Dogpile was only a good idea while Search Engines were mostly trash.
You needed to search all of them to find something decent.
That's roughly analogous to today. Ignoring cost, you'd be way better off asking all the LLMs to solve a problem (like coding) where you can verify the answer.
So the question is, for things like that -> can a group of models perform better than frontier models, especially at a reasonable cost?
Fable is not a great value, so unless you're trying to find answers to Erdos questions, you can probably do better on cost.
You can probably typically ask 3 or 4 of the top Chinese models for an answer and get a response for the same Fable question... Given that Fable isn't that much better, it's not surprising you can do better for a large subset of problems.
Mixture of Models, perhaps? tbf to OP, the setup they're proposing has also been recently evangelized by other "ai gateway" products (like OpenRouter, JusCode, Fireworks etc), so there's likely something useful here.
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.
All enterprises users (people using them for work and not side projects) can't get the subsidized plans. I would say subsidized plans are a minority of usage?
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."
It currently exposes 907 stored rows across seven benchmark families, with prompts, outputs, grades, and cost records. More benchmarks are coming soon.
Echo does not disclose its per-request routing decision because that policy is the product. We can, however, publish some of the eligible open-weight model pool, version dates, aggregate allocation mix, and evaluation settings without exposing the request-level recipe.
I've been working on a similar project and I found that it's easy to replicate Fable results if you use saturated benchmarks.
In my project, I wasted a huge amount of time trying to improve GPQA Diamond results above ~93% range. I realized my mistake when Fable dropped and made no improvement on this benchmark vs. Opus.
I wouldn't be surprised if ≈7% of GPQA Diamond questions simply have the wrong answer in the ground truth data, so that getting such a question correct is graded as an error. Most machine-learning benchmarks are rather badly validated.
It's basically trying to replicate OpenRouter, which works pretty well and has a lot of nice features to abstract away any single provider, such as failover, metering, autoswitching, etc. It's actually a really smart infrastructure abstraction.
I just wish this were solving an actual problem rather than being a fairly transparent attempt to say something approximating, "Hey VCs, OpenRouter just became a unicorn but I can basically vibe code it"
Calling it "Fable-level" feels intellectually lazy / dishonest, but then again, what do you expect when there's so much money on the table.
It feels a bit more like Fugu to me, which acts as a multi-LLM orchestrator (though I think Fugu combines open- and closed-weight models), but without being able to see the “secret sauce” behind how any of them decides the number of "plies" each model in the swarm gets, they all feel rather difficult to compare beyond the big public benchmarks...
Fusion is a totally different approach, though similar in the sense that it leverages different models.
Fusion generates many replies then synthesizes. This adds a ton of latency and cost, so it's going to be better only for cases where you're willing to wait a lot and pay a lot more.
Routers (like this project) are a different thing, they can theoretically improve performance and cost at the same time without increasing latency much. I'm a bit skeptical though, since knowing which LLM is going to be better on a cost adjusted basis is hard (see https://artificialanalysis.ai/models/capabilities/coding?cos..., where the cost per task vs. performance is not what you expect, for example comparing Qwen 3.7 Max to GPT Sol.
A project I'm working on is aimed at improving performance without added latency but from a different angle. Instead of waiting for all replies for synthesis (like OpenRouter Fusion), it streams the "best" reply immediately (using a router to pick the best model) then synthesizes with emoji reactions and optional replies from the background models. It's free to use here with no login: http://pellmell.ai
You are right that the privacy wording was too broad. We are fixing it now so it states explicitly that Echo does not use customer prompts, files, chats, or outputs to train or fine-tune models. We are updating the matching Terms language at the same time.
Echo also starts users with free credit and does not require a credit card to try it. The current signup flow did not make that clear enough, so we are fixing that presentation too.
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.
Thanks for checking the individual rows. HumanEval+ is one small code slice, not the whole basis for the launch claim. The public evaluator currently contains 907 rows across seven benchmark families, and matched SWE-bench Verified and BigCodeBench runs are the next code evidence being added.
