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elgertam 16 minutes ago [-]
I ran into a problem at work recently: we are given access to a bunch of models up to a full Claude Opus 4.8, but a monthly budget of 100k tokens. We are also given access to Gemini 3.5 Flash & 3.1 Pro with a daily budget of 50M tokens, but no tool calling. I'd love to hook Claude Code (or Pi) into the Gemini model, but the lack of tool-calling makes it quite difficult. I've been planning out how an intelligent router might be able to use a token-efficient tool-calling model (including a small local open-weights model) to handle the basic tools like reading from the file system or interfacing with MCP servers such that context is gathered, but then send the built up context to the Gemini model where I have a nearly unlimited (for my use cases) token budget.
Could your router handle this?
thombles 9 minutes ago [-]
I’m curious how a workplace ends up with a model policy like this. It seems like you’d spend more time trying to work out how to use a tiny number of Opus tokens than doing it yourself.
Jaxkr 5 minutes ago [-]
Monthly budget of 100k Opus tokens? So $2.50 worth?
nikcub 10 hours ago [-]
I'm glad there are more attempts at solving model routing, as costs (at API rates) has really become an issue. Some feedback:
1. Reiterate the cache issue from other comments already here. there is a lot of optimisation in harnesses around caching and a proxy model blows that up
2. Coding agents are model aware - they already route code discovery to mini / flash models, planning to heavy models, workflow design to ultra, implementation to mid / high etc. They know when they're exploring, planning, implementing, reviewing etc. and which model class to select and when it fails.
With a proxy you're breaking this control loop and feedback. It doesn't know, for ex. that it just attempted with deepseek v4 and it failed, lets try Opus?
3. How are you going to RL improvements and prevent the router becoming stale? You only have access to your own internal prompts and ~thousands of samples.
This is RL'd on one orgs codebase. There are going to be a lot of prompts you haven't seen before and have no insight to on how to route correctly, and you have no insight into users HF to improve your own model. Orgs aren't going to share their traces with you, so you need other sources to train on and improve
There are also new model releases every week that you need to keep up with - whats the story going to be here
4. Publish evals by running terminalbench / deepswe bench. Show us the performance / cost / time chart vs the other agent and model sets. If you can show gains there, you have a very simple value prop to sell where you can charge for a % of the saved costs
adchurch 10 hours ago [-]
Really appreciate the thoughtful feedback!
1. Agree it's important, fwiw the proxy model doesn't blow this up though - only incurs a 1 time cost when switching models and we're aware of that when making routing decisions
2. The agents are model aware yes but they are not incentivized to optimize too heavily here (in particular they don't use OS models even when they would be better). I think that's where this router comes in and brings genuine improvement.
3. Two parts here: 1 is continuing to grow our golden dataset over time, 2 is using reward signals from production traffic (on a per-customer basis or, if allowed, across all users)
4. Yes we have these internally, great callout that we should publish! Will do + will link from the repo soon. (Fwiw I think these benchmarks are useful but don't fully capture vibes - you should try it out yourself for that!)
stpedgwdgfhgdd 13 hours ago [-]
The thing I do not get with these routers is that you will have more cache misses (5min ttl). And if there is one thing i’ve learned; using the cache is crucial.
How does this router translate to $$$ when developing?
adchurch 12 hours ago [-]
You're right and that's why we built the router to be cache aware! Once it starts using one model, the threshold to switch to another model will be higher because the additional cost of the cache miss needs to be worth the cost savings or quality increase.
This is the key thing that other routers we've seen miss: they're stateless so for a coding agent use case you end up spending more money due to all the cache misses.
alansaber 12 hours ago [-]
That is interesting, sounds like in practice you only end up routing between 2 models
adchurch 12 hours ago [-]
I'd say that a typical main agent loop has 1-3 models (obviously very situationally dependent), but when you have subagents those can get routed independently since they have a fresh context window, so there are a lot more degrees of freedom there.
echelon 11 hours ago [-]
Or not routing at all.
In practice you just pick one and stick with it until the API stops or you hit performance issues.
adchurch 11 hours ago [-]
The choice on the first turn is super important for this reason! But if a user prompt sends the convo in a very different direction then often it does make sense to reroute at that point.
mthoms 11 hours ago [-]
This is a key point. I don't know if you can still edit your submission, but I think this would be helpful to mention up front. I'm looking forward to testing this.
12 hours ago [-]
jakozaur 11 hours ago [-]
It's rather hard to do at the proxy level with agentic coding, such as Claude Code or similar. These are long-chained sessions of tool use that heavily rely on prompt caching. Changing mid-flight is costly.
It looks like much more context is required to decide on the best model (e.g., summarizing logs might use a cheap model, whereas you likely want Opus/Mythos/GPT 5.6 to debug multithreading logic). In an agentic system, a decision about the model may be embedded in the decision to orchestrate the model.
