moonshot AI released Kimi K3.1 Pro, which ranks 8th on Code Arena WebDev with 1,692 points, matching Claude Opus-5 (Max) while costing 70% less, and rising 9 places from Kimi K3 (Max)'s 17th. The model is priced at $2/$10 per million input/output tokens, with a median cost of $1.23 per task, 29% lower than Kimi K3, and its weights will be opened on November 4. Kimi officially calls it a 3T-class open-weight model, featuring a 1M-token context, a 2.8T total parameter/104B active parameter MoE mul

    Matched fieldsSummary

    #editorial#Evaluations / benchmarks#English text is being prepared.#Open-source ecosystem
  7. Rohan PaulSelected

    Moonshot AI Releases Kimi K3.1 Pro, Scoring 65.7% on Terminal-Bench 4.0

    Moonshot AI has released Kimi K3.1 Pro, its first updated version, which scores 65.7% on Terminal-Bench 4.0—0.7 points lower than Claude Opus 5.5 and 7.5 points higher than GPT-6 Astra—while its average cost per task is lower than both. The model is a 3T-class open-weight MoE model with 2.8T total parameters and 104B active parameters, supports a 1M-token context and multimodality; in a tool-augmented hallucination stress test, its false-claim rate dropped from 23.4% in K3 to 8.3%. K3.1 Pro and the 2–3× faster Highspeed version are now available in the Kimi app and API, with full weights scheduled for release on November 4.

    Moonshot AI has released Kimi K3.1 Pro, its first updated version, which scores 65.7% on Terminal-Bench 4.0—0.7 points lower than Claude Opus 5.5 and 7.5 points higher than GPT-6 Astra—while its average cost per task is lower than both. The model is a 3T-class open-weight MoE model with 2.8T total parameters and 104B active parameters, supports a 1M-token context and multimodality; in a tool-augmented hallucination stress test, its false-claim rate dropped from 23.4% in K3 to 8.3%. K3.1 Pro and

    Matched fieldsSummary

    #ai-news#Model releases#Agents#Reasoning
  8. Google Research

    Google Research uses open models such as MedGemma to advance tuberculosis screening implementation

    Google Research and Google for Health presented progress in bringing health AI research into clinical practice at IDWeek 2026, including open-source chest X-ray embeddings and MedSigLIP, as well as open-weight multimodal models MedGemma at 4B and 27B parameter scales; the latter can run locally offline and has accumulated over 10 million downloads. Tools developed by partner Nexus AI based on these algorithms have obtained EU CE MDR class IIb certification and ISO 13485 certification, and have been deployed in about 40 medical institutions across six countries including South Africa, Sierra Leone, and Côte d'Ivoire; more than 90 clinicians have evaluated over 25,000 chest X-rays, reducing turnaround time from weeks to under 45 seconds. Indonesia's Ministry of Health is fine-tuning MedGemma and MedSigLIP with domestic chest X-rays to support a screening target of 50 million people per year on local infrastructure.

    A Nexus AI software interface displaying a chest X-ray with three circled lung areas marked as "TB Suggestive" and "FIR".

    Google Research and Google for Health presented progress in bringing health AI research into clinical practice at IDWeek 2026, including open-source chest X-ray embeddings and MedSigLIP, as well as open-weight multimodal models MedGemma at 4B and 27B parameter scales; the latter can run locally offline and has accumulated over 10 million downloads. Tools developed by partner Nexus AI based on these algorithms have obtained EU CE MDR class IIb certification and ISO 13485 certification, and have b

    Matched fieldsSummary、Body

    #lab#Industry news#Multimodal#Open-source ecosystem

Friday

  1. MiniMax

    MiniMax M3.1 Flash Gets Day-One Support from SGLang and Miles

    MiniMax announced that M3.1 Flash is ready from inference to RL, with SGLang and Miles providing support on the very first day of release. According to SGLang, the model achieves over 1K tok/s for bs1 decode on SGLang, supports NVIDIA Blackwell and Hopper (GB300/B300/B200/H200) as well as AMD Instinct MI350X and MI355X, and supports MiniMax's DSpark draft model along with HiCache and multimodal input.

    Matched fieldsSummary

    #hot-signal#Product updates#Reasoning#English text is being prepared.

Wednesday

  1. Kling AI

    Kling 4.0 launches on the fal platform

    Kling 4.0 is now live on the fal platform. The official description calls it a next-generation native multimodal video model, with improvements in stability for complex motion, consistency of character and scene references, and precision of cinematic narrative control. It is currently available only through the fal UX and fal Agent, with API access coming soon.

    Matched fieldsSummary

    #hot-signal#Product updates#Video#English text is being prepared.
  2. fal

    Kling 4.0 Launches on the fal Platform

    Kling 4.0 is now available on the fal platform. It is a new-generation native multimodal video model that improves the stability of complex motion, reference consistency for characters and scenes, and control precision for cinematic storytelling. It is currently available only through fal UX and fal Agent, with API access coming soon.

    Matched fieldsSummary、Body

    #hot-signal#Product updates#Video#Multimodal
  3. Unite.AI

    NVIDIA Releases Nemotron 3.5 Series of Open Models, Covering Embedding, Reranking, and Document Parsing

    On October 21, 2026, NVIDIA expanded the Nemotron Retriever series, launching Nemotron 3.5 Embed (8B BF16, 1B BF16, 1B NVFP4), Nemotron 3.5 Rerank 8B BF16, and the 631-million-parameter Nemotron 3.5 Parse, aimed at multimodal retrieval and document processing for enterprise agent workflows. NVIDIA says that adding Rerank to the agent retrieval pipeline reduces the token cost per query by about 14% (with the 8B embedding model) and 13% (with the 1B model), respectively, while also improving retrieval accuracy. Parse scores 75.20 on ParseBench and processes 10.5 pages per second on a single H100; the open weights, data, and training recipes have been released on Hugging Face and can be deployed via NVIDIA NIM as well as Baseten, DeepInfra, and FriendliAI.

    On October 21, 2026, NVIDIA expanded the Nemotron Retriever series, launching Nemotron 3.5 Embed (8B BF16, 1B BF16, 1B NVFP4), Nemotron 3.5 Rerank 8B BF16, and the 631-million-parameter Nemotron 3.5 Parse, aimed at multimodal retrieval and document processing for enterprise agent workflows. NVIDIA says that adding Rerank to the agent retrieval pipeline reduces the token cost per query by about 14% (with the 8B embedding model) and 13% (with the 1B model), respectively, while also improving retri

    Matched fieldsSummary、Body

    #hot-signal#Model releases#RAG#Multimodal

Tuesday

Monday