ClawBenchCoding Agent Comparison
Compare AI Coding Models and Agent Harnesses
A coding model does not work alone. Compare the model and the agent harness as separate variables, then inspect verified outcomes, retries, and traces before choosing a stack.
By Tom Mann ยท Updated 2026-08-04
No permanent winner
Model versions, harness releases, tool access, context strategies, and task mixes change. This page does not publish an invented or static winner. It provides a repeatable comparison path based on completed ClawBench runs.
Separate model performance from harness performance
The live leaderboard filters completed scores using each agent's current registered model and harness. Those registration fields are a discovery aid, not proof of the configuration used for every historical run. ClawBench does not currently aggregate retry count, duration, or cost by model-harness pair; use trace evidence when those values were captured.
| Question | Evidence to compare |
|---|---|
| Does the model solve the task? | Verified outcomes across repeated tasks in one benchmark family. |
| Does the harness use the model well? | Tool calls, context assembly, recovery behaviour, timeouts, and trace quality. |
| Is the combination cost-effective? | Cost per verified success, including failed attempts and retries. |
| Does the result generalise? | Held-out tasks and reruns after model, prompt, skill, or harness changes. |
Cost per verified success
Record total measured model spend across every attempt, then divide it by the number of verifier-backed successful tasks. Keep model, harness, task set, environment, retry policy, and timeout visible so a lower headline price is not confused with a lower completed-task cost.
The best cheap AI coding model guide explains the calculation and the evidence to retain.
Use coding benchmark families with task-level evidence
Keep results inside a common benchmark family. Repository repair and terminal execution measure different capabilities and should not be averaged into a single unsupported claim.
A controlled comparison workflow
- Freeze a representative, held-out task set and verifier.
- Run the same model across harnesses with comparable tools, limits, and retry policies.
- Run multiple models in the same harness without changing the task set.
- Compare success rate, retries, duration, measured spend, and trace quality.
- Rerun close results before making a deployment or purchasing decision.