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Benchmark

Benchmark commands are product-scoped and read-only. Pass --scope <org-slug>/<product-slug>.
These commands read your product’s results on the public TPC Benchmarks leaderboard: whether AI coding agents can go from your documentation to a first working integration. They do not create experiments. To set up your own competitive benchmark experiments, see Agent Experience and tpc sim ax setup --type benchmark. This is product data for signed-in TPC users. The public TPC Benchmarks plugin for ChatGPT is a separate product, described in TPC Benchmarks in ChatGPT. Every command accepts --format json and prints the raw response data instead of a table.

How your product matches a vendor

A product shows results when it matches a benchmarked vendor in one of two ways. A product that matches no vendor prints Not benchmarked with a hint to request a benchmark from the product’s Benchmarks page in TPC.

Summary

Prints the vendor, how it matched (linked or domain), the completion rate as passed over total, the experience score, the readiness score, the date it was last tested, the public results link, and a table with one row per task. Tasks that TPC cannot score yet show the status unsupported and no completion rate.

Trend

Prints the daily readiness score, then one table per harness and model setup with each day’s runs, completion rate, experience score, and the titles of the tasks tested that day. A day is flagged tasks changed when the set of tasks, or a task’s version, differs from the previous day of the same setup. Use the flag to tell a real change in results from a change in the benchmark itself. Run trends need runs on at least three different days. Until then the command prints Not enough runs yet.

Suggestions

Suggestions appear only for linked products. Open suggestions are grouped by effort, quickest first: low, low-medium, and medium+. Each experience suggestion is labeled Spec requirement when a cited specification requires it, with the specifications listed under Specs, or Optional · TPC readiness credit when it goes beyond the specification and earns TPC readiness credit. Experience suggestions are marked AI-generated. TPC drafts them from the failed or struggling benchmark runs and reviews them before publishing. A recheck confirms whether the problem still happens, so treat the evidence date as the freshness of the suggestion. Closed suggestions follow the open ones, newest first. A suggestion closes when the issue stops showing up in scans or runs, or when your team marks it fixed or declines it. Responding to a suggestion, claiming a vendor, and requesting a benchmark are done in TPC, not from the CLI.