Model choice is an engineering decision that shows up in both output quality and your bill. This walks through models.dev and artificialanalysis.ai as the two resources for shortlisting on specs/price and then checking capability, cost per task, and latency, with worked comparisons across GPT-5.6 Sol, Claude Fable 5, Gemini, LongCat-2.0, and Seedance 2.0.
OpenAI's GPT-5.6 announcement is dense with benchmark numbers and efficiency claims. The company positions Sol as the flagship model, with Terra and Luna filling in for balanced and cost-efficient tiers. The through line is not just higher raw scores. It is capability per token and time to result across agentic workflows, coding, browsing, science, and cyber tasks, with repeated emphasis on lower cost and latency for comparable outcomes.
Google quietly published OKF, a plain markdown-plus-frontmatter format for agent knowledge, built on a pattern Karpathy sketched in a gist months earlier. Working through both taught me to stop thinking of an LLM as one brain and start thinking of it as two.
Claude Fable 5 was pulled from every user globally for two and a half weeks after a government export control order, then came back with a new safety classifier. Here is what happened and what it tells me about how "free" a model actually is.
I followed the Mastra tutorial to build and publish a simple MCP server, then connected it to a self-built agent and to Claude. Here is what the protocol actually does under the hood and what surprised me.