GPT-5.4 LANDS: LONG CONTEXT, NATIVE COMPUTER USE, AND CODING GAINS
OpenAI’s GPT-5.4 is rolling out with stronger coding, long‑context reasoning, and native computer‑use, pushing teams to revisit model selection, guardrails, and...
OpenAI’s GPT-5.4 is rolling out with stronger coding, long‑context reasoning, and native computer‑use, pushing teams to revisit model selection, guardrails, and costs.
OpenAI’s cookbook shares practical guidance for multimodal vision and document workflows with GPT‑5.4, including prompt patterns for structured outputs, tool calls, and retrieval‑friendly prompts; it’s a good starting point for productionizing doc/vision tasks developer guide. Launch coverage underscores GPT‑5.4’s positioning as a frontier model for complex professional work across surfaces news recap.
For API testing and budgeting, a third‑party listing via Puter.js shows GPT‑5.4 with ~1.05M context, 128K max output, native computer‑use, and a user‑pays model at $2.5/M input and $15/M output; validate these specs in your target interface before rolling out Puter model card.
If you are choosing between GPT‑5.4 and Anthropic’s Claude Opus 4.6, a deep comparison finds overlap in long context, tool use, and agentic workflows, but different rollout posture and gating, including how ChatGPT variants are exposed to users feature‑by‑feature comparison. Real‑world testing shows GPT‑5.4 can excel at desktop task automation yet still miss simple questions, so plan evals, fallbacks, and guardrails from day one test notes.
Agentic computer use plus long context moves more end‑to‑end workflows from scripts to AI orchestration.
Pricing and limits affect repository‑scale tasks, latency, and how you design retrieval and tool chains.
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Run head‑to‑head evals on repo‑scale coding and multi‑tool workflows, tracking accuracy, latency, and cost.
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Exercise desktop automation with strict observation, least‑privilege access, and safe rollback paths.
Legacy codebase integration strategies...
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Swap models behind your LLM gateway and verify function/tool schemas, token budgets, and streaming paths.
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Rebaseline evals and cost guardrails; add fallbacks for brittle steps like UI automation and long‑running chains.
Fresh architecture paradigms...
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Design around long‑context by default, with chunked IO, resumable plans, and structured tool contracts.
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Adopt eval‑driven development from the start with golden tasks, budget caps, and deterministic checkpoints.