#Agent

Google Releases Gemini 4 Hydrogen, Claiming 2.5x Speed Over Gemini 3.8 Flash

Google released Gemini 4 Hydrogen, a closed-weights model from Google DeepMind positioned below Gemini 4 Carbon and Argon as a daily driver for coding and agentic workflows. It offers a 1,048,576-token unified window, four thinking levels with high as the default, and API pricing of $1.50/$7.50 per 1M input/output tokens, flat across the full window. Google reports 57% on Terminal-Bench 4.1 versus 18.2% for Gemini 3.8 Flash and 51 on the Artificial Analysis Intelligence Index, but the model ranks only #5 on Agent Arena; pairing it with Carbon in Antigravity used 24% less quota and finished 19% faster than Carbon alone.

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, 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”

Fish Audio Releases Live 3, a Full-Duplex Voice Model That Reacts in 186 ms

Fish Audio released Live 3, a full-duplex voice model for real-time voice agents that keeps listening while it speaks and learns turn-taking from real human conversations instead of hand-tuned pipeline rules. Fish Audio says it can decide to respond in as little as 186 ms for short reactions like "mm-hmm," though that figure is a floor for small reactions rather than time-to-first-audio for a full reply, and no benchmark scores have been published. Live 3 runs on the Fish Agent Platform (public beta), the Fish Audio API and on-premises for enterprises, priced at $0.035 per minute until December 31, 2026 versus a $0.07 list rate, and supports English only.

Edge0 open-sources Edge0-145B and Edge0-120B on-device models with inference framework

Edge0 open-sourced Edge0-145B and Edge0-120B along with its unified inference framework and deployment tools, claiming Edge0-145B is the first open-source 145B-parameter on-device model. The company reports 29GB peak memory, 1,100+ tokens/s prefill, 80+ tokens/s decode and roughly 120W typical power on a compatible laptop, with 128K–256K context. It cites internal scores of 64.6 on DeepSWE 1.1, 86.52 on Terminal-Bench 2.1, 90.4 on GPQA-Diamond and 80.6 on IFBench, and says weights are on Hugging Face with day-one support for macOS, Windows and Linux.

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.

ElevenLabs releases Scribe v3 and Scribe v3 Live speech-to-text models

ElevenLabs released two speech-to-text models, Scribe v3 for pre-recorded audio and Scribe v3 Live for low-latency real-time use, both available through the ElevenAPI developer interface and the ElevenAgents platform. The company says Scribe v3 cuts word errors by about 25 percent versus its predecessor across 102 languages, and Scribe v3 Live uses conversational context to tell when a speaker has actually finished instead of relying on pause timers. Independent benchmarks from Artificial Analysis place Scribe v3 among the most accurate transcription models, trailing only an Alibaba preview model, but show it lagging on speed and priced at $3.33 per 1,000 minutes.

Tether AI Research Releases BotPsy: A 0.8B On-Device Agentic Model Built on Qwen3.5-0.8B

Tether AI Research released BotPsy, a 0.8B-parameter agentic model built on Qwen3.5-0.8B for on-device personal-assistant tool use. It roughly doubles its base model's agentic scores, with BFCL v4 rising from 23.4 to 44.0 and τ-bench from 23.2 to 51.5, and beats every model up to 1.2B parameters tested across six tool-use benchmarks. It also cuts unsafe responses to 21% from 36% and uses about 3× fewer reasoning tokens on GPQA Diamond.

Google DeepMind releases Gemini 4 Carbon for agentic coding, cybersecurity and scientific reasoning

Google DeepMind made Gemini 4 Carbon available to developers, enterprises, scientists and consumers, positioning it above Gemini 4 Argon for tasks that need deeper reasoning and more tokens. Carbon ranks #1 on CursorBench 4.0, Perplexity's WANDR and Zapier's AutomationBench at more than 2X lower cost per task than the next best external model, and is priced at $2 per million input tokens and $10 per million output tokens, the same introductory price as Argon. Google also previewed /swarm in Google Antigravity, which turns a single prompt into a coordinated team of agents, and reported that Carbon blocked 99% of attack trajectories in simulated cyberattack testing.

