Which model for coding work

The decision

Coding work splits into implementation, review, debugging, and architecture. The right model depends on which part you are doing and whether the product can read files, run commands, inspect errors, or apply changes. For task prompts, start with agentic coding workflows or code review prompts.

Start with the coding-agent surface your workflow already uses when it has repository access. Use a chat model for review, explanation, and planning when direct repository tools are not needed.

Model fit

Claude is a strong fit for review, long-context code reading, and careful reasoning over a diff. OpenAI’s code-generation docs position Codex as its coding-agent surface for writing, reviewing, and debugging code. Gemini is useful when the coding task includes screenshots or diagrams because Gemini’s docs describe broad image-understanding capabilities. Grok’s model docs position Grok 4.6 for code and agentic tool calling, so it is worth testing when your stack already uses xAI.

Prompting pattern

Task: fix the failing import flow.
Evidence: paste the exact error, failing command, and changed files.
Constraints: keep the public API stable and avoid unrelated refactors.
Verification: run the smallest relevant test first, then the full affected suite.

Report the root cause, files changed, and verification result.

What to avoid

• Asking a plain chat model to “fix the repo” without giving it the relevant files.
• Letting the model refactor before it reproduces the bug.
• Treating benchmark reputation as stronger than a local test run.
• Using a cheap high-volume model for a risky migration just because the prompt is short.