The latest updates across the posts highlight the rapid advancement and specialization of OpenAI’s GPT-5.1 models, particularly in coding and agent-based workflows. Key trends and announcements include:
- GPT-5.1 Launch: GPT-5.1 introduces major improvements such as a low-latency “none” reasoning mode, enhanced steerability, better prompt calibration, and refined output formatting and adherence to instructions.
- New Coding Capabilities: Both the base GPT-5.1 and the specialized
GPT-5-Codexsubstantially upgrade coding support.GPT-5-Codexis optimized for engineering tasks, complex code review, and offers adaptive reasoning while prioritizing minimal, precise prompts. - Advanced Agent SDK & Tooling: The OpenAI Agents SDK enables developers to build sophisticated coding agents that scaffold and refine software projects via prompt interactions. Agents can execute shell commands, apply code patches, search the web, and access current documentation, all within secure, sandboxed environments.
- Best Practices Emphasis: Across models, a “less is more” prompting philosophy is recommended. Developers are encouraged to keep prompts concise, leverage new migration features for personality steering, and carefully manage tool invocation and response length.
- Safety and Isolation: All examples and guidelines stress the importance of running agents and models in isolated environments with explicit user approval for sensitive operations to maintain production safety.
Overall, these updates position GPT-5.1 and its ecosystem as more efficient, customizable, and safe solutions for modern app development and advanced coding tasks.
New Cookbook Recipes
Build_a_coding_agent_with_GPT-5.1.ipynb
Source: openai/openai-cookbook
The blog post discusses creating a coding agent using the GPT-5.1 model and the OpenAI Agents SDK. The agent can scaffold apps from prompts and refine them based on user feedback. Key features of the agent include:
- apply_patch for file editing,
- shell for executing shell commands,
- web_search for retrieving up-to-date online information,
- Context7 MCP for accessing current documentation.
The tutorial outlines setting up the agent with essential tools and executing commands to create and manage codebases efficiently. The example demonstrates building a NextJS app, iterating through code improvements, and emphasizes the importance of using isolated and safe environments for command execution in production settings. With detailed steps and code snippets, it primes developers to harness advanced coding capabilities for app development.
gpt5-1_prompting_guide.ipynb
Source: openai/openai-cookbook
The blog post introduces GPT-5.1, highlighting its enhancements over GPT-5, such as a new “none” reasoning mode for low-latency tasks, improved prompt calibration, and enhanced steerability in personality and output formatting. Key migration tips include emphasizing persistence and output detail, adapting personality for user interactions, and ensuring better instruction adherence. The model also allows for refined user updates during agent deployments, prioritizing communication clarity and completeness. Innovative coding agent guidance is provided to manage response length effectively, avoid unnecessary verbosity, and enhance efficiency through a structured tool-calling process. Additionally, the post emphasizes the improved parallel tool calling capability and the efficient execution model of the new “none” reasoning mode, making GPT-5.1 suitable for various applications.
gpt-5-codex_prompting_guide.ipynb
Source: openai/openai-cookbook
The blog post outlines the features and best practices for prompting the new GPT-5-Codex, which is designed specifically for coding tasks. Key enhancements include improved steerability for complex engineering tasks, adaptive reasoning based on task complexity, and proficiency in code reviews. Users are advised that GPT-5-Codex requires different prompting techniques than its predecessor, emphasizing a “less is more” approach. Essential guiding principles include using minimal prompts and avoiding lengthy instructions or preambles. Additionally, the model operates within a sandboxed environment with specific rules for network and filesystem access, requiring user approval for certain actions to ensure safety. The article also provides guidelines for integrating the model efficiently into coding workflows, particularly via the Codex CLI and IDE environments.