Review: Gemini 4 Carbon's coding outpaces its judgment

Every's hands-on review finds Gemini 4 Carbon, released today by Google DeepMind, is a much stronger coder than Gemini 4 Argon and can build better 3D objects and work for hours with little supervision. Testers disagree on how much supervision it needs, and the review says Anthropic's Fable 5.2, released a day earlier, has raised expectations that a flagship agent also pick the right work, respect limits and know when it is finished. Google gave Every early access and had no input on the review.

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.

Every review: Fable 5.2 can build a full product with little steering

Every reports that Fable 5.2, Anthropic's newest model, let Kieran Klaassen build a video editor combining Adobe Premiere, After Effects and Ableton-style features, with an AI agent running it, and Everycut went live three days after he started. The review says Fable 5.2 can take a whole product and keep building, testing and refining until it is usable daily, unlike Opus 5.5, which keeps the user checking its work, and Fable 5 and 5.1, which run large tasks for hours or days. Every calls it the first model it has tested that lets professionals build production-grade tools without reading the underlying code.

Meta brings specialized Muse and Meta AI models with Active Memory and Active Focus

Meta is rolling out specialized versions of its existing models, trained using Muse Wonder, to people who use Muse and Meta AI. The specialized models add Active Memory and Active Focus, and Meta reports the current Muse model scores 47% on its Personal Superintelligence Index versus 38% for the previous model, 90% versus 76% on the Active Memory eval, and a 67% improvement in completing everyday tasks where unexpected issues arise.

Thinking Machines releases Inkling 2 Small, scoring 31 on the Artificial Analysis Intelligence Index

Thinking Machines released Inkling 2 Small, a proprietary reasoning model that scores 31 on the Artificial Analysis Intelligence Index v5.0.1, 18 points above Inkling Small, with Apache 2.0 weights planned for a later, unconfirmed date. The 594B-parameter model (17B active) is available first through Tinker and Tinker Playground at $0.30/$1.00 per 1M input/output tokens, and it matches Gemini 3.8 Flash (high) while trailing Claude Sonnet 5.5 on coding and hallucination benchmarks. It averages about 150K output tokens per Intelligence Index task, roughly 4x Inkling Small, at $1.18 per task versus $0.20.

Mistral releases open weights for Mistral Large 4 (Le Chonk), a 1-trillion-parameter multimodal model

Mistral AI released the open weights of Mistral Large 4 (Le Chonk), a 1-trillion-parameter natively multimodal model that entered public preview earlier this month and was trained from scratch in Europe. The company says three additional weeks of reinforcement learning lifted it to the top of open-weight coding, with 41% on Terminal-Bench 4.0 and 48% on the Artificial Analysis Coding Agent Index while using 47% fewer tokens per task than GLM-5.3. It adds Low, Medium and High reasoning modes, scores 51% on the Artificial Analysis Cyber Index, and is priced at $0.70 input, $0.07 cached input and $2.10 output per million tokens under a modified MIT license.

Google DeepMind announces Gemini 4 Carbon, a frontier model focused on cybersecurity and long-horizon tasks

Google DeepMind announced Gemini 4 Carbon, its strongest model yet, with state-of-the-art cybersecurity and long-horizon capabilities, ranking #1 on the v5.0 Artificial Analysis Intelligence Index at 59. It scores 88.2% on SEC-bench Pro for finding vulnerabilities, 54.8% on ExploitGym for exploitation and 71.0% on CWE-bench for patching, and reaches #4 (1768) on WebDev Arena. Carbon launches at the same introductory price as Argon, $2 per million input tokens and $10 per million output tokens, and is first available to a subset of cyber defenders through the Fairwind Program before wider release.

Gemini 4 Carbon tops Artificial Analysis Intelligence Index v5, ranks second on Coding Agent Index

Google's Gemini 4 Carbon is the new No. 5 on the Artificial Analysis Coding Agent Index. 5 (max, 62), and it leads 4 of the 9 Index evaluations including HLE-Diamond and Terminal-Bench-Science. 1 rises from 4% to 11%; the model is being rolled out to selected users and is not yet publicly available.

