New models and versions, open weights, and changes in capability and pricing, with their evaluations.
Aravind Srinivas announced two open-sourced Decision Models: pplx-decider-flash-v1.2: 4B params, 1 cent per million tokens, and the top 4B model on the HF Decision Index. pplx-decider-v1.2: 27B params, 2 cents per million tokens, and #2 overall on the HF Decision Index.
Google is rolling out Gemini 4 Argon globally to developers, enterprises, and paid subscribers, emphasizing coding, professional knowledge work, multimodal understanding, and sustained task execution. API pricing is $2 per million input tokens and $10 per million output tokens through November 30, rising to $4 and $20 on December 1. Google reports that Argon worked unattended for 11 hours on ADK optimizations, with engineers merging two changes; Ethan Mollick described improvements but noted remaining weaknesses in complex multi-agent tasks.
Elorian released Glimpse on Friday as a research preview with API access and a waitlist-only chat interface. Its visual scratch space lets it crop image regions, retrieve video frames, and run code to measure or compare visual content during reasoning. Pricing is $0.60 per million input tokens and $3 per million output tokens; Elorian’s evaluations report performance above GPT-6 Astra on scientific chart reasoning and short-form video captioning.
Google DeepMind’s Gemini 4 Argon scored 97.5 percent on ARC-AGI-3 using the Provider Adapter Harness at a cost of $4,800, behind GPT-6 Astra’s 99.9 percent. Its score fell to 49.8 percent at low reasoning in the Standard Harness, where API problems prevented completion of all games. Epoch AI placed Argon at 165 ECI points, close to Opus 5.5 and GPT-6 Astra, although early estimates ranked it eighth in math and sixth in software engineering.
Why it matters: The comparison shows how testing environments can change benchmark standings, providing context for interpreting Argon’s proximity to leading models alongside its weaker math and software engineering results.
Aravind Srinivas says Gemini 4 Argon has set a new SoTA on the Wide-And-Deep-Research (WANDR) benchmark and is now available to all Perplexity users in fast search and Computer (agent) modes. https://t.co/0U671BhbMj The quoted @perplexity_ai announcement specifies availability for Pro and Max subscribers. https://t.co/n1TyI9RhBX
Why it matters: The post identifies availability across fast search and Computer modes, while its broader access claim differs from the quoted announcement limiting availability to Pro and Max subscribers.
Reflection released Beam on Hugging Face, a 501B MoE model with 23B active parameters per token, a 1M-token context window, and an Apache 2.0 license. The text-only model targets coding and agentic work, with BF16, FP8, and NVFP4 checkpoints and day-0 support in transformers, vLLM, and SGLang. Hugging Face provides deployment and post-training recipes alongside its own task evaluations and demos.
Why it matters: The deployment guidance distinguishes active computation from total weight memory, helping teams assess hardware requirements for running and adapting a sparse model with a long context window.
Reflection launched Beam, a 501B MoE model, with open weights under the Apache 2.0 license and 40+ ecosystem partners. NVIDIA AI says it was trained on NVIDIA GB300 NVL72 and delivers over 70K tokens/sec/GPU on Blackwell Ultra out of the box on day 0. Reflection’s quoted announcement includes BF16, FP8 and NVFP4 weights, a DFlash2 drafter, and Mirror CLI; the Beam 1.0 NVFP4 checkpoint is available at https://huggingface.co/reflection/Beam-1.0-501B-A23B-NVFP4.
Why it matters: The Apache 2.0 license, multiple weight formats, and released training harness give developers concrete information for assessing Beam’s suitability for their deployment workflows.
Liquid AI has released d1-ultra, which Artificial Analysis reports leads its Decision Index v1.0 with a score of 65. On AA-Omniscience-Decision, its non-hallucination rate rose to 85% from d1’s 2%, while accuracy fell to 32% from 41%. This non-hallucination metric measures how often the model says it does not know among questions it does not answer correctly. d1-ultra takes 92 ms per task and costs $0.042 per 1M input tokens, billed only on input tokens.
