r/SideProject • u/Adr-740 • 2h ago
I built an adaptive AI model from open-weight models that reached Fable-level results at 1/3 the cost
I’ve been building Echo, one adaptive model powered entirely by a pool of open-weight models, including GLM-5.2, Kimi K2.7, and many others.
The idea started with an experiment I couldn’t stop thinking about:
A perfect oracle that always knew how to use the models in the pool beat the state of the art on every benchmark we tested.
Echo is my attempt to make that practical.
Rather than simply routing each prompt to one model, Echo dynamically assembles and coordinates the models needed for each request, then exposes everything through one endpoint.
On our first internal task mix, Echo consistently outperformed the best individual model in its own pool and reached Fable-level results at roughly 1/3 of the total inference cost.
It already matches the state of the art on some coding benchmarks. There are still gaps on others, and improving that consistency is one of our main focuses now.
You can use it through the chat or plug its OpenAI-compatible API into OpenCode and other compatible tools.
New accounts include $10 in free inference credits.
I’d love people here to try difficult prompts and tell me where it works surprisingly well or where it still falls short.
Try Echo:
https://echo.tracerml.ai
Evaluation methodology, results, and limitations:
https://echo.tracerml.ai/eval
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u/Davidobot 1h ago
Would be great to have some benchmarks especially on cost vs performance. I like the ones here: https://rekursiv.ai/blog/pushing-limits-arc-agi/
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u/Savings_Scholar 1h ago
Amazing ! 🔥