Production-ready code with minimal oversight, and it can verify its own outputs More control over reasoning effort 3x better vision (now up to 3.75MP images) Improved instruction following and overall reliability New xhigh reasoning mode for finer control between speed and depth
Same pricing as Opus 4.6 ($5 and $25 per million input and output tokens). The new tokenizer can use around 1.0 to 1.35x more tokens depending on content, though this can be managed through effort settings and task budgets.
Everyone is hyping up GEO as the new SEO. Honestly? They re just merging. But hype aside, if you aren't tracking how ChatGPT or Perplexity cite your product, you're flying blind. What tools are you actually using to monitor your brand's AI visibility right now?
I ve been talking to a lot of SaaS and marketing teams lately, and there s a massive recurring theme: everyone is using AI to write, but almost everyone complains that the output sounds generic and lacks personality.
When you give an LLM a prompt like "Write a blog about X," you're giving it a topic, but completely starving it of context. It misses the brand voice, the specific audience positioning, and the terminology that makes a company unique.
I'm curious how other founders and content leads are handling this friction right now. Are you manually pasting your brand guidelines into your prompts every single time? Have you built custom custom GPTs? How are you actually bridging the gap between generic AI text and real, brand-aligned content?