Meta has released Muse Spark Open, an 816B (42B active) open-weights model that Artificial Analysis reports scores 39 on its Intelligence Index, making it the #1 US open-weights model. Muse Spark Open (xhigh) scores 22 points above Muse Glimmer (high, 17), Meta's 30B dense open-weights model. https://t.co/jf31mQNINe
Why it matters: The comparison places Muse Spark Open 22 points above Muse Glimmer on the same intelligence index, providing a concrete reference for the reported capability gap between Meta's open-weights models.
NVIDIA Nemotron 3.5 Super VL is now available on Ollama. The model combines reasoning and visual understanding across a range of agent workflows. Ollama says it delivers up to 5x higher throughput on a single GPU for agentic tasks compared with other open models of its size.
NVIDIA AI introduces Nemotron 3.5 Bolt 24GB, saying it fits into 24GB of memory. The quoted @NVIDIARTXSpark post describes 74B-A9B parameters packed into 24GB of memory, optimized for RTX Spark and designed to run alongside your apps. https://t.co/SOiBgeMMjD https://t.co/yPsBt9N58A
IBM released Granite-Switch-4.1-8B and -30B, combining chat, a general-purpose Decision aLoRA, a specialized Guardian aLoRA, and Granite-Speech-5.0-Turbo-CTC transcription through one hosted endpoint. IBM says the combined model uses approximately the same GPU VRAM as its base model, with adapters reusing the base model’s KV cache. Users can also compose custom Switch models from Granite Libraries adapters; an Adapter SDK for training adapters in Mellea is planned.
Why it matters: Shared KV caches and modular adapters offer a concrete approach to combining general and specialized decisions with transcription while keeping GPU memory use close to the base model.
Ant has open-sourced Ling-3.1-flash, a model with approximately 560B total parameters, about 25B activated per token, and a context window of up to 1M. QbitAI tested it on an e-commerce promotion plan and a simulated medical appointment preparation task, reporting traceable calculations and explicit handling of missing information. The review also found occasional attention drift with long inputs and noted that the current version lacks native visual understanding.
Meta announced Muse Spark Open, describing it as the most capable US open-weight model to date, built for coding, agentic work, and multi-step reasoning. NVIDIA AI says the 816B model was trained on NVIDIA GB300 NVL72 and has a 1M-token context window for working across large codebases. Meta also produced NVFP4 checkpoints optimized for Blackwell Ultra. https://t.co/yAtoxNeYJm https://t.co/HJUKdC1n3D
The same story is shown in the picks as 《Meta releases Muse Spark Open, an open-weight multimodal mixture-of-experts model》
Meta announced Muse Spark Open today, releasing its weights and saying it can run on two Apple M5 Ultra Mac Studios using the latest MLX framework. Meta says the natively multimodal model has 816 billion total parameters and excels at long-horizon tasks, complex software engineering, and multi-step reasoning. Meta and Apple collaborated over several months, and Apple published a technical post covering MLX, Muse Spark Open, and two other open models.
The same story is shown in the picks as 《Meta releases Muse Spark Open, an open-weight multimodal mixture-of-experts model》
Baoyu reports that Meta released Muse Spark Open on October 15, an open-weight model with 816B total parameters, 42B active parameters, and a 1M token context window. It accepts text, images, video, and audio and outputs text; the Muse Model License permits free commercial use subject to attribution, usage restrictions, and a separate agreement for qualifying API businesses. The model card lists minimum memory requirements of 700GB for NVFP4 and 2TB for BF16, while Meta describes optimization for two Mac Studio machines with 512GB unified memory each.
The same story is shown in the picks as 《Meta releases Muse Spark Open, an open-weight multimodal mixture-of-experts model》
1/ Muse Spark open is the most capable US open-weight model to date. 816B total params, 42B active. 1M-token context. Natively multimodal. Built for coding and agentic work like long-horizon tasks, hard software engineering, and multi-step reasoning. America! 🇺🇲 https://t.co/NjtOB7aY9D
Today Meta is releasing Muse Spark Open, which it calls the most capable US open-weight model to date. Built for coding and agentic work, Meta says it excels at long-horizon tasks, complex software engineering, and multi-step reasoning. Key model specs include 816B total / 42B active parameters, a 1M token context window, and native multimodality across text, image, video, and audio.
