Use tasks that resemble your work
A benchmark question with one correct answer can be useful, but it does not tell you how an agent will behave in your codebase. Build a small task set from real maintenance work: explain a subsystem, find a regression, review a diff, add a focused test, make a bounded change, and recover from a failed command.
Keep tasks safe and reversible. A disposable repository or branch lets you compare agents without turning an evaluation into a production incident.
Measure the workflow, not just the final text
A correct patch is not enough if the agent used excessive context, ignored a permission boundary, changed unrelated files, or could not explain what it did. Evaluate the path as well as the outcome.
- Did it identify the right files and constraints?
- Did it use tools deliberately and recover from failures?
- Did it keep changes scoped and produce a reviewable diff?
- Did the requested validation actually run and pass?
Compare model and harness separately
The same model can behave differently with different context selection, tool schemas, permissions, and retry behavior. Record the provider, model, prompt, workspace state, and policy so you know what changed between runs.
A provider-flexible runtime such as Truss makes this comparison more useful because the client workflow and tool boundaries can remain consistent while you test a different model profile.
Next steps
Put it to work in your own workspace.
Pick the surface that fits your day, then connect a model you control.
FAQ
Questions developers ask before switching.
What is the best benchmark for a coding agent?
The best benchmark is a repeatable set of safe tasks drawn from the work your team actually does, with clear expected evidence and validation.
Should I evaluate an agent with one large feature?
Use a mix. Large features reveal planning and persistence, but smaller tasks make failures easier to diagnose and compare.