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 released Gemini 4 Hydrogen, the lowest-cost model in the Gemini 4 family, priced at $1.50 per million input tokens, $0.075 for cached input and $7.50 per million output tokens. Simon Willison notes that Carbon and Argon both cost $2.00/$0.10/$10.00 until Nov 30, 2026 and double from Dec 1, 2026, so Hydrogen is currently only about 25% cheaper than its larger siblings, a gap that widens to roughly 75% after the increase. Hydrogen is also the new free tier model and will power AI features in Google Search.
Same event; the pick carries“Google DeepMind releases Gemini 4 Hydrogen, a faster Gemini 4 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”
Cloudflare Workers AI has released Clairic in preview, a strongly typed extraction model that pulls, computes, estimates and forecasts values from inputs and returns confidence scores and probabilities for each output. The model has a 65,536-token context window, supports vision, offers zero data retention and is priced at $0.10 per M input tokens. In the documented invoice example, Clairic extracts fields such as invoice number, due date and total, returns null with p_null 1 for a missing PO number, and gives a median of 30 with a p10-p90 interval of 21 to 38 for days_to_pay.
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.
Why it matters: Edge0 open-sources a 145B on-device model that reports 29GB peak memory and 80+ tokens/s decode, offering a concrete data point on how far local inference can scale.
Google released Gemini 4 Hydrogen, a new model it says gives more helpful answers to complex questions, including questions with multiple parts, hard-to-understand typos, or highly specific requests. It is available starting today in Search, the Gemini app, all tiers of Google AI Studio, the Gemini API, Gemini for business, and Google Antigravity for AI Pro and Ultra subscribers, and Google Search VP Robby Stein says it is now the default model in AI Mode and is used for the hardest questions in AI Overviews.
Same event; the pick carries“Google DeepMind releases Gemini 4 Hydrogen, a faster Gemini 4 model for coding and agentic workflows”
Google has released Gemini 4 Hydrogen, which replaces Gemini 3.8 Flash across the company's products with immediate access rather than the staged, paid-tier-only rollout used for its recent Argon and Carbon releases. Google says Hydrogen runs at one of four thinking levels (low, medium, high, and a new xhigh) and is 2.5 times faster than Flash 3.8 even in high mode, while cutting fabricated claims by more than 5x. It also more than triples 3.8 Flash's Terminal-Bench score and reaches 54 on the Artificial Analysis Intelligence Index in xhigh mode, versus 50 for Argon.
Same event; the pick carries“Google DeepMind releases Gemini 4 Hydrogen, a faster Gemini 4 model for coding and agentic workflows”
Google released Gemini 4 Hydrogen, a fast-execution model it calls its biggest Search improvement yet, and is rolling the update out to all Search users globally. Google says Hydrogen completes tasks 2.5 times faster than Gemini 3.8 Flash and beats the more powerful Gemini 4 Argon on the Artificial Analysis Intelligence Index at its default high thinking level. In Search it improves intent understanding, typo handling, fan-out subqueries and visual responses in AI Mode, and it is also coming to AI Studio, the Gemini API, the Gemini App and Antigravity.
Same event; the pick carries“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.
Why it matters: Google's Gemini 4 Hydrogen targets fast everyday coding and agentic work, with flat $1.50/$7.50 per million token pricing across a 1M context window and a new Orchestrator that switches between Gemini 4 models.
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”
inclusionAI released LemonDecider 4B v3, a decision model fine-tuned from Qwen3.5-4B that returns choice, yes/no or ordinal probabilities via a single joint forward pass instead of generating answer tokens. It was trained on about 1.20M decision/QA rows and scored 61.67 on the public Decision Index 0.4 suite, with median request latency of 49.9 ms.
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.
Latent Space tested NaiveAI's open-weights Naive-N0.5-Flash, a coding model derived from Xiaomi's MiMo-V2.5 and paired with the NaiveRT inference engine, against DeepSeek V4.1 Flash 1017 and GLM 5.3 Flash. In the author's runs it peaked near 2,000 tokens per second and sustained about 1,000 during thinking-heavy tasks, with a median response time of 3.1 seconds versus 5.3 and 10.1 seconds for the two comparison models. On hard HLE questions it averaged about 108,500 completion tokens with thinking on, roughly nine times GLM 5.3 Flash and 11 times DeepSeek V4.1 Flash 1017, and the author notes the sample was 10 questions per difficulty level with no accuracy grading.
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.
Why it matters: Google's release notes detail how Gemini 4 Carbon trades higher token usage for deeper reasoning, ranking first on CursorBench 4.0, WANDR and AutomationBench at under half the per-task cost of the next external model.
Meta AI launched Muse Ember, described as its smallest and most efficient model, aimed at tool calls, coding subtasks, routing, extraction and long document parsing. For shorter prompts it is priced at $0.10 per million input tokens and $0.50 per million output, the same as GPT-6 Luna and Claude Haiku 5.5, while its long-prompt rates of $0.30 input and $1.50 output above 200k tokens place it between the two. Simon Willison notes the model sits on the Pareto line of Artificial Analysis's Intelligence Index vs. Cost per Task chart, and that its six reasoning levels produced pelican SVGs of comparable quality to Muse Wonder at 40x the price.
