OpenBench – A benchmark for comparing coding-agent harnesses

OpenBench is a benchmark aimed at comparing coding-agent harnesses, the layers that wrap models with tools, prompts, sandboxes, and orchestration. The item surfaced through HN AI Agents via a post from mattlam_ and, at listing time, sat at 1 point with 0 comments on Hacker News.

A harness is not the model. It is the control plane: how tasks are issued, how file and shell access is granted, how retries and recovery work, and how success is scored. OpenBench’s stated job is to put those systems on a common yardstick so differences in agent behavior are less often blamed only on the underlying model.

For engineers building or buying coding agents, that split matters. Two stacks on the same model can diverge hard on reliability, latency, and cost once harness design differs. A dedicated harness benchmark gives a way to test the integration layer on its own, instead of treating “agent quality” as a single opaque score.

The market is full of agent frameworks, IDE copilots, and autonomous coding tools that compete on demos and blog benchmarks. Those often mix model choice, prompt craft, tool policy, and evaluation harness into one result. OpenBench sits in the narrower gap: comparing the harnesses themselves so product claims can be checked at the system boundary where most of the engineering risk lives.

What to watch next is whether OpenBench becomes a shared reference in agent evaluations, and whether harness authors publish results against it rather than only against ad hoc task suites. Track the discussion thread and the original post for methodology, task set, and any published comparisons as they appear.