You also found a real UI bug: the inspector should show both stored answers and currently does not in some rows. We are fixing that.
On the row you reran: the page records a frozen matched run. It does not claim that Fable is incapable of solving that prompt on another run. We are adding repeated matched trials and making run count and variance explicit. Your rerun is exactly the kind of external check the row-level page is intended to make possible.
The broader result remains: Echo is competitive with Fable across the evaluated task mix at materially lower measured inference cost. We are filling in the harder agentic-code evidence now rather than asking anyone to infer it from HumanEval+.
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)
You might be interested in a project that I'm working on, which is kind of like OpenRouter Fusion, but instead of waiting for all models to synthesize, we stream the best model immediately and background the rest. The background models then reconcile with an emoji reaction and optional reply. It gets similar results to Fusion and is a lot faster! There's a free version that leverages open weight models here: http://pellmell.ai.
I like that I was able to test without an account and my prompt (What's the feasibility of a NetNavi IRL, along with a PErsonal Terminal? Create a document that outlines how to make this happen.) makes me think Pellmell is gonna be a research tool I'll continue to use.
OpenAI's router chooses between models of different sizes, which are still trained on roughly the same data. Its purpose is to reduce infra cost for OpenAI for simpler queries. No need to pay for GPT 5.6 Sol inference for "Hello" prompt.
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".
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.
In MoE systems the routing decision is made per-token, not per prompt or task. It’s one of ML’s many confusing naming conventions.
Even more confusingly, there are older pre-LLM MoE systems which ensemble and pool the predictions from multiple sub-components. For example in a random forest you could take the majority vote of the decision trees or the average of their numerical predictions.
After that, we developed neural net architectures for predicting a single thing like whether the user will click on your ad. An MMoE is in the same family.
And so now we are at massive MoE networks for LLMs which have similarities with MMoE in that the “decision” is about the very next token to predict.
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.
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.
They're obviously not claiming this is a new model that is fable-like at 1/3. It's a router that saves money by only using Fable when necessary. I don't think you should be calling someone's post a "scam" without doing a minimum of research. (I'm not associated with the company, but very interested in this.)
> 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.
Enterprise plans pay API costs, they're only subsidizing individual accounts and that's only because they have to compete with open-source models.
When they are successful at making those illegal/inaccessible, both Anthropic and ChatGPT are going to rip the band-aid off and inference will only be sold to those that can afford it.
> When they are successful at making those illegal/inaccessible
This would be like trying to outlaw Linux or peer-to-peer file sharing. It's technically possible to write and pass a law, but it's basically impossible to enforce it.
Because that is a short term solution, it won’t be offered forever. Large organisations have to purchase credits at $/tokens. Eventually everyone else will too.
I think you are looking at it incorrectly. No business is buying individual accounts, because if they do, they open themselves up to considerable risk.
The $200 plans are priced so that the power-users use them and then advocate about how great the product is. If you're buying a $200 plan, you're not doing it because of the price point but rather because of the amount of work it is doing for you.
The point still stands. The chinese labs don't have super discounted plans, so if the price per task benchmarks[1] are correct, and we apply the discount, you'll actually be paying more by using cheaper chinese models and this technique.
I think most people assume the subsidized plans will go away or get more limited eventually. They are basically a loss leader and a marketing cost that is very flexible and easy to change w/o directly impacting their primary customers.
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.
The entirety of "agents" and tool calls is a process of combining LLMs to get better results. Is it the same LLM in many cases? Yes. But it doesn't have to be.
It's the natural move that happened after people realized you couldn't throw away half a century of AI research.
Most of these focus on costs. But it is simply the case that the one-shot output did not scale for harder problems on workflows.
You definitely still would, but you need to pay the full input cost twice, so the equation really depends on how much first message vs repeat message matter.
So this is the dogpile.com of the askjeeves, alta vista, and lycos approach? Time is a flat circle?
