#多模态

inclusionAI Releases Duo-1.0-distill-quant FP8 Weights for Full-Duplex Interaction

inclusionAI released Duo-1.0-distill-quant, FP8-quantized four-step distilled Realization weights for the Duo 1.0 full-duplex multimodal world model. The weights overlay onto the inclusionAI/Duo-1.0-causal-base bundle, which supplies the shared Intent, codecs, speech models and Realization backbone, and require a runtime supporting the packaged ModelOpt FP8 format. The weights were initialized from MiniMax H3 and then trained by the Duo team.

inclusionAI Releases Duo-1.0-distill Four-Step Distilled Weights for Duo 1.0

inclusionAI has released Duo-1.0-distill, the four-step distilled Realization weights for Duo 1.0, a full-duplex multimodal world model for real-time audio-visual interaction. The weights ship as a PyTorch Distributed Checkpoint overlay on the inclusionAI/Duo-1.0-causal-base bundle, using an Euler sampler with 4 steps and flow shift 5.0. The weights were initialized from MiniMax H3 and then trained by the Duo team.

SingVL-4B GGUF Released: GGUF Build of Sing VL S1 4B Multimodal Decision Model on Qwen3.5

Sing VL S1, a 4B-parameter multimodal decision model built on Qwen3.5, now has a GGUF release for llama.cpp CPU inference. It ships in BF16 (8.42 GB), Q8_0 (4.48 GB) and Q4_K_M (2.71 GB) decoders plus a shared F16 vision projector, with a default 4096-token context. GGUF validation covers 9 examples and 11 questions; full public scores for the GGUF variants are still pending.

Google releases Gemini 4 Hydrogen, a fast execution model for coding and agentic workflows

Google introduced Gemini 4 Hydrogen, a model built for rapid execution in everyday coding and agentic workflows, rolling out globally across the Gemini API, AI Studio, Gemini for business, the Gemini app, Google Search, and Google Antigravity.

Same event; the pick carries“Google DeepMind releases Gemini 4 Hydrogen, a faster Gemini 4 model for coding and agentic workflows”

Google releases Gemini 4 Hydrogen, scoring above the larger Argon four weeks after Argon's launch

Google has released Gemini 4 Hydrogen, a fast general-purpose model for developers and everyday users, priced at $1.50 per million input tokens and $7.50 per million output tokens with a one-million-token context window. Google says Hydrogen scores 51 on the Artificial Analysis Intelligence Index at its default high thinking level and 54 at the new xhigh level, versus 50 for the larger Gemini 4 Argon. Independent testing by Artificial Analysis puts Hydrogen roughly on par with Claude Sonnet 5.5 at maximum thinking, but weaker on agentic terminal work, scoring 54 percent on Terminal-Bench 4.0 against 64 percent for Claude Sonnet 5.5.

Same event; the pick carries“Google DeepMind releases Gemini 4 Hydrogen, a faster Gemini 4 model for coding and agentic workflows”

Google DeepMind releases Gemini 4 Hydrogen, a faster Gemini 4 model for coding and agentic workflows

Google DeepMind introduced Gemini 4 Hydrogen, a Gemini 4 model built for fast everyday coding and agentic execution rather than the long-horizon reasoning of Gemini 4 Carbon and Argon. It scores 51 on the Artificial Analysis Intelligence Index at its default high thinking level versus 50 for Argon, reaches 54 at a new xhigh setting, and is priced flat at $1.50 per million input tokens and $7.50 per million output tokens across its 1 million-token context window. Google also launched Orchestrator in Google Antigravity, which switches between models like Carbon and Hydrogen within a session and reached near-Carbon task success on Terminal-Bench 4.0 while using 24% less quota and finishing 19% faster.

Google releases Gemini Decision 1 decision model API with multi-modal input

Google has released Gemini Decision 1, a decision model API that supports yes/no, choice and rubric-scoring questions, joining Jev and OpenAI in the decision model category. It is priced at 10 cents per million input tokens with free output, and unlike Jev's text-only input it accepts text, images, audio, video and PDFs. In a test on a robot video, Gemini returned probabilities such as 0.9234 for robot and 0.0498 for dragon instead of the 1.0 or 0.0 that Jev and OpenAI return.

Google DeepMind launches Gemini Decisions API and Gemini Decision 1 model

Google DeepMind introduced the Gemini Decisions API, built for closed-ended decisions such as yes/no questions, multiple choice, and scale-based ratings, with the underlying Gemini Decision 1 model processing up to 1,000 independent questions in a single pass across text, images, audio, video, and PDFs. It returns probabilities with confidence values in 150 to 250 milliseconds and costs $0.10 per million input tokens with no output-token charge, which Google says is about five times cheaper than Gemini 3.5 Flash-Lite. The API is available now through Google AI Studio, with integrations for OpenRouter, LangChain, and Vercel rolling out, and Google says it already uses it internally for model routing in Gemini Notebook, design prototyping in Stitch, and safety checks in Google Search.

