Echo - Dynamic AI system combining open-weight models for cost efficiency
Show HN: Echo – Fable-level results at 1/3 the cost using open-weight models
Echo is an innovative AI system that dynamically orchestrates a pool of open-weight models, including GLM-5.2 and Kimi K2.7, to solve tasks more effectively than any single model. Instead of relying on one model for every request, Echo intelligently decides which models to use and how to combine their outputs based on the specific problem. This approach achieves performance comparable to Fable while reducing inference costs by roughly two-thirds. Users can test the system via a chat interface or an OpenAI-compatible API, making advanced multi-model reasoning accessible and affordable for developers and researchers alike.
One thing that surprised me while building it was how complementary the models are. A model that is clearly weaker overall can still be extremely useful on particular problems or as part of a combination.
- dluan
So this is the dogpile.com of the askjeeves, alta vista, and lycos approach? Time is a flat circle?
- adam_rida
thanks to everyone for taking the time to try Echo and share feedback, this is precisely why i wanted to launch early.
i am going to try to address a couple of topics that came up often:
- i'll keep publishing stronger evals, including more difficult coding and agentic benchmarks, to map out more precisely the differences with sota
- the public eval dashboard will keep expanding and be updated (very open to more benchmark suggestions as well!)
- some people found issues in the eval dashboard ui and the sign up flow, should be now all fixed in prod
some important precisions as well:
- NO credit card is required to try Echo
- each acount includes 10$ of free credits to try on both the API and the chat
on the approach itself: the idea i'm exploring is more broader than model routing, i'm looking at how to allocate inference efficiently across open-weight models, deciding not only which models to use, but also how much computation a request deserves and how intermediate work should be combined.
ensembling by itself is not new. since random forests and probably even before in statistics/classic ml we knew that bringing multiple models together can outperform individual ones. the interesting problem for Echo is how to model and leverage this without paying the full ensemble cost at each request.
while there are conceptual similarities with systems like Fusion or Fugu, the architecture and optimization objective are different.
thanks again for all the thoughtful feedback.
- cheema33
> Fable-level results at 1/3 the cost
I am guessing this is not targeting those of us on the heavily subsidized $200/mo plans. Sure, these plans may be temporary, but none of us really know how temporary they are. Until then, 1/3rd of the published API pricing is not very appealing.
- kamranjon
No benchmarks, no info on which models are used, ai generated video, just a signup page with nothing else.
Anyhow, this kinda reminds me of that quote about architecture: "We replaced our monolith with micro services so that every outage could be more like a murder mystery."
- tj800x
No single signin. Privacy policy allows training. No try it first without credit card. It's a good idea, but this looks premature.
- subygan
This does not really work well, if you don't know the complexity of the problem ahead of time and ensure all future conversations go to the same model.
Else, you break the cache by doing a round robin of the same conversation across different models. Likely you'll end up paying more than what it would've cost with a cache aware system
- slashdave
Replace "Show HN:" with "Advertisement:" ?
- meander_water
Seems similar to Openrouter Fusion - https://openrouter.ai/docs/guides/routing/routers/fusion-rou...