Good ideas are usually still good across time and tools
Dogpile was only a good idea while Search Engines were mostly trash.
You needed to search all of them to find something decent.
That's roughly analogous to today. Ignoring cost, you'd be way better off asking all the LLMs to solve a problem (like coding) where you can verify the answer.
So the question is, for things like that -> can a group of models perform better than frontier models, especially at a reasonable cost?
Fable is not a great value, so unless you're trying to find answers to Erdos questions, you can probably do better on cost.
You can probably typically ask 3 or 4 of the top Chinese models for an answer and get a response for the same Fable question... Given that Fable isn't that much better, it's not surprising you can do better for a large subset of problems.
> Dogpile was only a good idea while Search Engines were mostly trash.
Precisely.
Exactly
High quality, fast & cheap (all 3 combined) - is a formula success.
It’s just way easier said than done.
There's a reason most pros will tell you pick two of the three.
Indeed. What a deep cut
Mixture of Models, perhaps? tbf to OP, the setup they're proposing has also been recently evangelized by other "ai gateway" products (like OpenRouter, JusCode, Fireworks etc), so there's likely something useful here.
Ensemble Methods:
https://en.wikipedia.org/wiki/Ensemble_learning
This sounds like networking.
Did OP invent an “intelligence router?”
timecube?
> 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.
All enterprises users (people using them for work and not side projects) can't get the subsidized plans. I would say subsidized plans are a minority of usage?
The subsidized userbase is large enough that cheema is right to call this out this distinction for other readers.
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."
The evaluator is public here: https://echo.tracerml.ai/eval/
It currently exposes 907 stored rows across seven benchmark families, with prompts, outputs, grades, and cost records. More benchmarks are coming soon.
Echo does not disclose its per-request routing decision because that policy is the product. We can, however, publish some of the eligible open-weight model pool, version dates, aggregate allocation mix, and evaluation settings without exposing the request-level recipe.
New video is also being made.
Isn't the relevant benchmark RouterBench? https://arxiv.org/html/2403.12031v2#S7
> No benchmarks, no info on which models are used, […]
The benchmarks are here → https://echo.tracerml.ai/eval/
They are not good benchmarks but at least they exist.
I've been working on a similar project and I found that it's easy to replicate Fable results if you use saturated benchmarks.
In my project, I wasted a huge amount of time trying to improve GPQA Diamond results above ~93% range. I realized my mistake when Fable dropped and made no improvement on this benchmark vs. Opus.
I wouldn't be surprised if ≈7% of GPQA Diamond questions simply have the wrong answer in the ground truth data, so that getting such a question correct is graded as an error. Most machine-learning benchmarks are rather badly validated.
Yep! I found this interesting article after banging my head against a wall for a long time: https://epoch.ai/gradient-updates/gpqa-diamond-whats-left
It's basically trying to replicate OpenRouter, which works pretty well and has a lot of nice features to abstract away any single provider, such as failover, metering, autoswitching, etc. It's actually a really smart infrastructure abstraction.
I just wish this were solving an actual problem rather than being a fairly transparent attempt to say something approximating, "Hey VCs, OpenRouter just became a unicorn but I can basically vibe code it"
Calling it "Fable-level" feels intellectually lazy / dishonest, but then again, what do you expect when there's so much money on the table.
It feels a bit more like Fugu to me, which acts as a multi-LLM orchestrator (though I think Fugu combines open- and closed-weight models), but without being able to see the “secret sauce” behind how any of them decides the number of "plies" each model in the swarm gets, they all feel rather difficult to compare beyond the big public benchmarks...
https://github.com/SakanaAI/fugu
OpenRouter's model router for coding isn't really sophisticated. This is very different.
Unrelated - but reminds me of my favorite quote by tenderlove:
"microservices turn function calls into distributed computing problems"
grug wonder why big brain take hardest problem, factoring system correctly, and introduce network call too
seem very confusing to grug
It's easy enough to copy and paste in a prompt, no?
