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Everyone is building LLM routers, we deprecated ours
- maxrev17 2mo agoYeah routers suck, been doing this myself for the best part of a year now and it’s really difficult to make it behave. A good model for your task is the best bet.
- velcrovan 2mo agoIronically, my confidence that a human had at least an active part in writing/editing this article went up because of this train wreck of a sentence: > "A cache-aware model router will take that into account by adding stickiness to the initially chosen model and keeps querying it."
- hhh 2mo agowhat’s wrong with the sentence? reads fine to me
- 1bpp 2mo ago'keeps' is the wrong tense and could be 'will keep'
- thehamkercat 2mo agoi think it should be "by adding stickiness to the initially chosen provider of that model"
- inigyou 2mo agoWeird tense combination. I'm not sure if it's officially wrong but it feels weird to change tense mid-sentence after so many words. "X will do ABCDEFGHIJKLMNOPQRSTUVWXY and does Z." I thought it could be a typo for "X will do ... and do Z" but only when I went to write why it's weird, I realised it doesn't have to be a typo. It doesn't even matter here. It would mean the same thing either way.
- brunaxLorax 2mo agoa French human doing its best to sound punchy in English :)
- scotttaylor 2mo ago[flagged]
- owenthejumper 2mo agoRouting belongs on the client side
- hrpnk 2mo agoEven the simplest router that differentiates models between plan & execute will ensure that this is consistently followed. Folks have too high FOMO to choose themselves.
- mmargenot 2mo agoI find this routing problem to be opaque and I’m generally skeptical that the label people are trying to predict is meaningful. If you really need more discrimination of the complexity of an input to get an efficient response, sft or rl tuning something for your harness would be more effective.
- coffinbirth 2mo agoSome routing services not just route to different LLMs, they also handle all the legal issues (GDPR compliance, ISO certification, guaranteed Zero-Data-Retention, domestic data processing/European based clouds, etc.). In regulated industries, these things matter a lot, especially when processing of sensitive data is involved.
- inigyou 2mo agoBy "guaranteed" they mean "we promise really hard" right? Data retention has so much profit potential that any provider would have to be completely stupid not to retain data, even after promising they didn't.
- brunaxLorax 2mo agoIf your router has GDPR/ZDR/EU compliant, it doesn't make your provider compliant. You're just adding a middleman.
- rush86999 2mo agoI'm still working on mine: https://github.com/rush86999/atom/blob/main/docs/architecture/LEARNING_LLM_ROUTER.md https://github.com/rush86999/atom/blob/main/docs/architectur...
- brunaxLorax 2mo agoCheers, good luck!
- overgard 2mo ago> Just as a painter knows exactly what brush they need to use, and the craftsman carefully chooses their tools, engineers should understand trade-offs and subtleties of the different models. I'm really skeptical of this idea. Pragmatically: who has time to understand the nuances of these models when there's like a new one every week? Also without any view into the training, figuring out what each model is potentially good at is more or less just throwing spaghetti against the wall, except the spaghetti is potentially very expensive and might insert subtle issues into your code base.
- zackify 2mo agoI just use whatever's cheapest for personal things. Dsv4 flash and got 5.6 Luna are great for the price and I'll use the openrouter benchmark view to compare the overall score.
- bobmcnamara 2mo agoFound the drywaller
- cyanregiment 2mo agoMy landlord just muds over cob webs and open space. They're over 100 years old so they legally count as part of the building's foundation. So is he, and so does he. He looks like the Tales From The Crypt guy I have no idea how he's still doing manual labor.
- mountainriver 2mo agoYou do still need to know though, different prompts have different affects on them.
- inigyou 2mo agoI'm not surprised that people who use LLMs all day for everything get a feel for the different ones.
- danielmarkbruce 2mo ago
- robertclaus 2mo agoI think fail-over for a production critical service is an equally important responsibility of the router.
- ramraj07 2mo agoYou cant fail over to a different model though. Even failing over to a different provider isn't always as simple as set it and forget it. For example bedrock doesn't support the tool search tool feature from anthropic.
- jml78 2mo agoThey beta support for it now I believe
- brunaxLorax 2mo agoI didn't know that! I thought that all models had the same features no matter the provider
- brunaxLorax 2mo agoYes, providers have downtime and retry/fallback solves this problem.
- youre-wrong3 2mo ago[dead]
- randomblock1 2mo ago> Complexity cannot be deduced from the prompt alone. Let’s take an example: “evaluate the tests for the repo $GIT_REPO and improve them” can be a very simple task if you mention a personal website written in plain HTML5; or an incredibly complex task if you target the Linux kernel repo. I don't think this a good example. The first step would be reading the documentation, reading an overview of the tests, then executing commands to run the tests. A cheap model could do that. After that, though, the router will have to figure out how complex the tests are, which is the hard part, but I can't imagine it's that hard to determine a bunch of C code is tougher than some HTML from looking at it. Unless they want to select one model at start and never change it, because that's pretty clearly not the right way to go about routing. Regardless I agree that routers usually aren't worthwhile, at least in the form of something that's meant to be universal. It's probably more efficient to just change something in the repo code, whether that's skills or instructions or something else. Benefit of that being it's persistent, portable, and more well tuned than guessing complexity on every turn.
