Q: What are the main GPT-5.4 features for developers that speed up workflows? A: GPT-5.4 features for developers include native computer use, on-demand tool search, improved token efficiency, /fast mode and priority processing, plus experimental support for up to a 1M-token context in Codex. The model also provides an upfront planning preamble in ChatGPT and stronger web research and document handling to reduce back-and-forth and lower latency. Q: How does GPT‑5.4’s native computer-use capability change what agents can do? A: GPT‑5.4 can drive desktops and browsers by writing code (for example with Playwright) and issuing mouse and keyboard commands in response to screenshots, enabling agents to operate across applications. This lets agents complete multi-step workflows and end-to-end flows like onboarding users, filing forms, or updating records without manual babysitting. Q: What is tool search and how does it reduce token usage? A: Tool search gives the model a lightweight list of available tools and lets it fetch a tool’s full definition only when needed instead of including every definition upfront. In tests across 36 MCP servers, placing servers behind tool search reduced total token usage by 47% while achieving the same accuracy. Q: How does GPT‑5.4 improve coding speed and iteration in Codex? A: GPT‑5.4 offers /fast mode in Codex for up to 1.5x faster token velocity and priority processing via the API for lower queue latency, which accelerates coding, iteration, and debugging. It also includes the Playwright (Interactive) Codex skill to visually build, test, and debug web and Electron apps as they are developed. Q: What are the context window limits and cost implications for long-horizon tasks in Codex? A: Codex has experimental support for a 1M-token context window that you can enable via model_context_window and model_auto_compact_token_limit, allowing specs, logs, tests, and code maps to live in a single session. Requests that exceed the standard 272K context window count against usage at 2x the normal rate, so you should monitor budget when using the extended window. Q: How does GPT‑5.4 Thinking in ChatGPT help with longer or more complex queries? A: GPT‑5.4 Thinking can provide an upfront plan or preamble for longer tasks so you can adjust direction mid-response, which helps align final outputs without extra turns. The model also maintains context longer and improves deep web research, making it better at synthesizing information from many sources for specific questions. Q: Does GPT‑5.4 reduce factual errors and improve document handling compared to earlier models? A: The article reports that GPT‑5.4’s individual claims were 33% less likely to be false and its full responses were 18% less likely to contain errors relative to GPT‑5.2 on a set of flagged prompts. It also shows improved document parsing (OmniDocBench average error 0.109 vs 0.140) and higher spreadsheet modeling scores on internal tasks (87.3% vs 68.4%). Q: How should developers choose between gpt-5.4 and gpt-5.4-pro and which settings deliver the fastest results? A: Choose gpt-5.4 for a balance of speed and cost and gpt-5.4-pro when you need maximum performance on the most complex tasks, with Batch and Flex pricing available to lower costs and Priority processing for faster queues. To maximize speed gains, enable /fast mode in Codex, use priority processing for latency-sensitive runs, set reasoning_effort to None for lowest latency or xhigh for hardest tasks, and adopt tool search while keeping prompts lean.