Eval tests while giving general indicators might not be similar for each use case.
Seems similar to Openrouter Fusion - https://openrouter.ai/docs/guides/routing/routers/fusion-rou...
Fusion is a totally different approach, though similar in the sense that it leverages different models.
Fusion generates many replies then synthesizes. This adds a ton of latency and cost, so it's going to be better only for cases where you're willing to wait a lot and pay a lot more.
Routers (like this project) are a different thing, they can theoretically improve performance and cost at the same time without increasing latency much. I'm a bit skeptical though, since knowing which LLM is going to be better on a cost adjusted basis is hard (see https://artificialanalysis.ai/models/capabilities/coding?cos..., where the cost per task vs. performance is not what you expect, for example comparing Qwen 3.7 Max to GPT Sol.
A project I'm working on is aimed at improving performance without added latency but from a different angle. Instead of waiting for all replies for synthesis (like OpenRouter Fusion), it streams the "best" reply immediately (using a router to pick the best model) then synthesizes with emoji reactions and optional replies from the background models. It's free to use here with no login: http://pellmell.ai
There's so many people reimplementing Sakana fugu from its two ICLR papers but no open source version of it.
No single signin. Privacy policy allows training. No try it first without credit card. It's a good idea, but this looks premature.
You are right that the privacy wording was too broad. We are fixing it now so it states explicitly that Echo does not use customer prompts, files, chats, or outputs to train or fine-tune models. We are updating the matching Terms language at the same time.
Echo also starts users with free credit and does not require a credit card to try it. The current signup flow did not make that clear enough, so we are fixing that presentation too.
Thanks for calling both out.
If you want to try a similar idea that you can chat with immediately (free tier uses open weight models), I'm working on this: http://pellmell.ai
I assume trying without a credit card will bankrupt him immediately.
Replace "Show HN:" with "Advertisement:" ?
"Backed by YCombinator"
https://www.ycombinator.com/companies?query=tracerml
I don't see it?
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.
I can get it to write win32 unsafe rust code.
I can't get it to review win32 unsafe rust code.
Make it make sense.
Thanks for checking the individual rows. HumanEval+ is one small code slice, not the whole basis for the launch claim. The public evaluator currently contains 907 rows across seven benchmark families, and matched SWE-bench Verified and BigCodeBench runs are the next code evidence being added.
You also found a real UI bug: the inspector should show both stored answers and currently does not in some rows. We are fixing that.
On the row you reran: the page records a frozen matched run. It does not claim that Fable is incapable of solving that prompt on another run. We are adding repeated matched trials and making run count and variance explicit. Your rerun is exactly the kind of external check the row-level page is intended to make possible.
The broader result remains: Echo is competitive with Fable across the evaluated task mix at materially lower measured inference cost. We are filling in the harder agentic-code evidence now rather than asking anyone to infer it from HumanEval+.
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)
You might be interested in a project that I'm working on, which is kind of like OpenRouter Fusion, but instead of waiting for all models to synthesize, we stream the best model immediately and background the rest. The background models then reconcile with an emoji reaction and optional reply. It gets similar results to Fusion and is a lot faster! There's a free version that leverages open weight models here: http://pellmell.ai.
I like that I was able to test without an account and my prompt (What's the feasibility of a NetNavi IRL, along with a PErsonal Terminal? Create a document that outlines how to make this happen.) makes me think Pellmell is gonna be a research tool I'll continue to use.
This is really cool, super fun!
OpenAI's router chooses between models of different sizes, which are still trained on roughly the same data. Its purpose is to reduce infra cost for OpenAI for simpler queries. No need to pay for GPT 5.6 Sol inference for "Hello" prompt.
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".
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.
In MoE systems the routing decision is made per-token, not per prompt or task. It’s one of ML’s many confusing naming conventions.
Even more confusingly, there are older pre-LLM MoE systems which ensemble and pool the predictions from multiple sub-components. For example in a random forest you could take the majority vote of the decision trees or the average of their numerical predictions.
