What changed in AI today

• Orion-1 links log data from networks, endpoints, cloud and SaaS apps to trace signals back to a single attacker.
• On Artemis's own DGR benchmark, it scored 80.8 on attack reconstruction versus 58.3 for the next-best model.
• Private preview for select customers; high-impact actions require human approval by default.
• Artemis says no customer data was used in training, and it has not said whether Orion-1 builds on another provider's model.

Why it matters for marketers: Comparisons rest on Artemis's own benchmark, which omits Mythos 5 and GPT-6 Astra, so treat its performance claims as unverified until independently tested.

• 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.