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A source-first roundup showing how official documentation, launch notes, practitioner reactions, and video context can become one useful editorial package without losing provenance.
Why it matters: Use the catalog as the current source of model names, capabilities, pricing, and availability; do not freeze those facts into application code.
The current guidance starts with Sol for complex work, Terra for balance, and Luna for cost-sensitive volume, then breaks specialist work into image, realtime, speech, transcription, and embedding families.
Why it matters: The launch case for mini and nano was not only lower price. Faster tool use and bounded subagent work make more tasks economical to hand off.
OpenAI introduced GPT-5.4 mini and nano as faster, lower-cost choices for coding, computer use, multimodal work, extraction, ranking, and supporting agents.
Why it matters: The useful split keeps architecture and progress in the parent task while faster agents explore and check narrow questions.
This reaction turns a model release into an operating pattern: protect the main thread's attention and delegate bounded checks to a faster lane.
Why it matters: This post points at the product gap between a good plan and a system that can turn it into tracked work without repeated setup.
A short feature request links planning in ChatGPT with task breakdown and execution in Codex. It is a useful test for whether model choice improves a full workflow, not only one response.
Why it matters: Third-party explainers can add context, but the card must state who made the video and link back to the original upload.
This independent video overview summarizes GPT-5.4's professional-work positioning. HRVSTR keeps it beside the official release rather than treating it as equal authority.