After that, we developed neural net architectures for predicting a single thing like whether the user will click on your ad. An MMoE is in the same family.
And so now we are at massive MoE networks for LLMs which have similarities with MMoE in that the “decision” is about the very next token to predict.
"In MoE systems the routing decision is made per-token, not per prompt or task."
Have there been experiments with doing it per task? Like, "oh this is python project, use this model" "oh this is about writing fantasy, use this"?
It’s a good idea. The results probably depend a lot on how close your task is to the benchmarks though.
I think OpenAI already has (had?) a feature like this called “auto” mode for thinking.
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.
Sometimes expensive models are cheaper on easier tasks because they use fewer tokens, too.
Yeah apparently Opus and Sonnet are like that.
Yep! There are a lot of models like this: https://artificialanalysis.ai/models/capabilities/coding?cos...
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.
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.
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.
They're obviously not claiming this is a new model that is fable-like at 1/3. It's a router that saves money by only using Fable when necessary. I don't think you should be calling someone's post a "scam" without doing a minimum of research. (I'm not associated with the company, but very interested in this.)
enjoying how people are re-discovering ensemble methods.
> 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.
Enterprise plans pay API costs, they're only subsidizing individual accounts and that's only because they have to compete with open-source models.
When they are successful at making those illegal/inaccessible, both Anthropic and ChatGPT are going to rip the band-aid off and inference will only be sold to those that can afford it.
The good ole American way.
> When they are successful at making those illegal/inaccessible
This would be like trying to outlaw Linux or peer-to-peer file sharing. It's technically possible to write and pass a law, but it's basically impossible to enforce it.
Enough to make it a non-started at the organizations that pay their bills. Everyone else isn't big enough to matter.
Going to be an interesting world where big enterprises have to spend 100X the cost for the same value of AI as startups and small businesses.
Because that is a short term solution, it won’t be offered forever. Large organisations have to purchase credits at $/tokens. Eventually everyone else will too.
This is what OpenAI and Anthropic are trying to make everyone believe. Most accountants will flinch at this (they already are).
The $200 odd plans are already out of reach of many, many people.
The attrition of customers if they were to get rid of these subscriptions plans would be untenable.
I think you are looking at it incorrectly. No business is buying individual accounts, because if they do, they open themselves up to considerable risk.
The $200 plans are priced so that the power-users use them and then advocate about how great the product is. If you're buying a $200 plan, you're not doing it because of the price point but rather because of the amount of work it is doing for you.
Lots of businesses are buying and using these plans. Basically every small business I interact with.
You may want to consider the incomes of developers outside the US, students, unemployed. $200/month is a lot to a lot of people.
The point still stands. The chinese labs don't have super discounted plans, so if the price per task benchmarks[1] are correct, and we apply the discount, you'll actually be paying more by using cheaper chinese models and this technique.
https://artificialanalysis.ai/agents/coding-agents#artificia...
They can still write code.
I think most people assume the subsidized plans will go away or get more limited eventually. They are basically a loss leader and a marketing cost that is very flexible and easy to change w/o directly impacting their primary customers.
doesnt exist for enterprise plans
M-o-MoE
That’s basically the same idea IBM advertises with Bob?
This looks different, it's not a main agent delegating to subagents, it's a router.
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.
The entirety of "agents" and tool calls is a process of combining LLMs to get better results. Is it the same LLM in many cases? Yes. But it doesn't have to be.
It's the natural move that happened after people realized you couldn't throw away half a century of AI research.
Most of these focus on costs. But it is simply the case that the one-shot output did not scale for harder problems on workflows.
how does this differs from OpenRouter fusion?
how does this differs from Sakana Fugu?
but u wouldnt get caching savings
You definitely still would, but you need to pay the full input cost twice, so the equation really depends on how much first message vs repeat message matter.
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.
Is this yet another Sakana Fugu / OpenRouter Fusion?
Looks like it but with open-weights only.