The same story is shown in the picks as 《Meta releases Muse Spark Open, an open-weight multimodal mixture-of-experts model》
We now have state-of-the-art pre-trained base models in the 3T-parameter weight class, built entirely in-house at MAI. Post-training results coming soon—stay tuned! Lots of details in our technical blog post. https://microsoft.ai/news/frontier-pre-training-at-microsoft-ai/
Why it matters: The announcement places MAI’s reported progress at the pre-training stage, with post-training results still forthcoming, providing context for interpreting its claim of state-of-the-art base models.
Meet NVIDIA Nemotron 3.5 Super VL. NVIDIA’s new open model combines reasoning and tool use with text, image, document, and video understanding for long-running AI agents. 120B total parameters with 12B active.
The same story is shown in the picks as 《NVIDIA Expands Nemotron Family With Super VL and Bolt Open Models》
Mistral AI released Timestral in four sizes, from 5M to 209M parameters, for probabilistic, multivariate forecasting with past-only and known-future covariates. Mistral reports that its 209M model ranks first among pretrained, zero-shot models across five metrics on GIFT-Eval, TIME and fev-bench, using a single checkpoint without ensembling. All sizes support CPU inference, and Mistral published a GitHub repository for running Timestral on users’ data; domain adaptation and fine-tuning are available through Mistral Forge.
Why it matters: The comparisons across model sizes and CPU latency provide a concrete basis for assessing forecasting accuracy against compute requirements in production deployments.
NVIDIA AI congratulates StepFun on Step 5 Preview, calling it StepFun’s largest open weights release. NVIDIA says the 600B MoE model supports agent tasks lasting up to 24 hours and runs at nearly 8K tokens/sec/GPU on NVIDIA GB300 using NVFP4. StepFun’s quoted announcement lists 27B active parameters per token, a 1M-token context window, native text and vision input, and BF16 and FP8 weights on Hugging Face and ModelScope. NVP4 checkpoint: https://t.co/lSECChAwlW Deploy with https://t.co/GO1wqTSBOk https://t.co/28TBzUo6ZU
Why it matters: The reported throughput ties Step 5 Preview to a specific GPU and precision format, giving readers concrete deployment context for its long running agent workloads.
Project Apertus released Apertus 1.5 in 8B and 70B sizes, adding image and audio understanding, optional thinking, a 262,144-token context window, and improved instruction-following and tool use. The weights were released on Hugging Face on July 24, 2026 under Apache 2.0; the models generate only text, and spoken-language processing remains experimental. The team also publishes training data, recipes, intermediate checkpoints, and reproducible evaluation records.
Why it matters: Published intermediate checkpoints, training mixtures, and evaluation settings provide a concrete basis for investigating how individual training stages change model behavior and for reproducing reported results.
Huawei says its mass-produced Kirin 9050 Pro uses logic folding to shorten signal paths through vertically connected circuit layers, supporting performance and efficiency gains in the Mate 90 Pro Max. The article explains how dense hybrid bonding enables this layout and how memory coordination allows Kirin 9050 chips to run a 30B MoE model with less than 2.5GB of additional memory usage. Huawei also says Kirin 2027 chips are in testing, with results meeting expectations so far.
Gemini 4 Argon is already unlocking new scientific breakthroughs in quantum computing, nuclear fusion, protein design and beyond. 🧵 https://t.co/Shj3Y4whoK
@lightseekorg reports 685 output tokens/s per user for Qwen3.8-Flash-Next. Congrats to @lightseekorg on the impressive TokenSpeed results! 🚀 Great to see Qwen3.8-Flash-Next reaching 685 output tokens/s per user on Nvida GPUs. Excited to see the community unlock more of Qwen’s inference potential!