- brunaxLorax 2mo agoSounds like an orchestrator for me ratter than a router
- dweez 2mo agoI spent a lot of time researching LLM routing last year and also came to the conclusion that it's generally not worth the effort. It's too hard to understand the difficulty of a query a priori. One specific challenge I was seeing is that difficulty depends a lot on what information is retrievable by the agent. Consider the question "what is the 5-state busy beaver number?" (https://en.wikipedia.org/wiki/Busy_beaver https://en.wikipedia.org/wiki/Busy_beaver). In 2023 this would be a Mythos-tier research problem, but a solution was proved in 2024 so today any minimally intelligent model with a web search tool can just fetch the answer. You don't know which queries will be basic summarization and which will be deep reasoning until you get going.
- dofm 2mo agoYou also have problems with short prompts whose results depend highly on the understanding of nuance. The router is going to have to mostly solve the prompt to decide which model to send it to. What you actually want is a model that can conclude either "I know the answer to this with confidence" and answer, or "I think I don't know the answer to this, I should ask another model and I know which one". But I don't think LLMs can really bring their uncertainty to the surface in that way yet? Their internal confidence can be measured and returned, so you could probably front a more powerful model with a knowledgeable assistant, but they can't consciously mark their own homework?
- zbentley 2mo ago> Their internal confidence can be measured and returned It can? I was under the impression that confidence was either self-reported by the LLM or assessed by having another model interpret the output response. If there's a confidence score at the level of the actual model math, that's news to me.
- jdiff 2mo agoAt least one model can today. https://github.com/cactus-compute/cactus-hybrid https://github.com/cactus-compute/cactus-hybrid
- seizethecheese 2mo agoTheir router classified prompts into difficulty buckets. This obviously won't work. Consider a senior developer routing work based only on the task description. Clearly you need to dig a bit deeper into the task. Saying routers don't work is sort of like saying serverless doesn't work. It depends on when and how! One routing implementation that recently launched here is interesting (https://news.ycombinator.com/item?id=49099143 https://news.ycombinator.com/item?id=49099143). It routes based on the models' initial trajectories. This is like having multiple developers get started, seeing what they're doing, then pulling all but one off the project. It should work, but doesn't seem ideal! I've gotten routing working well for typical chatbot prompts in http://pellmell.ai http://pellmell.ai. This is fine because prompts are easy to classify into category buckets (for example: legal, medical, general knowledge, code). And models definitely have strengths and weaknesses. You want Gemini to answer General Knowledge and you want Claude to answer coding.
- brunaxLorax 2mo agoI didn't know about Tokenless, the approach seems really innovative, if it works it fixes the "Complexity cannot be deduced from the prompt alone" problem. However you still have the other hidden costs: cache, breaking behavior consistency, and unpredictability. Your point is interesting, you say that task specificity is easier to classify than task complexity, which I agree - I didn't mention it but we had task specificity routing too :). My opinion is that in many cases task specificity calls are easy to distinguish at build time, and therefore you can isolate them and attach the right model/settings beforehand, so you have less need for a smart routing on the fly.
- bluejay2387 2mo agoI think this should probably be scoped to 'generic router systems that don't understand query context' are not useful. We have had lots of good results with routers that understand the context of the types of workloads they process and can route requests to the most efficient models.
- try-working 2mo agoI recently wrote about first principles of model routing that I've learnt building a model router. The model pool should be kept small, and models in the pool should be clearly differentiated. For example, one large frontier model for quality, one small, fast and cheap model like DeepSeek V4 Flash for routing work. These two principles by themselves solve the issues with caching, with routing decision making. I routinely hit >99% cache while routing between GPT 5.4 and DeepSeek. https://try.works/first-principles-of-model-routing https://try.works/first-principles-of-model-routing
- htrp 2mo ago>Our router was classifying each request into one of four different tiers of complexity: simple, standard, complex and reasoning. Seems like a naive classification model lacking context?
- brunaxLorax 2mo agoCould be. In that case many routers are concerned as they all have more or less the same category buckets.
- 0xDEAFBEAD 2mo agoFrom a big-O notation perspective, if you're serving N queries, working the kinks out of your routing system costs perhaps O(logN) in developer time, whereas LLM provider savings grow as O(N). In other words, the more queries you're serving, the more worthwhile it looks to figure out a viable method of model routing.
- jeremyjh 2mo agoI agree with one distinction - coding agent workflows can use defined subagent roles that are pinned to specific models and I have found this very effective. The orchestrator is building all the context to make these assignments - it’s not a dumb router. Using Minimax M3 for exploration and librarian tasks for example is fast and cheap - my $10 plan lasts all month and saves a lot of tokens for my main coding plan.
