What changed in AI today

• Dyna-2.1 pairs new wheeled semi-humanoid hardware with an agentic system built around the Dyna-2 world-action model.
• Dyna reports a 99.4% success rate folding restaurant napkins over 24 straight hours, per co-founder Jason Ma's AIE talk.
• Robots reportedly fold thousands of napkins daily at Din Tai Fung, and can learn new tasks from as little as 45 minutes of demonstrations.
• Dyna-2 was pre-trained on over one million hours of human video and predicts future video to guide robot actions.

Why it matters for marketers: Content teams covering robotics get concrete benchmarks for hour-long autonomy, though the figures are company-reported and not independently verified.

• Available now via the API in AI Studio and Gemini platform, plus Google AI Ultra and Pro subscriber access in the Gemini app.
• Introductory pricing is $2 per million input tokens and $10 per million output tokens, with cached input 95% off.
• Google says Argon ranks #1 on the Vals Index, which measures performance across finance, coding, legal and tax work.
• Google reports Argon helped its own teams raise agentic code submissions by more than 35% in Antigravity.

Why it matters for marketers: Content and marketing teams using Gemini tools get a model Google positions for multi-step professional work, at a stated introductory price, though the Vals ranking is Google's own report.

• Announced Friday as "flows", a new multiagent orchestration feature built on Claude Managed Agents, presented at AI Engineer New York.
• Previously developers configured a coordinator and subagent roster; with flows, Claude writes an orchestration program that launches agents and manages information flow.
• Flows are available in Claude Console, and developers can still pass in pre-configured specialist sub-agents if they wish.
• Presenters said flows cost "much more" than a single agent but were "much more effective" at finding details; developers can set session budgets, not the model.

Why it matters for marketers: Teams running many agents should budget for higher token costs and use Claude Console graphing to audit what each sub-agent receives.

• Muse Wonder costs $4 per million input tokens and $20 per million output tokens via the Meta Model API.
• Wonder Full is limited to Muse Code subscribers and is not served through the API.
• Meta says Wonder scores higher on MemoryBench than Fable, Opus, or Astra, per its own testing.
• Models were reportedly trained at Meta's Prometheus data center in central Ohio.

Why it matters for marketers: Content teams building long-running assistants may get a stronger memory option, but pricing is comparable to rival frontier models and Meta's benchmark claims are self-reported.