PFN releases PLaMo 3.1 Prime with Web search and Deep Research

Preferred Networks began offering PLaMo 3.1 Prime on October 27, 2026, adding Web search and a Deep Research feature that autonomously plans, collects, analyzes and writes source-cited reports over several minutes to tens of minutes. Deep Research also runs on-premises and can connect to internal document search systems, while the company reports an average ~15% score improvement across eight hallucination-related benchmarks including AA Omniscience versus PLaMo 3.0 Prime. PFN also cites 1.7x faster processing via speculative decoding and a 90% reduction in output tokens in non-reasoning mode, and launched a free PLaMo API plan plus a PLaMo Chat campaign offering Deep Research twice daily until November 30, 2026.

inclusionAI releases Ling-3.0-flash-Sante, a 124B healthcare-specialized MoE model

inclusionAI released Ling-3.0-flash-Sante, a healthcare-specialized model built on Ling-3.0-flash with 124B total parameters and 5.1B active parameters per token, with weights on Hugging Face and ModelScope. It uses a hybrid-linear MoE architecture combining Kimi Delta Attention, gated MLA and sparse MoE layers, trains to 256K tokens, and enables thinking mode by default with tool calling through compatible serving frameworks. The company labels it a research model, not a medical device, and says outputs must not replace professional medical advice, diagnosis or treatment.

Mistral releases Voxtral 3 speech models and voice agent SDK

Mistral released Voxtral 3, a family of speech-to-text and text-to-speech models plus a voice agent SDK built on LiveKit that streams listening, reasoning, tool calling and speaking in one pipeline. Voxtral Small Transcribe 3 covers 50+ languages with diarization and context biasing, Voxtral Mini TTS 3 supports 35+ languages and 50+ dialects, and the stack is available through Mistral's EU-hosted API or self-deployment. Pricing starts at $0.0035/min for batch transcription and $13 per 1M characters for TTS.

MiniMax releases M3.1 Flash, claiming 70% lower cost and nearly 3x faster inference

MiniMax officially rolled out MiniMax M3.1 Flash, a next-generation Flash model that it says cuts usage cost by 70% versus its predecessor and makes online inference nearly three times faster. The company reports 73.7% on DeepSWE v1.1, 76.2 on BankerToolBench, 61.7 on OSWorld V2, 29.6 on Agents' Last Exam and 82% on CyberGym, with output speeds of 200+ TPS and a median of 170+ TPS. Pricing is $0.15 per 1M input tokens, $0.60 per 1M output tokens and $0.015 per 1M cached input tokens, and the model is available through the MiniMax API, M Plan and MiniMax Code, with weights to be released later.

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.

Meta releases Muse Wonder and Muse Wonder Full frontier models for personal and professional agents

Meta introduced the Muse Wonder series, comprising Muse Wonder and Muse Wonder Full, which it says delivers state-of-the-art performance on personal and professional agentic tasks, memory and coding. Muse Wonder is available today through the Meta Model API and Muse Code, while Muse Wonder Full is available today for Muse Code subscribers. The models pair Active Memory, which carries context across conversations and retrieves task-relevant details, with Active Focus, which sustains work on long-running goals and checks in before important actions; Meta says both were trained at its gigawatt-scale Prometheus data center.

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.

Anthropic releases Claude Sonnet 5.5 with unchanged per-token pricing and 30% faster output

Anthropic released Claude Sonnet 5.5, the second model in the Claude 5.5 family after Opus 5.5, which the company says is smarter, more efficient and 30% faster than Sonnet 5 at the same per-token price. Because it typically needs fewer tokens per task, Anthropic says most work costs up to 30% less, and the model ships with a native 1M-token context window, 128k max output and thinking on by default. The release guide lists breaking changes for Sonnet 5 users, including replacing disabled thinking with the between_tools setting, swapping forced tool_choice for auto plus strict tools, and moving computer use to the computer_toolset_20260801 toolset.

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.