370 summarised stories about Model Release, each linking back to the original source. Browse all topics →
MarkTechPost 4 days ago 46 ● 27 sources
Meta launched Muse Wonder and Muse Wonder Full as its most capable models for long-running personal and professional AI agent work, available via its API and Muse Code.
Simon Willison’s Weblog 4 days ago 41 ● 20 sources
The Gemini API and a $100/month subscription plan added support for the Gemini 4 Argon model via llm-gemini 0.35. The article says Argon costs $2/million input and $10/million output at launch, doubling on December 1st. Token-based benchmarking and overall running cost comparisons are expected to matter more as the price changes.
Latent Space 4 days ago 44
Dyna Robotics followed its Dyna-2 release with a physical robot agent, Dyna-2.1, and used a talk with Jason Ma to explain how its world-action model is used for long-horizon autonomy in real robotics. The robots reportedly achieved a 99.4% success rate folding restaurant napkins for 24 hours straight, including recovery after pulling over a stack. The focus shifts from training a large world-action model on video to deploying hour-long, task-executing physical agents built around custom hardware and agentic execution.
Trending Topics 4 days ago 11 ● 20 sources
Google expanded Gemini 4 Argon beyond selected cybersecurity partners and made it available via the Gemini API and paid Google AI subscriptions with rollout across developers, businesses, consumers, researchers, and expanded cyber partners. A 50% introductory discount applies until the end of November, then API prices double from $2/$10 per million input/output tokens to $4/$20 per million. After November, Argon becomes more expensive per token and per typical task, though it remains comparatively cheaper than several other frontier models in the article’s cost ranking.
The New Stack 4 days ago 7 ● 20 sources
Google debuted Gemini 4 Argon, making its new flagship model available in the Gemini API (paid tier) and across Google’s developer and consumer tools, with an initial rollout limited to selected Fairwind partners and testers. The model includes a unified 1M token limit that combines input and output in the same budget, and it costs $2 per million input tokens and $10 per million output tokens through November 30. After this shared limit, long prompts reduce the remaining tokens available for responses, and Google’s later description shifts from an output cap to a single input/output budget.
OpenAI 4 days ago 13
Higgsfield added longer video generation capabilities using GPT-6 Astra, along with improved visual generation via GPT-Image-2.5 and a voice-focused filmmaking path via GPT-Live. Video length increased from 1 minute to over 10 minutes. Creators can now produce longer AI videos and aim for more precise visuals and voice-driven workflows.
The Verge 4 days ago 39 ● 20 sources
Google launched its Gemini 4 Argon AI model after months of delay. The release is for Gemini 4 Argon. Google says it improves performance on complex workflows, reduces hedging and repetition, and is being used internally while it claims benchmark wins over rival models.
Product Hunt 4 days ago 21 ● 20 sources
Google launched Gemini 4 Argon as its frontier model for long-horizon software engineering, enterprise knowledge work, and cybersecurity defense. As of Oct 13, it is available globally in the Gemini API paid tier, AI Studio, the Gemini app, and AI Mode in Search with a 1M unified input and output token limit. It moves the model into multiple Google products and paid developer access with a specified token cap for longer tasks.
Latent Space 4 days ago 20 ● 27 sources
Meta’s Superintelligence Labs shipped the Muse Wonder model family, including Muse Wonder and the higher-tier Muse Wonder Full. Muse Wonder Full is priced at $4 per 1M input tokens and is available to Muse Code subscribers. Muse Code’s quotas were temporarily drained due to a usage-limit bug (fixed the same day with a reset), and third-party evaluations report Wonder Full outperforming the prior Muse Spark model on multiple benchmarks.
MarkTechPost 4 days ago 20
Z.ai launched GLM-Legal, a law-focused version of GLM-5.3-Flash trained for agentic legal tasks like contract review and compliance research. It scored 19.17% all-pass on Harvey’s Legal Agent Benchmark (LAB), up from 6.67% for its base model. Developers can access it via an application-only hosted API (no public weights or published price), and the rollout changes from model experimentation to API-based integration.
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17 October 2026
AI governance moved from slideware to the boring-but-important mechanics of who gets to fetch, act, and prove. Meta quietly rewrote parts of its commercial terms, removing wording that appeared to let it use web content to “train” and “maintain the integrity” of its products; the update followed legal scrutiny that it could be read as broad scraping permission. At the same time, users reported that major websites are blocking AI agents from completing tasks across services—turning “agentic” demos into something closer to permissioned automation. ServiceNow is now trying to sell the glue for that reality with its AI Control Tower, positioning it as an enterprise platform to discover and govern agents across models and clouds, while a field study of 15 large-enterprise customers questioned how fully its runtime enforcement and AI lifecycle evidence hold up versus alternatives.
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