What changed in AI today

• 24B total parameters with 2B active per token, built as a reasoning model that thinks before answering.
• Scores 48.09 on LiveClaw versus 38.19 for Qwen3.6-35B-A3B, a model with roughly 50% more parameters.
• Decodes 184 tokens/s on an Apple M5 Max and 114 tokens/s on a Ryzen AI Max+ 395, per the source.
• Open-weight and available on Hugging Face now; supports llama.cpp, MLX, vLLM, SGLang, and ONNX from day one.

Why it matters for marketers: Content teams building tool-using assistants can run a local agent model, but should pair it with retrieval or search for knowledge-heavy tasks.

• 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.

• 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.