alt Hacker NewsShow HN: Echo – Fable-level results at 1/3 the cost using open-weight models
I’ve been building Echo (https://echo.tracerml.ai/), an experiment in making one AI system out of a pool of open-weight models rather than choosing a single model and using it for every task.
It started with a simple experiment. I took a group of models, including GLM-5.2, Kimi K2.7 and others, and ran them on the same evaluations. Then I measured what would happen if, for each problem, you somehow knew in advance which models would be useful and how their outputs should be combined.
That hypothetical system performed substantially better than any individual model in the pool. Of course, it is not something you can actually deploy because it relies on knowing which decisions were good after seeing the result. Echo is my attempt to recover some of that advantage without having that information in advance.
For each request, Echo decides how much computation to allocate, which models should participate, and how their work should be combined. Some prompts may only need a relatively small amount of inference, while others benefit from multiple models working on different parts of the problem.
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.
On my first evaluation mix, Echo consistently performed better than the best individual model in its pool. It also reached roughly the same aggregate result as Fable, which I used as one of the stronger comparison systems, at around one third of the inference cost.
There are still some cases where Echo makes the wrong allocation or combination decision. I’m currently spending a lot of time understanding those failures, as well as testing whether the same approach holds up on coding and agentic tasks where measuring the quality of each decision becomes much harder.
I built a chat interface (echo.tracerml.ai) and an OpenAI-compatible API (https://echo.tracerml.ai/docs/api) so the system can be tested outside the evaluation setup.
Here is a short/high level video on how it works: https://www.youtube.com/watch?v=lJFJSvOdXhg
I wrote up the evaluation methodology, individual model results, costs and current limitations here: https://echo.tracerml.ai/eval
I would love for you to try it! Especially if you hit any weird failure cases or places where the allocation looks unintuitive.
Comments
So this is the dogpile.com of the askjeeves, alta vista, and lycos approach? Time is a flat circle?
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.
> 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.
I'm curious if there is measurable value in diversity of thought, and if there's diminishing returns on a single models thought pattern.
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?
No single signin. Privacy policy allows training. No try it first without credit card. It's a good idea, but this looks premature.
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
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."
we did it with 1/10 the cost with Prometheus-1.0 and further https://trustedrouter.com/blog/prometheus-2-new-draco-state-...
Replace "Show HN:" with "Advertisement:" ?
Sounds like another token router. Not sure what is the benifit compared with codex, I used 5.6 Sol, codex already route some of the task to luna not sol.
Seems similar to Openrouter Fusion - https://openrouter.ai/docs/guides/routing/routers/fusion-rou...
This reminds me on OpenRouters report that combining multiple different models gave comparable performance to Fable 5. I think this approach has lots of potential. Maybe OpenAi was ahead of its time with GPT-5 (it being a router to different models rather than just being one new model)
I think approaches like this have potential. Only time will tell. This reminds me of the mixture of experts taken by deepseek r2 (I think it was r2, at least), but less specific models I guess.
I have often wondered how tools like GHCP choose the best model for the job when set to "auto".
So the word security or any topic related to it is mentioned and it flips to an older gen model? Fable is nearly useless now it you do anything around auth.
The "ensemble of models" is nothing new. It's just that it's not really a moat that can be monetized. In the end you're always paying for something. You can engineer an elaborate harness with multiple models but it is not going to particularly solve a novel problem that the frontier models can with the same level of efficiency. You're saving money and paying with time. You're going to pay with something one way or another no matter what.
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.
I'm not an expert on this, but this sounds a lot like a larger-scale MoE (Mixture of Experts) type of architecture.
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.
There's so many people reimplementing Sakana fugu from its two ICLR papers but no open source version of it.
Intuitively, your savings depend heavily on how hard the tasks are in the first place. If you have a base rate where 99% of your tasks can be routed to a cheap model, yeah, you can save a ton by not using Fable for that.
So “1/3 the cost” really depends.
I’m very confused on what this is, my initial thought was “oh nice, open source router.”
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.
What signals does Echo use to decide which models should participate before it has seen their outputs?
I have been using this : https://magnitude.dev/ for a while now. Is it something similar you are doing? I would love to have something that would connect to my codex, Claude, and opencode subscription rather than having to make a new subscription.
> Fable-level results at 1/3 the cost using open-weight models
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.
M-o-MoE
By the way, is it just me or opus 4.8 is much better at some programming tasks than fable? I've been really disappointed lately, i stopped using it evem though it's "premium on my subscription"
such a scam, there is only one fable-like model, that somewhat behind, it cost half, not 3x. so from here you can stop reading.
enjoying how people are re-discovering ensemble methods.
I don’t find the recent spate of blog posts and systems delegating and combining LLMs to get better performance particularly interesting. Especially given that anyone who’s taken an ML 101 course has learned about ensemble methods.
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.
Fable-level, yea, but can it run gstack?
Might want to rethink the name to avoid an Amazon issue.
If you copy Perplexity, they let you have the first few rounds of chat for free to get you going before asking to sign up.
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I have been trying something like this with local only models. I think I’ve tried everything that will fit in 96GB alone and in concert with each other using real project data (Rails). I haven’t found much benefit to anything other than Qwen 3.6 27b dense feeding a Claude final pass. I really thought layering was going to work like the law of large numbers, but for my workload it just hasn’t.