Same event; the pick carries“Google launches Gemini Decisions API and Gemini Decision 1 model for fast closed-set decisions”

MBZUAI's IFM Releases PAN 2, an Open 29B World Model for Physical AI

MBZUAI's Institute of Foundation Models released PAN 2, a 29B-parameter open world model that takes a scene plus a text action and simulates the outcome as video, with weights, training code and inference code under Apache 2.0. It pairs an LLM planner that writes structured JSON world states with a 29B MMDiT diffusion transformer conditioned by a frozen Qwen3.5-9B encoder, and IFM reports 34.0% full-plan completion on 100 multi-step tasks, ahead of MiniMax-H3 at 32.50%, while trailing MiniMax-H3 on single actions and human preference (Elo 1595 vs 1620). IFM's ablation shows removing the LLM planner drops full-plan completion to 0.0% and step completion from 75.1% to 8.7%.

inclusionAI Releases SingVL S1 4B Multimodal Decision Model on Qwen3.5

inclusionAI released SingVL S1, a 4B multimodal decision model built on Qwen3.5 that answers named questions over text or JSON and up to four images in a single Transformers forward pass. It supports choice, yes/no (noul) and 2–10 level score questions, scoring 73.94 on the Public Vision Index and 69.76 on the Public Text Index. The model ships with Python inference code and a FastAPI HTTP server.

Simon Willison tests Gemini 4 Carbon in Antigravity IDE as Google opens access to all users

Gemini 4 Carbon, announced two weeks ago for a subset of cyber defenders, is now available to everyone at $2/million input and $10/million output, with prices scheduled to double to $4/$20 on December 1st. Simon Willison tested it in Google's Antigravity IDE, generating a Three.js animation of a mech stomping through New York City using OpenStreetMap data, and noted that Google CEO Sundar Pichai highlighted Antigravity's /swarm feature building an open-world 3D game with Blender assets, WebGL shaders and Lyria music.

Same event; the pick carries“Google DeepMind releases Gemini 4 Carbon for agentic coding, cybersecurity and scientific reasoning”

inclusionAI Releases GGUF Weights for ZoomGain-9B

inclusionAI has released GGUF-format weights for ZoomGain-9B, converted with llama.cpp, covering the language model and the vision encoder/projector (mmproj) in FP16, Q8_0 and Q4_K_M. Image input requires pairing the language model with the matching mmproj file, and the repo documents CLI inference via llama-mtmd-cli and a web chat setup via llama-server. A llama.cpp build with Qwen3.5 multimodal support is required, and quantization may affect accuracy.

Anthropic releases Claude Fable 5.2 for ambitious technical work

Anthropic released Claude Fable 5.2, a model designed for the most daunting technical challenges and ambitious open-ended projects, with improvements in coding, reasoning and vision. The company recommends starting with Opus 5.5 for most coding and knowledge tasks; Fable 5.2 is available today on Pro, Max, Team and Enterprise plans.

Meta releases Muse Ember, its smallest and most efficient model for agent tasks

Meta released Muse Ember, its smallest and most efficient model, aimed at the repetitive work agents do all day such as tool calls, coding subtasks, routing, extraction and long document parsing. Meta says it matches Claude Haiku 5.5's best score at about a third of the cost and beats every GPT-6 Luna setting, at $0.10 per million input tokens and $0.50 per million output tokens. It ships with a 1M-token context window, up to 512K output tokens, native text, image, audio and video input, and is available today on Muse Code and the Meta Model API as muse-ember-1.0.

Google DeepMind releases Gemini Omni 1.2 Flash with 15-second clips and 30% lower price

Google DeepMind rolled out Gemini Omni 1.2 Flash, now generally available across the Gemini API and Gemini for business, with native 15-second video generation, multi-turn conversational editing and stronger reference following. The price drops 30% from Gemini Omni 1.1 to $0.07 per second for 720p video, and users can draft at 360p or 720p before upscaling to 1080p or 4K. Clips can be set from 3 to 15 seconds in one-second increments, and outputs carry an imperceptible SynthID watermark.

Google releases Gemini Omni 1.2 Flash with 15-second clips and a 30% price cut

Google DeepMind rolled out Gemini Omni 1.2 Flash and made it generally available across the Gemini API and Gemini for business. The model generates and edits native clips up to 15 seconds, up from 10 seconds in Omni 1.1, supports multi-turn conversational editing, and holds character, product, and style references more consistently; 720p video now costs $0.07 per second, 30% less than Omni 1.1. Outputs carry a SynthID watermark, and the model is rolling out in Flow, Google Vids, the Gemini app, and Google Ads.

