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TECHNOLOGY, EXAMINED.AI CODING / MODELS / PRACTICE

The field guide

The best model for which job?

A model choice becomes interesting when you stop asking for a winner and start describing the work.

A quick syntax question, an unfamiliar repository, and a delicate migration ask for different things. Build your shortlist around the work you repeat. A convincing explanation is useful, but it cannot substitute for a correct change.

Make a small evaluation set

Choose three tasks from a repository you know: explain a real failure, make a bounded edit, and identify a missing edge case. Keep the starting commit, instructions, and available files identical. Use a disposable branch and record any help you give.

Keep separate scorecards

QuestionEvidence to keep
Did it solve the problem?Reproduction before; focused check after.
Did it stay in scope?The complete diff, including deletions.
Could you review it?Assumptions, file references, and uncertainty.
Does it fit your setup?Actual endpoint behavior and observed usage.

Read the failures, too

A single successful run tells you little about consistency. Repeat the difficult case and note whether the model asks for missing evidence or fills the gap with a guess. Do not combine every observation into one mysterious score: a cheap answer that needs extensive repair may be the expensive choice.

Choose provisionally

Select a default for a named task class, then write down why. Revisit the choice when the model, provider configuration, or workload changes. These are your observations in one setup, not universal benchmark results.