We NEED a harness benchmark leaderboard

Reddit r/AI_Agents Tools

Summary

This article argues for the need of a benchmark leaderboard that compares AI model harnesses (e.g., KimiCode vs OpenCode vs Codex) rather than just models themselves, proposing a repo to test model+harness combinations on cost, runtime, token usage, and score.

This question is always sitting in the back of my brain: If I’m using a Kimi model, is KimiCode actually better than OpenCode for interacting with it? What if a lower-intelligence GPT model in OpenCode performs better than the same model in Codex? What if the “best” setup is not just about the model, but about the harness wrapped around it? Today we have tons of AI model leaderboards, but almost nothing comparing the harnesses that use those models. That’s why I made this repo: (down in the comments section) The idea is simple: test and publish results for every combination of: model intelligence/reasoning level harness benchmark cost runtime token usage From that, we can start answering questions like: Which harness gets the best score from the same model? Which harness burns the fewest tokens? The repo was one-shotted by GPT-5.5 xhigh on Codex, I’m too poor to run the full benchmarks myself though, haha. Feel free to fork it, roast it, nuke the idea completely, or build a better version. Mostly I just want to spark the idea that harness benchmarking is an unexplored part of vibe coding / coding-agent evaluation.
Original Article

Similar Articles

@AntCaveClub: What exactly is Harness? Harness = Evaluation Harness. In AI, "harness" is industry jargon – a set of tools to "harness" a model and run standardized evaluations. The industry standard is EleutherAI's lm-e…

X AI KOLs Timeline

This article deeply explains the importance of the evaluation framework (Harness) in AI, analyzes the strategic significance of DeepSeek building its own Harness team, and compares the differences between the open-source lm-evaluation-harness and an in-house system.

Stop Comparing LLM Agents Without Disclosing the Harness

arXiv cs.AI

This position paper argues that in long-horizon LLM agent tasks, the execution harness often determines performance more than the model itself, and current benchmarks misattribute harness-level gains to model improvements. It proposes a harness-aware evaluation framework with disclosure standards and variance decomposition protocols.