- brunaxLorax 2mo agoYes. orchestrator > smart router
- scionaura 2mo agoInsider take: routing will not be a (successful, durable) thing, at least not externally to model providers. The labs are incentivized to solve this problem themselves, since they’re competing on a 2D cost-intelligence frontier. If they can reduce cost without harming intelligence they will do that and pass on (some of) the cost reduction to the user. There are nicer solutions available to them because they can cut into lower levels of abstraction. E.g. you should consider speculative decoding to be one (very conservative) form of routing and note that you can’t implement that for the labs from the outside.
- CuriouslyC 2mo agoThis arbitrage isn't durable either though, as it's just a race to the bottom with open research, and the long term destination of everything is custom models for different tasks, since it's going to become increasingly apparent that some areas of knowledge have anti-synergy as we push up the diminishing return curve.
- brunaxLorax 2mo ago100% agree. MoE is the perfect example: reducing FLOPs while keeping intelligence
- fibuladev 2mo ago[dead]
- maherbeg 2mo agoNow that even the smaller models from labs (Luna, deepseek v4 flash) are getting powerful, I think the orchestrator pattern of a smart model coordinating smaller models for work will end up being the way to go.
- brunaxLorax 2mo agoAgree. The orchestrator pattern (big model managing smaller ones) works already well
- atlex2 2mo agoWhat was the architecture of your router? If it was based on GRPO/RL, it would be interesting to hear why your router performance capped. I think the truth is that it's not an efficient cost cutting method. Your router has to be at least as 'smart' as all the but the smartest of your models (models do poorly when asked 'is this a task you're well suited to'), and that means you're caching multiple prompt histories including kv-filling/prefix caching on your expensive router model. Most of the time, not super great for savings.
- brunaxLorax 2mo agoWe tried different things: heuristic, TF-IDF and LLMs. I voluntarily didn't talk about the tech because it doesn't fix the problems mentioned by switching models on the fly.
- atlex2 2mo agoWould love to hear your thoughts on NVIDIA NeMo Switchyard...
- sudb 2mo agoTo add: I doubt frontier labs will build routers - they are not financially incentivized to optimize token usage (though in the short term they may be incentivized by constrained GPU capacity to reduce load).
- brunaxLorax 2mo agoAll inference providers (labs and neoclouds like TogetherAI or Fireworks) are incentivized to be efficient to be more competitive. For example MoE reduces compute without reducing output quality. I would not be surprised if they end up implementing some kind of internal routing at some point.
- ljlolel 2mo agonobody else is putting a router inside a Trusted Execution Environment https://trustedrouter.com/ https://trustedrouter.com/
- blackcat201 2mo agoI think the author need to rethink out of the box what's LLM routers in a traditional sense ( input in, route, output ) and move to think how a router would work in agentic workflow. See cognition Devin Fusion design.
- deleted 2mo ago[deleted]
- firasd 2mo agoThe 'personality' of the model matters a lot even for stuff like Code Gen. If you're building something with Fable and then get routed to Opus cause the router decided your task is not 'demanding enough' it's like: great, now I have to read complex prose like "That's the thing, and the thing is the point:..." Opus-isms when I just wanted to make a shopping cart
- brunaxLorax 2mo agoAgree
- KoleSeise1277 2mo agoClassifying complexity from the prompt alone was always the weak point. Most of what makes a task hard shows up after the first tool call.
- jing09928 2mo ago[flagged]
- aegisora_ai 2mo ago[flagged]
- luciana1u 2mo ago[flagged]
- joshowens 2mo ago[flagged]
- kaycey2022 2mo agoIt seems apparent to me that task complexity can’t be determined by prompt alone. How? A prompt is just a simple rambling. An agent will go through many many tool calls and steering just to arrive at the right approach. A serious router therefore needs to build up a dataset of how different models responded, end to end, to different tasks on different contexts. I won’t comment on whether the current frontier models can reliably judge these outputs, but I am sceptical about that. And moreover look at the state of evils! They are gamed to hell and keep losing credibility. A more difficult problem for router builders is that they are working on an opaque system behind an external API. How can you reliably guarantee model behaviour when model behaviour has been shown to deteriorate under arbitrary conditions that have nothing to do with the task given? So much investment only to be an AI company that can get rug pulled by the real AI companies at any given time. I would think the only people who can come up with good routers for a collection of models are the inference providers themselves. Because theoretically they have full control of how their models are served. And inference is not zero cost or cheap for them either. And going by OpenAI’s experience routing is not an easy problem for them to solve either. And they don’t have the incentive to route you to cheaper models and reduce costs for customers at the same time. Routing objective for them is to increase their own profits.
- rrvsh 2mo agoDidn't this slightly contradict itself? The conclusion I drew based on the problem statement was that the best way was to have a model router handle the root prompt and stick to a model for the rest of the session - engineers still have the discretion to handoff to a smaller or larger model when needs arise.