Choosing a coding agent shouldn't be a guess.
New models and agents keep arriving, at very different prices. Most companies still choose between them on benchmarks, marketing and the loudest opinion in the room. Yardstick replaces all three with evidence from your own work.
Benchmarks aren't your codebase.
Public benchmarks are useful for model builders and close to useless for buyers. They measure standard tasks with no knowledge of your code, your conventions or your tests, and once a benchmark matters commercially it gets optimized against.
Public benchmark scores
What most decisions rest on today- Standard, synthetic tasks
- No knowledge of your codebase
- Not your conventions or tests
- Gameable, and quickly saturated
Your engineers, your repo
What the decision actually depends on- Real tasks on real code
- Judged by engineers who know it
- Checked against your own CI
- Re-run whenever a new model ships
Opinions follow brands.
Ask engineers which agent is better and you mostly learn which one they used first, or which company they like. That isn't a criticism of engineers. Everyone does it, which is why taste tests are blind. Yardstick hides which setup produced which result, so a preference can only come from the code.
Better isn't the same as worth it.
A setup that is slightly better at five times the price may be the right choice or a waste of money, depending on the work. Yardstick puts the verdict and what each setup costs side by side, so that becomes a decision rather than a guess.
Why it works.
Blind and fair
Same starting point, same instructions, and no hint of which setup produced which result, so preferences reflect quality rather than brand.
Your engineers decide
The verdict rests on your engineers' blind picks. Cost and CI results are shown alongside, so you see quality and price together.
Your keys, your code
It runs in your own GitHub Actions with your own API keys. The keys stay on your runners and never reach Yardstick. Your code never goes to China-based agents or model hosts, even when you test a Chinese-built open model like Kimi K3.
What you get.
spendBetter spending decisions
Answers like "the cheaper model is no worse on your work" can cut AI spend substantially.
rolloutConfident rollouts
Standardize on a tool with evidence your engineering leaders and finance team will accept.
paceKeep up with the market
Test each new model or setting on your own work as it arrives.
customizationProof that customizations help
Check whether your instruction files, skills or agent settings actually improve results.
Built by people who did this at scale.
Yardstick comes out of years of building experimentation and ads marketplace systems, where a badly measured experiment costs real money immediately and the statistics have to hold up under scrutiny. We are applying that discipline to the newest and least measured line item in engineering.
Run one pilot. Settle one decision.
One agent question, your engineers, real work, and an answer you can defend instead of an argument.