Gemini 4 Argon taught a robot to play cornhole just by watching a video. It mapped the physics, coded its own simulation, and taught @Apptronik's Apollo humanoid to sink 7 throws in a row.
Why it matters: The demonstration connects video observation, physics modeling, and a model-written simulation in a concrete example of teaching a humanoid robot a physical task.
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.
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.
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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.
Some great use cases from Googlers building with Gemini 4 Argon 🧵 It has a much stronger grasp of coding and multi-step tool use - starting from a raw Pixel CAD file in Google Antigravity, Argon created this exploded-view animation, lighting, camera choreography, and audio through Blender MCP. It ran 1,000 rounds of iterative loops to check its own renders and refine the code along the way.
@chetaslua says Gemini 4 ARGON built a detailed Pagoda model using multi-agent visual critique loops in Antigravity. The model autonomously sculpted a temple complex in Blender. Josh Woodward reacted: “WTF” https://twitter.com/chetaslua/status/2110050110784798950
Ethan Mollick says Gemini 4 is a big improvement over earlier Gemini models and the first non-OpenAI or Anthropic model to pass the Fable/Mythos/Astra bar. He finds it more pleasant and agentic, with a distinct personality, while noting that it lags in complex multi-agent work and can be overly certain and lazy. His post also links to three city-builder demos.
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.
Gemini 4 Argon is here. It’s available globally today for developers and enterprises in the paid tier of the Gemini API and the Gemini platform, for Ultra subscribers in Google Antigravity, and Ultra and Pro subscribers in the Gemini app and Search. Hope you all enjoy it!
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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》
Josh Woodward writes “Go!” and links to Sundar Pichai’s announcement that Gemini 4 Argon is here. https://twitter.com/sundarpichai/status/2110038898009407584 Pichai says it is available globally today for developers and enterprises in the paid tier of the Gemini API and Gemini platform, Ultra subscribers in Google Antigravity, and Ultra and Pro subscribers in the Gemini app and Search.
Gemini 4 Argon is now globally available. Built to solve your hardest problems from start to finish, the model brings frontier capabilities across coding, enterprise workflows, and cyber defense.
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
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Google Gemini says its new frontier model, Gemini 4 Argon, is rolling out now in the Gemini app to bring users’ most complex ideas into reality. The post introduces a thread on Gemini 4 Argon and the latest Gemini app updates to help users get the most out of every prompt.
The same story is shown in the picks as 《Gemini 4 Argon is now available worldwide as Google's most capable frontier model》
François Chollet congratulates the release, saying Gemini 4 Argon looks like a fantastic model. In the quoted announcement, Sundar Pichai says it is available globally today for developers and enterprises in the paid tier of the Gemini API and the Gemini platform, for Ultra subscribers in Google Antigravity, and for Ultra and Pro subscribers in the Gemini app and Search.
The same story is shown in the picks as 《Gemini 4 Argon is now available worldwide as Google's most capable frontier model》
Gemini 4 Argon is rolling out today to Google Ultra and Pro subscribers in @GeminiApp and Search, and to Ultra subscribers in @antigravity. Demis Hassabis praises its knowledge work and coding capabilities, sharing a wizard chess game they asked it to create as an example of its physics understanding and 3D modeling capabilities that reminds him of Battlechess on the Amiga. More information and full availability: https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-argon/
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 released Muse Wonder and Muse Wonder Full, scoring 52 and 56 on the Artificial Analysis Intelligence Index v4.3, versus 48 for Muse Spark 1.3. Both support a one-million-token context window and text, image, video, and audio inputs, with stronger coding and agent results but weaker factual knowledge than leading competitors. Muse Wonder is available through Meta's API and Muse Code at higher prices than its predecessor; Muse Wonder Full is currently limited to Muse Code users.
The same story is shown in the picks as 《Meta releases frontier models Muse Wonder and Muse Wonder Full》
muse wonder over a cheeky pint at some point in this video i get my annual performance review live on camera -- a truly tender moment 🥹
Great to see what the Fin team has built with NVIDIA Nemotron 🙌 They fine-tuned Nemotron 3 Super and Nano for their new Fin Apex customer service models. Congrats on the launch! https://t.co/XxVuc3TzqJ
Had some early access to Muse Wonder, but did not get a chance to use it a ton. From limited testing, it seems like a good model. Perhaps more impressive is the progress since April on my drowned neogothic city twigl shader test. Very fast progress from Meta in a short time.
