ChatGPT Codex 2026: Vad Som Förändrades, Vad Det Kan Göra och Hur Du Använder Det Effektivt

Codex Is Not What It Was

When OpenAI launched Codex in 2021, it was a code-completion model — the technology behind GitHub Copilot. The name has since evolved to mean something entirely different: an autonomous software engineering agent built into ChatGPT that can take a task, work in an isolated environment, and produce working code with tests and documentation.

The new Codex is not a completion engine. It is a software engineer you task through a chat interface.

What Codex Can Do Now

Isolated, sandboxed execution

Codex runs in its own sandboxed environment. It can install packages, run commands, execute tests, and read output — a complete execution environment independent of your local machine. You describe a task; Codex works through it autonomously, running and observing code at each step.

GitHub integration

Codex can connect to your GitHub repositories. Give it read access to your codebase and it can understand existing patterns, make changes consistent with your conventions, and open pull requests. This is the "Codex as junior developer" workflow that gets the most attention — and generates the most discussion about what works and what does not.

Multi-file, multi-step tasks

Unlike earlier AI coding tools that operated on single files, Codex understands the multi-file scope of real tasks. "Add rate limiting to the authentication endpoints" involves understanding the existing middleware structure, adding the implementation, updating tests, and documenting the change. Codex can plan and execute across all of these.

Parallel task execution

One of the genuinely new capabilities: you can assign multiple independent tasks to Codex simultaneously. While one instance is implementing a feature, another is writing tests, and a third is fixing a bug — all running in parallel sandboxes. This changes the productivity arithmetic for small engineering teams.

What Changed Most Recently

The most significant recent changes to Codex are in reliability and task complexity handling:

• Longer tasks: Codex can now maintain coherent context over longer-running tasks without losing track of the goal or contradicting earlier steps.
• Better test generation: Test quality has improved significantly. Generated tests now better cover edge cases and reflect real usage patterns rather than trivial happy-path tests.
• Improved error recovery: When Codex's first approach fails (a test fails, a command errors), recovery and adaptation quality has improved. It still gets stuck sometimes, but less often on solvable problems.
• Coding style adherence: Better at adopting the patterns in your codebase rather than generating code in a generic style.

Honest Limitations

Security review is non-negotiable. Codex generates code that works but is not always secure. Missing input validation, insufficient auth checks, and incorrect error handling patterns appear regularly. Never merge Codex-generated code into production paths without security review.

Architecture decisions remain weak. Ask Codex to implement something and it will implement it. Ask it what you should build, and the answer will be generic. System design judgment remains a human responsibility.

Context window at scale. For very large codebases, Codex works on what it can see. It can miss patterns in code it has not been shown. Providing explicit pointers to relevant files improves output quality significantly.

Codex vs Claude Code: The Honest Comparison

Dimension	Codex (ChatGPT)	Claude Code
Task execution (agentic)	Strong, sandboxed	Strong, local environment
GitHub PR workflow	Native integration	Via hooks and CLI
Code quality / instructions	Good	Excellent — better instruction following
Extensibility (skills, MCP)	Limited	Rich — skills, MCP, hooks, memory
Local codebase access	GitHub only	Full local filesystem
Model breadth (GPT-5 features)	Full OpenAI stack	Anthropic stack

Practical Starting Point

If you have not tried Codex as a software agent (as distinct from GitHub Copilot's autocomplete): start with a well-scoped, isolated task. "Add input validation to this API endpoint and write tests for the validation logic." Give it the relevant files. Review the output before merging. Build trust incrementally.

The developers who will benefit most from Codex are those who treat it as an amplifier — capturing the productivity gain while maintaining the code quality standards that prevent technical debt from accumulating.