Google is rolling out Gemini 4 Argon worldwide to developers, businesses, and consumers, replacing Gemini Pro as its frontier series. Access includes the API through AI Studio and the Gemini platform, plus selected subscription tiers across Antigravity, the Gemini app, and Google Search's AI mode; free and Plus users are limited to Flash models. Introductory pricing is $2 per million input tokens and $10 per million output tokens, with a 95 percent discount on cached inputs.
Why it matters: The new access tiers clarify how Google's flagship rollout affects existing subscribers, with free and Plus users limited to Flash while higher tiers gain Argon access.
Artificial Analysis reports that Gemini 4 Argon is now GA, matching GPT-6 Astra on its Intelligence Index at 60% of the Cost per Task under discounted pricing until November 30. Argon costs $1.99 per task and $2/$10 per 1M input/output tokens, rising to $4/$20 after November 30, with a 95% cache-read discount. Artificial Analysis also reports substantial agentic evaluation improvements and a 15% hallucination rate on AA-Omniscience, the lowest among models scoring 45+ on its Intelligence Index.
Why it matters: The comparison separates intelligence scores from task costs, showing how Argon's temporary pricing affects its cost advantage over GPT-6 Astra at a comparable intelligence level.
Google launched Gemini 4 Argon, claiming improved reasoning and benchmark performance against GPT-6 Astra, Claude Fable 5.1, and Claude Opus 5.5. It is rolling out today to Pro and Ultra subscribers in the Gemini app and AI mode in Search, and Ultra subscribers on Antigravity; access for Plus subscribers and free users remains unclear. Introductory pricing is $2 per million input tokens and $10 per million output tokens, doubling at the end of November, though lower token rates do not necessarily mean lower task costs.
The same story is shown in the picks as 《Gemini 4 Argon is now available worldwide as Google's most capable frontier model》
Liquid AI released LFM2.5-24B-A2B, an open-weight reasoning model with 24B total parameters and 2B active parameters, for on-device agents that reason and call tools. Its context window expands from 32K to 131K tokens, and both the reasoning and Base versions are available on Hugging Face. Liquid AI reports 184 tokens/s on an Apple M5 Max and competitive tool-use results, while acknowledging that larger models retain advantages on knowledge-heavy and coding benchmarks.
Why it matters: The benchmark comparison helps distinguish workloads suited to local deployment, with strong tool use and instruction following alongside weaker knowledge and coding results than some larger models.
Baoyu reports that Google released Gemini 4 Argon on October 13 for complex, multistep tasks, citing Sundar Pichai’s announcement of global paid availability. Access includes the paid Gemini API and platform, Ultra subscriptions in Google Antigravity, and Pro or Ultra subscriptions in the Gemini app and Search. Introductory API pricing is $2 per million input tokens and $10 per million output tokens, including thinking tokens, through November 30; prices double December 1. Input, thinking, and output share a 1,048,576-token limit.
Why it matters: The shared budget for input, thinking, and output makes context allocation consequential for long tasks, while repeated input accounting leaves a specific billing detail unresolved.
Google DeepMind has launched Gemini 4 Argon globally through its API and selected Google products, with access depending on subscription or service tier. Introductory pricing is $2 per million input tokens and $10 per million output tokens, with cached input tokens at 95% off and a 1M unified input-output token limit through the Gemini API. Google reports improvements across coding, professional workflows, scientific research, and robotics, alongside stronger agent safeguards and prompt injection resistance.
Why it matters: Google’s reported gains in code submissions and reductions in rollbacks connect Argon’s engineering capabilities to delivery speed and code stability in internal workflows.
Google DeepMind introduces Gemini 4 Argon, a general-purpose physical reasoning model for robot control, simulation reconstruction, physical understanding, and self-improvement. Google DeepMind reports an 87% ERQA score using pure reasoning and a simulation-generated training example that raises real-world robot success from 56% to 75% while reducing completion time from 35s to 24s. Argon is available today to Google AI Ultra subscribers through Google AI Studio and Google Antigravity, and through the paid Gemini API tier using gemini-4-argon in the Interactions API.