Anthropic released Claude Fable 5.2 and Claude Mythos 5.2, which top most independent leaderboards, including Artificial Analysis Intelligence Index v5 at 65 and SemiAnalysis GDPx at 16.4%. Pricing drops 20% to $8/$40 per 1M input/output tokens, but SemiAnalysis reports cost per GDPx task rose from $67.16 to $88.15 because the model writes hundreds to thousands of test cases and spawns subagents to check its work. Multiple evals show the max effort level underperforming high, and Fable is capped at 50% of a weekly subscription limit.
Why it matters: Anthropic releases Fable 5.2 and Mythos 5.2 with a 20% per-token price cut, yet independent evals show higher per-task cost and a max-effort setting that underperforms high.
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.
Why it matters: Claude Fable 5.2 targets ambitious open-ended technical work, with stated gains in coding, reasoning and vision, while Opus 5.5 remains the recommended default for most coding and knowledge tasks.
Meta's Muse Ember scores 36 on the Artificial Analysis Intelligence Index, matching Claude Haiku 5.5 (max) at $0.14 per task versus $0.41 for Claude Haiku 5.5. It stands out on AutomationBench-AA at 68.1% and scores 46 on the Coding Agent Index in Muse Code, ahead of Claude Haiku 5.5 in Claude Code (37) and GPT-6 Luna in Codex (31). Its main weakness is knowledge: AA-Omniscience accuracy of 21.1% versus 34.7% for Claude Haiku 5.5, largely because it declines to answer 64% of questions, which holds its hallucination rate to 17.4%.
Preferred Networks released PLaMo 3.0 Fin, a finance-specialized small language model built on the PLaMo 3 NICT pretraining model from joint research with NICT and further trained on Japanese financial data, publishing open weights on Hugging Face under the PLaMo Community License. The model adds step-by-step Reasoning with reasoning traces output in Japanese, plus tool calling for web search, calculation and internal database access, and can run on a single GPU in closed cloud or on-premise environments. PFN says the instruction-following training for Reasoning and tool use was carried out on its own MN-Core 2 AI chip, with code porting completed within a week and training speed comparable to a GPU environment.
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.
Why it matters: Artificial Analysis's independent benchmark run shows where Inkling 2 Small gains over its predecessor and where it still trails Gemini 3.8 Flash and Claude Sonnet 5.5.
inclusionAI released Ling-3.0-flash-Sante-fp8, an FP8-quantized version of its healthcare-specialized Ling-3.0-flash-Sante model, keeping 124B total and 5.1B active parameters per token. It matches the BF16 checkpoint's 73.9 average across healthcare benchmarks while cutting weight memory for FP8-capable hardware. The research-only model supports 256K-token contexts and defaults to thinking mode with tool calling.
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.
Why it matters: Gemini 4 Carbon tops Artificial Analysis Intelligence Index v5 at 59 and trails Claude Opus 5.5 by 3 points on the Coding Agent Index, with per-task cost and hallucination figures for comparison.
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 released Timestral, its first family of time-series foundation models, in four sizes from 5M to 209M parameters. The largest model ranks first on all three main public benchmarks, GIFT-Eval, TIME and fev-bench, among pre-trained zero-shot models from a single checkpoint without ensembling, using two-thirds of the parameters of the previous state of the art. Timestral produces probabilistic multivariate forecasts with 21 quantiles, supports past-only and known-future covariates, runs on CPU without a GPU, and is available through Mistral Forge for fine-tuning, with a GitHub repository and technical report published under a modified MIT licence.
Why it matters: Mistral's first time-series foundation model family claims top zero-shot rankings on GIFT-Eval, TIME and fev-bench with a 209M-parameter checkpoint, offering a size-accuracy tradeoff reference for forecasting work.
inclusionAI released Ming-3.0-audio-flash, a 126B-parameter Mixture-of-Experts audio model with 6.5B activated parameters, alongside the compact Ming-3.0-audio-tiny at 9.3B parameters with 2.7B activated. The flash model covers Chinese and English speech recognition across dialects and noisy multi-speaker settings, long-audio understanding, non-speech sound and music perception, audio reasoning, and end-to-end spoken dialogue with optional synthesized speech output. It ships with vLLM-Omni, SGLang, and Transformers inference paths under the MIT License, with default Flash profiles using tensor parallelism across four 80 GB H800 GPUs at 128K context.
Why it matters: The release details a 126B-parameter MoE audio model with 6.5B activated parameters, plus a 9.3B tiny variant, giving a concrete reference for open audio-language model sizing and deployment.
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.
Why it matters: Meta's Muse Wonder series claims state-of-the-art results on agentic, memory and coding benchmarks, with Active Memory and Active Focus as the two capabilities it is built around.
A new lab argues that the sameness of AI prose is a training problem and has built a writing-focused model to address it. In testing, the model's output proved less predictable, though not necessarily better. The piece frames the effort against what it calls stalled progress on writing at major labs.