Benchmark fixture

Best AI for Coding

A benchmark fixture page for evaluating AI coding assistants without publishing unsupported rankings.

This page defines the test shape for AI coding assistants. It intentionally avoids naming a winner until the site has a dated run log with prompts, outputs, reviewer notes, and validation evidence.

The benchmark is designed for operators, not model fandom. A coding assistant should be judged by verified delivery: whether the patch solves the task, keeps the diff scoped, handles edge cases, and reduces human review effort without weakening the project.

What is being tested?

This rubric tests three common coding-assistant jobs:

• Repair: fix a failing behavior with the smallest useful change.
• Extension: add a small feature while following existing patterns.
• Hardening: improve input handling when hostile or malformed data appears.

Those tasks are deliberately narrow. They expose whether the assistant can work inside constraints instead of rewriting the project around its preferred shape.

Run log requirements

This page can move from rubric ready to tested only after each candidate has a dated run log, fixture inputs, raw outputs or reviewer notes, scoring table, failure examples, and a scheduled retest date.

Each candidate run should record:

• Tool name, model, version, plan tier, and relevant settings.
• Repository fixture and starting commit.
• Prompt or task instruction.
• Commands run before and after the patch.
• Files changed and line count.
• Test/build/lint result.
• Reviewer notes and failure examples.

Without those fields, the page remains a benchmark fixture rather than a recommendation page.

How should scores be interpreted?

Correctness is weighted highest because a fast wrong patch is negative leverage. Diff quality matters because reviewers pay for every unnecessary change. Security and edge cases matter because coding agents often pass happy-path tests while missing hostile input. Review effort matters because a tool that generates large patches can make teams slower even when the final code works.

The scoring table is not a universal truth. It is a decision aid. A solo developer working on prototypes may weight speed more heavily. A team maintaining customer infrastructure should weight security, rollback, and review effort more heavily.

Recommendation segments

When evidence exists, recommendations should be segmented for solo developers, team reviewers, privacy-sensitive workflows, and teams that value low review effort over broad generation features.

Example segments to publish only after evidence exists:

• Solo developer: fastest verified patch with low setup cost.
• Team reviewer: highest signal findings with low false-positive rate.
• Privacy-sensitive codebase: strongest local or enterprise data boundary.
• Legacy codebase: best adherence to existing patterns and smallest safe diff.

What should disqualify a candidate?

A candidate should be marked high risk when it removes tests, invents APIs, modifies unrelated modules, hides validation failures, leaks secrets into logs, or cannot produce a reproducible path from task to result.

If multiple candidates fail a fixture, the benchmark should publish the failure rather than force a winner. “No public recommendation yet” is better than a fake ranking.

Use the Benchmark Run Log to run your own comparison, and pair it with How to Verify AI-Generated Code before merging assistant-created patches.