Why it matters: The simulation-to-hardware example provides a concrete basis for assessing how generated training episodes can improve robot task success and completion speed within a reported setup.
Logan Kilpatrick announces that Gemini 4 Argon is rolling out globally today in the Gemini API, to Pro + Ultra users in Gemini App, and to Ultra subscribers in Antigravity. He says over 100,000 people at Google tested the model over the last month, with cyber defenders in the fairwind program and early access customers receiving it over the last 2 weeks. He highlights writing, knowledge, persistence for coding, and 1M token output when the model needs to work more.
The same story is shown in the picks as 《Gemini 4 Argon is now available worldwide as Google's most capable frontier model》
Gemini 4️⃣ Argon ✨ is now rolling out globally. Google describes its frontier model as built for complex, real-world tasks across coding, finance, legal, cybersecurity, and science. See how to use Argon in your favorite Google product 🧵 https://t.co/PtoPqbtPel
The same story is shown in the picks as 《Gemini 4 Argon is now available worldwide as Google's most capable frontier model》
Meta Superintelligence Labs released Muse Wonder and Muse Wonder Full, its second-generation model family for personal and professional agentic work. Latent Space reports that Wonder is available through the Meta Model API and Muse Code, while Full is limited to Muse Code subscribers. Artificial Analysis gives Wonder Full and Wonder Intelligence Index v4.3 scores of 56 and 52, respectively, versus Opus 5.5’s 58; Wonder supports a 1M context window and text, image, video, and audio inputs, with text-only output.
Why it matters: The benchmark breakdown compares Wonder variants with competing models on agentic performance and token usage, showing how their relative standing changes across tasks rather than a single ranking.
Meta has released Muse Wonder and Muse Wonder Full, according to Artificial Analysis in the post quoted by Alexandr Wang. At max reasoning effort, they score 52 and 56 on the Artificial Analysis Intelligence Index v4.3, compared with Muse Spark 1.3’s 48, following a release gap of 6 weeks. Muse Wonder is available through Meta’s first-party API and Muse Code, while Muse Wonder Full is limited to Muse Code users; both have 1M-token context windows.
Why it matters: Artificial Analysis’s comparison highlights a tradeoff between Muse Wonder’s improved benchmark performance and its higher cost per task relative to Muse Spark 1.3 despite lower output token usage.
Meta’s Superintelligence Lab released Muse Wonder and Muse Wonder Full on Monday, larger models described as more capable than Muse Spark. Meta reports benchmark scores comparable to Fable 5.1 and GPT-6 Astra and emphasizes information retention through “active memory.” Wonder costs $4.00 per million input tokens and $20.00 per million output tokens through the Meta Model API; Wonder Full is available only to Muse Code subscribers. Both models will power the Muse agent.
Why it matters: The split between API access for Wonder and subscriber access for Wonder Full gives developers a concrete basis for comparing integration options within Meta’s new model lineup.
Meta launched Muse Wonder and Muse Wonder Full, available to Muse Code subscribers starting today; Wonder is also accessible through the Meta Model API. Meta claims internal benchmark testing shows the models can rival OpenAI’s GPT-6 Astra and Anthropic’s Claude Opus 5.5 and Claude Fable 5.1. The company plans to power its Muse AI agent with Wonder, starting with subscribers, but has provided no timeline.
Microsoft is introducing Maia 300, which Satya Nadella says delivers 40% more tokens per watt compared to leading GPU-based systems. Starting next calendar year, Microsoft will scale its Maia family to multi-gigawatt deployments, making it the primary inference system for its own products and services. Microsoft is already deploying Maia 200 across the US, Europe, and Australia, delivering up to 1.7x more tokens per watt across experiences in Teams and Copilot, with a goal of building on full-stack efficiency gains while continuing to bring ecosystem innovations into its fleet.
Why it matters: Making Maia the primary inference system for Microsoft’s own products and services gives its stated efficiency gains a concrete deployment context.
