How to choose a model for coding agents: test on your repo

To choose a model for coding, use leaderboards only to pick two or three candidates, then run the same five tasks from your own repository with each one and compare the results. The best model is the one that finishes your kind of work with the fewest turns and the fewest fixes from you, at a cost and privacy level you accept.

A model that tops a benchmark can still be the wrong pick for your stack, your budget or your company’s rules. An afternoon of testing tells you more than a month of reading launch posts.

What a good result looks like

Same task, two models, same starting commit:

task: "Expired coupons are still accepted at checkout. Fix it and add a test."

model A   9 turns   touched 2 files   tests pass   diff ready to merge
model B  22 turns   touched 6 files   tests pass   renamed helpers nobody asked for

Both “solved” it. Model A is the better coding model for you: it stayed in scope, used fewer tokens, and left less for you to review.

Which model is best for coding right now?

There is no stable answer, and any page that gives one is out of date within weeks. New models ship often, and prices and limits change with them.

What does stay true is what to look for:

It uses tools reliably. An agent calls tools dozens of times per task: read a file, search, run tests. Watch for malformed calls, the same call repeated, edits to the wrong file, and “done!” while tests still fail.
It keeps following your instructions. “Don’t touch the tests” must still hold on turn forty.
It handles a long context well. The advertised maximum matters less than how well it works when the context is full. Anthropic’s docs call the decline “context rot”: accuracy drops as the context grows.
It knows when to stop. Good agent models finish, report and ask. Weaker ones loop, or wander off into improvements you didn’t ask for.

Are coding benchmarks worth looking at?

Yes, for the short list. Terminal-Bench ranks a model inside a real agent on hard terminal tasks; SWE-bench Pro and SWE-rebench test models on fresh GitHub issues. Skip SWE-bench Verified: the top models all score nearly the same on it, and OpenAI stopped reporting it in February 2026. AI coding benchmarks, explained covers which leaderboards to trust and how to read them.

Two limits:

It is someone else’s code. Your stack, test setup and conventions may favor a different model.
The agent around the model matters. A benchmark run uses its own tools and prompts, not the agent you use every day. Your own test is the only one that includes both.

How do I compare models on my own code?

Pick five real tasks from your recent work: a bug fix, a small feature, a new test, a refactor, and a question like “explain how billing works in this repo”.
Write each task once, with a clear definition of done. See how to write good tasks for coding agents.
Run each task with each model in the same agent, from the same commit, with the same permissions. Change only the model.
Note whether it finished, how many turns it took, how long, and what it cost.
Review each diff as you would a pull request. See how to review AI-written code.

Give each run its own branch, so each model starts from the same commit and you keep every result to compare:

git switch -c try-model-a
git add -A && git commit -m "model-a attempt"
git switch main

Run the first line before the agent starts and the other two after it finishes. Then repeat with try-model-b.

Keep a simple sheet:

task              model-a            model-b            model-c
fix-coupon-bug    pass, 9 turns      pass, 22 turns     fail
add-csv-export    pass, in scope     pass, overreach    pass, in scope
explain-billing   correct            correct            wrong module

Five tasks won’t give you statistics. They will reliably show you a model that doesn’t fit your codebase.

To switch models during a session: /model in Claude Code and in Codex, which also sets the reasoning level where the model supports it. In Contenox, the Model chip on the message box lists the models from every provider you connected, including local ones, so you can run the same task through each from one place. See the quickstart.

Can I automate the comparison?

Yes. Give each model its own git worktree, run the same task in script mode, and commit what each produced. With Claude Code:

task="Expired coupons are still accepted at checkout. Fix it and add a test. Done when npm test passes."
for model in opus sonnet; do
  git worktree add -b "try-$model" "../try-$model"
  cd "../try-$model"
  npm install
  claude -p "$task" --model "$model" --max-turns 40 --allowedTools "Read" "Edit" "Write" "Bash(npm *)" > "../result-$model.md"
  git add -A && git commit -qm "$model attempt"
  cd -
done

Then compare the branches: git diff main..try-opus --stat and git diff main..try-sonnet --stat show how much each touched, and the result-*.md files hold each model’s own summary. Run your tests on each branch yourself rather than trusting the summaries.

Two things to watch: each worktree needs its own dependency install, and two runs at once can collide on ports or a shared database, so run them one after another as above. The same loop works with any agent that has a script mode and a model flag. In Codex, the claude -p line becomes codex exec -m "$model" --sandbox workspace-write "$task"; in OpenCode, opencode run -m "provider/$model" "$task".

Is the most expensive model worth it?

Compare cost per finished task, not price per token. A cheap model that needs three attempts and your help can cost more than an expensive one that gets it right once. Your time reviewing and fixing is part of the price too.

Reasoning (“thinking”) settings matter as much as the model. Higher settings use more output tokens on every turn. Use them for hard debugging and design, not for renaming a variable. More on this in AI coding costs.

Should I use more than one model?

Usually, yes. A common split:

A strong model for planning, hard bugs and code nobody on the team knows well.
A faster, cheaper model for routine edits, tests and boilerplate.
A local model for code that must not leave your machine, or for offline work.

Claude Code’s own cost guide suggests the same: use the cheaper model for most coding tasks and keep the top one for complex reasoning.

Where does my code go?

For many companies, this decides the shortlist before quality does.

A provider’s API or subscription: your prompts and code go to that provider. Read its data retention and training terms for your plan.
Your company’s cloud account (for example a model served through Amazon Bedrock or Google Cloud): your organization’s existing agreements apply.
A local model: nothing leaves your machine. See how to run local LLMs for coding.

AI coding costs: why cost per task beats price per token
How to run local LLMs for coding: when a local model is good enough
Best Ollama models for coding: local candidates by GPU size
How to write good tasks for coding agents: a fair test needs a clear task
How to review AI-written code: judge the diff, not the summary