🍉 MUSE WONDER 🍉 @hdubugras says Alex and Meta are on a roll, and that the model has replaced Astra/Opus in a variety of their workflows. They say it handles day-to-day tasks efficiently and quickly, and offer congratulations. Alexandr Wang’s reaction: “🍉 MUSE WONDER 🍉”
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.
If you announce a model's benchmarks without releasing it (weights, product, api, etc "later"), feels like almost a sure thing that someone releases a better model in your category before you get it out.
MUSE WONDER 🍉 this model was developed to power personal agents. three highlights: - ACTIVE MEMORY: best memory across days/weeks/months - ACTIVE FOCUS: gets your tasks done - DOMAIN EXPERTISE: sota in finance & legal, strong in health
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.
introducing muse wonder our new frontier model. state-of-the-art at personal agents and professional agents. our largest and most capable model yet—rivals the big bois
MUSE WONDER this is the culmination of over a year of research and infrastructure work in msl since we originally shipped muse spark in april, we have continued to scale our models across pre-training and RL, now reaching the frontier 🏆 see our model's improvements on the artificial analysis index:
Meta introduces Muse Wonder and Muse Wonder Full, describing them as its most capable models yet and frontier models built for personal and professional agentic tasks. Muse Wonder is available today via the Meta Model API and Muse Code. Muse Wonder Full is available today for Muse Code subscribers. Muse Wonder will also soon power experiences in Meta’s personal agent product, Muse, rolling out first to subscribers.
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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.
Zhipu has added the GLM-5.3-Highspeed model to Zcode, where it is being gradually rolled out to subscribers at random. A user reported a generation speed of 490 tokens per second; the article says the model is currently available for testing in Zcode 3.15.1. The reported speed has not been independently confirmed, and the author says they could not access the model.
Microsoft released Microsoft-Decision-1, a decision model post-trained from Qwen3.5-9B, and says it outperforms LLMs and decision models including Jev 1.13.0 on latency and accuracy in structured decision tasks. Its Xbox research team has used it to process more than 10,000 player feedback entries, while CEO Satya Nadella says Microsoft is testing applications across domains, including incident response, quality control, and scientific discovery. The report describes decision models as smaller, faster models that output predictions, probabilities, and confidence scores rather than natural language.
vLLM congratulated the vllm-sr team on releasing Decision 3.0, described as a state-of-the-art multimodal Jev model whose weights are now open-sourced on Hugging Face. The quoted announcement from Xunzhuo Liu says the open model family spans 0.6B to 27B and ranks #1 on both Jev Decision Index boards in its evaluation, on a new Pareto frontier.
Odyssey has launched a free public research preview of Odyssey-3, a world model that generates interactive environments from text prompts and runs in real time on an autoregressive diffusion transformer. The base model has 14 billion parameters and outputs 832 × 480 video, while Odyssey-3 Pro supports 1280 × 720. Its reported 66.1-point Physics-IQ Verified score came from a single run using a selection method, below the benchmark's four-run requirement.
Google released Nano Banana 2.1, an upgraded image generation model with 4K output, mask-based editing and improved subject consistency. According to Arena, it gained about 61.7 points on average across text-to-image, multi-image editing and single-image editing, and Google halved API prices for 1K and 2K images while cutting 4K by about 25%. It is available in the Gemini app and Google AI Studio, with 50 free credits daily.
Why it matters: The piece rounds up Arena ranking gains, API price cuts and early user tests of Nano Banana 2.1, including reported watermark and repeated-edit issues.
Microsoft released Decision-1, a decision model for classifications, evaluations and routing decisions, built on Qwen3.5-9B. Microsoft says it tops 36 benchmarks covering nearly 150,000 questions with 83.5% accuracy and 85 ms latency, and is 2.5 times faster than runner-up H2O-Lightning-4B. It is available through Microsoft Foundry and OpenRouter at $0.042 per million input tokens, with output tokens free.
StepFun's flagship base model Step 5 Preview reached the top of OpenRouter's Trending list and second place on its daily ranking, with an official open-source release set for October 15. The sparse MoE model has 600 billion total parameters and about 27 billion activated per token, and Artificial Analysis gives it a composite intelligence index of 44, placing it in the global open-source top three.
Introducing Microsoft-Decision-1, our new model for fast decision-making. It delivers top performance on structured decision tasks, outperforming both LLMs and other decision models in latency and quality. We’re already testing it across Microsoft for everything from incident response and quality control to scientific discovery.
