Skip to content
Only Tutorials#OpenAI
  1. OpenAI Developers Blog60

    OpenAI publishes API deployment checklist for GPT-6 workloads

    OpenAI published an API deployment checklist that walks developers through moving workloads onto the Responses API and the GPT-6 model family, including gpt-6-astra, gpt-6.1-sol, gpt-6 Sol and gpt-6 Luna. The guide covers reasoning.effort levels from low to max, text.verbosity, tool_search with deferred tool loading, Programmatic Tool Calling, Multi-agent, compaction, prompt caching and WebSocket mode, which OpenAI says has shown up to roughly 40% faster end-to-end execution for workflows with 20 or more tool calls. It also details reliability and safety steps such as handling 429 and 503 errors, background=True for long jobs, safety_identifier and misalignment monitoring.

    Why it matters: OpenAI's official deployment checklist consolidates model selection, reasoning effort, tool search, compaction and WebSocket mode into one migration guide for GPT-6 workloads.

  1. OpenAI Developers Blog67

    OpenAI updates Sora 2 prompting guide with character references, 20s clips and video extension

    OpenAI updated its Sora 2 prompting guide to cover new API capabilities: reusable character references for objects and animals, 1920×1080 and 1080×1920 exports, a maximum clip length raised from 12s to 20s, video extension that uses the full original clip as context, and a Batch API for asynchronous video jobs. The guide also walks through prompt structure for shot framing, camera motion, lighting and dialogue, and notes that resolution, duration and character IDs must be set as API parameters rather than described in prose. Extensions of up to 20 seconds are supported, and a video can be extended up to 6 times for a total of 120s.

    Why it matters: OpenAI's updated Sora 2 prompting guide details new API capabilities such as character references, 20s clips and video extension, plus reusable prompt templates for controlling shots.

  1. OpenAI Developers Blog62

    OpenAI publishes Codex prompting guide for gpt-5.3-codex

    OpenAI released a Codex prompting guide for developers calling the Codex-tuned gpt-5.3-codex model directly through the API, recommending medium reasoning effort for interactive coding and high or xhigh for the hardest tasks. The guide covers migrating an existing harness to the open-source codex-cli, including the standard Codex-Max prompt, the apply_patch, shell_command and update_plan tools, and first-class compaction via /compact in the Responses API for multi-hour sessions. It also describes the new phase field, supported only on gpt-5.3-codex, which marks assistant items as commentary or final_answer and must be preserved in history to avoid performance degradation.

    Why it matters: OpenAI's official guide details how to tune prompts, tools and compaction for gpt-5.3-codex, giving teams a concrete migration path for their own coding agents.

You are all caught up