Show HN: Echo – Fable-level results at 1/3 the cost using open-weight models

I've literally taken one step on your website - the one your site design invited me to take - and immediately got tripped up. I'm not coming back.

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.

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.

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."

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

The industry has largely focused on building larger models, but your results suggest that intelligently routing requests to the right combination of specialized models can deliver greater gains at a much lower cost.

It also reinforces the idea that weaker models are not necessarily obsolete. They may simply excel in different areas and become much more valuable when combined with others. I'm curious to see whether this still holds for coding and agentic tasks, where choosing the right models is likely much more challenging.

in short, all of them are token diarrhea. Annoyingly logorrheic. they output so much useless stuff that it comes to the point that it makes me thing if that's not on purpose.

it was not because of price, but quality, I started testing other models and honestly, glm 5.2 is FAR SUPERIOR than fable 5 in every aspect. It comes to a surprise when glm 5.2 does not finish the task successfully. Kimi k2.7 does require a bit more of guidance but still better experience than opus 4.8. I have not yet the chance of trying k3.

openai latest model are.... ridiculously bad at software design and implementation.

(note i am only speaking to the domain of my work which has lots of data analysis, machine learning and software engineering.)

I find that very off-turning!

I have often wondered how tools like GHCP choose the best model for the job when set to "auto".

For example, compute X tokens with model A, then feed those into model B, etc. to get chain of thought through a diverse set of mdoels rather than chain of thought through a heterogeneous chain.

Humans seem to strongly believe echo chambers are bad. Are LLMs the same?

As I understand it, in an MoE model, you essentially have hundreds of smaller sub-models ("experts") that are good at different tasks, and for every generated token, a single "master" model chooses which ones are most relevant to participate, and you only activate them.

So “1/3 the cost” really depends.

Don't want to derail what you're trying to do with Echo in case I'm wide of the mark, but yeah even in that case, if you hadn't considered that use case for it, I reckon there will, probably inside six months, be a substantial market for non-technical users who are sick of seeing ads in a service they already pay a subscription for, and who don't care what the underlying model is - or, indeed, don't even understand the concept of an "underlying model" because they interface with AI as a product.

You have already taken the HFG idea way further than I had even thought of yet, and I feel vindicated in seeing someone else do it. I wish you the very best!

But we get ~$2500/mo worth of Fable credits for $200/mo on Anthropic pan? I'm still confused why people (who don't have to use API billing) are chasing open weight models based on cost.

The frontier providers aren't dumb. They charge what they charge because they know this. If you think Fable is too expensive then the type of problems you are solving don't demand that level of capability.

If you are working on something cutting edge, something truly novel, the cost of frontier AI is well worth its price.

With all that being said. No one is going to complain if we can get the same capability at a lower cost. And I mean true parity. Not trading space for time.

While an LLM isn’t what you’d traditionally consider a weak learner, the theorems on learning systems clearly point to them being so in this context. The feigned surprise at combining them to yield better results seems disingenuous.

Even so, the work to predict which models are best suited for which task, how to delegate, and how to combine their outputs is interesting, especially if you’re placing a cost minimization objective on it. That said, this isn’t too far off from what many AI labs are already doing.

I go to the website…and it’s a sign up. I expected a repo. Otherwise how do I use it? As a SaaS? Yeah right.

Oh well I guess at least the benchmarks are good…I find the benchmarks and many are either not present or are not what the title claims.

My main question is how this has so many updoots from HN, probably the passerby not looking closer for sure.

I mean no offense and I really do wish you best on this, but it seems like what we used to call back in the day, vaporware.

https://www.ycombinator.com/companies?query=tracerml

I don't see it?