DEDUPED Hot AI repos, turned into use cases Get 5 a day

How to compare 12 coding-agent harness results side by side with Eval

Open the live FrontierHarness results to see pass rate, cost, and speed for each harness.

Who it is forA developer picking a coding-agent harness who wants evidence before adopting one.

The problemVendor claims about coding agents rarely hold the model and tasks constant, so harness choice is hard to judge.

The outcomeYou can see which harnesses passed the most tasks and which cost the least per pass, with the same model and tasks behind each row.

What you need first

  • A Runta account, with the runta CLI installed and authenticated via runta login
  • jq, Node.js 18 or newer, and Python 3.9 or newer
  • A provider API key for Kimi K3 (FIREWORKS_API_KEY, MOONSHOT_API_KEY, OPENROUTER_API_KEY, or TOGETHER_API_KEY)
  • A harness repository you can build on a clean Linux box, plus an install script

Steps

  1. Open the full results table in the README to see pass rate, median cost per pass, cache, and median time for each of the 12 configurations.
  2. Open the interactive report at https://frontierharness.org to see failed runs, total cost per task, and task-level results.
  3. Read the launch article at https://runta.com/blog/introducing-frontierharness-eval/ for the evaluation design and analysis.

Prompt for your agent

Open the repo frontier-harness-eval/eval and read the Full results table in its README. Report the harness with the highest pass rate and the harness with the lowest median cost per pass, with their values. Verify that both values match the table row for each harness.

Caveat. The published numbers come from one model and one set of 30 tasks, so the ranking may not carry over to your own work.

Share: X LinkedIn Hacker News Email Copy link

Disclaimer

Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked "Seen in the wild", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email [email protected] and we will look at it promptly. Full disclaimer page