PFN releases PLaMo 3 Translate open-weight translation LLM for edge devices

Preferred Networks released PLaMo 3 Translate, a translation-specialized LLM used in its PLaMo Translation service, as an open-weight model on Hugging Face on November 5. 8B, 2B, 8B and 31B) and supports 53 languages including Japanese and English, running on smartphones, laptops and internal servers without a cloud connection.

Ai2 releases OlmoEarth v1.3 satellite embedding model with global precomputed embeddings and a browser explorer

Ai2 released OlmoEarth v1.3, a satellite embedding model that turns a year of Sentinel-1, Sentinel-2 and Landsat time series into one 128-dimensional vector per 10-meter pixel, with the first 64 dimensions usable as a smaller embedding. Ai2 precomputed annual embeddings for the whole planet and published them on Source Cooperative, and its 128-dimensional embeddings scored highest on 20 of 27 evaluations against AlphaEarth Foundations and Tessera v2, including a new tropical crop benchmark called PLANTEUR. The release also includes the OlmoEarth Embeddings Explorer, a no-sign-in browser tool for comparing years, finding similar places and training small classifiers on the spot, with 2025, 2024 and 2023 global and 2017 to 2022 covering select countries; weights, pretraining data and code are open.

Meta releases Muse Spark Open, an 816B open-weight multimodal model that runs on two Mac Studios

Meta released the weights for Muse Spark Open, an open-weight natively multimodal model with 816B total parameters (42B active) and a 1M-token context window, positioned for coding and agentic work. It is the first model in its class to run on a desktop using two Apple M5 Ultra Mac Studios with the MLX framework, and it is available through API partners including Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter and Together AI, with weights on Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden and Microsoft Foundry Models. Meta says the model ships with auto-tunable Helion GPU kernels in SGLang, delivering 1.03-3.95x kernel-level speedups on NVIDIA and AMD GPUs and a 3% end-to-end speedup on AMD MI350.

Mistral launches Mistral Large 4 preview, a 1T-parameter multimodal model with weights due this month

Mistral has launched a public preview of Mistral Large 4, a 1 trillion-parameter natively multimodal model with 52 billion active parameters, available now through the Mistral Studio API. Mistral says the model is competitive with the strongest open-source models and outperforms other open-weight models from the US and Europe, scoring 82% on an Artificial Analysis Cyber Index vulnerability reproduction and patching test and 49.8% on its combined Coding Agent Index. The weights are set to be released by the end of the month, after red-teaming with cybersecurity partners and authorities; Mistral says the model was trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in its own European datacenters.

MiniMax releases Music 3.0, an open-weights music generation model for full songs up to five minutes

MiniMax introduced Music 3.0, an open-weights music generation model that composes, arranges and produces a complete song of up to five minutes from a creative concept and optional lyrics in a single generation. The pipeline pairs an 8B Global LLM initialized from Qwen3.5-8B with a 0.6B Local LLM, fuses their continuous hidden states to condition a 2.4B flow-matching module and a 123M Flow-VAE, and uses eight-layer RVQ plus Structured Captions that describe emotion, instrumentation and vocal delivery over time. MiniMax says this yields more accurate interpretation of creative intent, fuller arrangements and clearer, more natural vocals.

MiniMax launches H3, a general-purpose multimodal generation model with native stereo video at 2K

MiniMax launched H3, a general-purpose multimodal generation model that understands text, images, video and audio as a unified context and generates video with native stereo sound up to 15 seconds at 2K resolution. The company says 2K output costs less than a third of mainstream models per second, and it plans to open the model weights in the coming days subject to applicable laws and regulations. H3 is built on Contextual Omni Representation, H3-VAE, H3-Omni Transformer and In-Context Regeneration, and MiniMax says a full technical report is coming soon.

MiniMax M3 released with 1M context, frontier coding and native multimodality

MiniMax released M3, an open-weight model it says is the first to combine frontier coding and agentic performance, a 1M-token context window, and native image and video input. M3 uses MiniMax's new MSA sparse attention architecture, which the company reports cuts per-token compute at 1M context to 1/20 of the previous generation and speeds prefill by more than 9x and decoding by more than 15x. It scores 59.0% on SWE-Bench Pro, 66.0% on Terminal-Bench 2.1 and 74.2% on MCP Atlas, and is available through MiniMax Code, the Token Plan and the API, with weights and a technical report promised within 10 days.