But intuitively I think it makes sense that a model can learn what model to route things to if it has all the relevant info, and experimentally it works pretty well in our experience
g00k 12 hours ago [-]
Man, I'm not so sure if I'd use something like this because the way I prompt already changes based upon what model I am using. I'm not convinced it would route to the right model based on my diction or whatever.
adchurch 12 hours ago [-]
Yeah that's a really interesting point, tbh I think the more relevant variable here is the harness you're using rather than the specific model? i.e. GPT 5.5 in the Claude harness behaves a lot more like Claude than Codex if that makes sense.
Hard to quantify this ofc but that's what I've felt vibes wise from using this for the last month.
devmor 11 hours ago [-]
I have the same general feeling as well. Like you, I can’t prove it’s not just personal feeling - but e.x. Opus via Copilot CLI behaves entirely different than Opus via Claude Code, which behaves differently than Opus via OpenCode or Pi.
ValentineC 6 hours ago [-]
I have the same feeling. I've been trying Claude Code directly ever since Copilot nerfed their request-based system, and Opus just seems to perform "better" in Claude Code.
It's also possible that it's the 1m context versus the 200k context (Copilot's limit) doing some of the work here.
stronglikedan 10 hours ago [-]
> Man, I'm not so sure if I'd use something like this because the way I prompt already changes based upon what model I am using.
Perhaps you're just not the best use case. It may work better when Average Joe is the one prompting.
alansaber 12 hours ago [-]
Yep this was always the reason to avoid "auto" mode in cursor.
GodelNumbering 11 hours ago [-]
This would not work in the way that shows any significant genuine benefit IMO. Caching and optimum routing of a single request are at odds with each other. Higher the distinct model count in a conversation, more cache misses you accept.
Based on what OP said elsewhere in the discussion "threshold to switch to another model will be higher" means that essentially you reduce the workflow into two models at most. The two model primitive, one planner and one executor, is already sufficient for such a use case.
For lower than 2 models, it's just a simple single model cache-preserving conversation which arguably doesn't need another layer. For larger than 2 models, you are likely paying a large aggregate cache penalty that negates most of the gains
adchurch 11 hours ago [-]
When we started building this we did it as an experiment and we thought the same thing might be true (cache misses would make the whole thing pointless). This turned out not to be true! I think there are 3 reasons intuitively:
1. Small models can carry out a good number of requests e2e
2. Small model for part of a request + cache miss < big model for entire request in many cases
3. Subagents
For our own usage we've saved 40% so far (that is of course including costs of uncached requests when switching models)
GodelNumbering 11 hours ago [-]
This assumes a perfect problem routing though. Determining the complexity class of an arbitrary problem is generally undecidable or extremely hard (Rice's theorem implication). So, in real use cases, you need to amortize all cases where the problem got routed to the wrong model and recovery had to be performed)
For example, if my task was "refactor this component to decouple all messy nesting", the problem router can't possibly know what is being referred to. This works for clear cut and dry problems but not for ambiguous problems. Most of the real world problems carry a lot of ambiguity.
adchurch 8 hours ago [-]
I think the key detail here is that we use embeddings of the prompt + previous context in order to decide where to route the request, and if one model is getting stuck we can course correct and move to a different model.
So: we can reasonably cluster similar problems together and learn how models handle them, and the entire system doesn't fail if the initial decision is off.
gopher_space 10 hours ago [-]
In my mind one of the problems is that I'm using the term 'router' to describe something more akin to a train schedule. A list of abilities, cost, and timeframe to be used by a model capable of deconstructing its own process.
peterbell_nyc 12 hours ago [-]
I auto tune my prompts to a locked model version based on production data used as evals with holdback data. I think the use case for this would be one off interactive prompts? For now I just run those all against an Opus 4.8 MAX and I'm sure I could downtune, although for interactive my opening prompt isn't always reflective of my overall goals for the multi turn session.
I'm just trying to figure out why on the fly routing would beat testing and tuning and locking models and versions for each class of call, with evals and auto tunes running to explore more possible models for commonly run classes of prompt over time . . .
gopher_space 9 hours ago [-]
"Based on your subscription tier and local hardware here's a list of models that fit and process definitions your biggest brain will comfortably handle."
I guess that sounds a lot like moving your evals and auto tunes to a third-party, but I don't have the time, budget, or inclination to create a system like this out of whole cloth and then keep it relevant.
I could see something that provides on-the-fly routing information being useful, but actual decision-making is too dependent on context.
adchurch 11 hours ago [-]
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matt_d 9 hours ago [-]
Looks interesting!
Out of curiosity, how does it compare with vLLM Semantic Router?
- vllm-sr/auto: efficient, fast, balanced routing, similar in spirit to Fugu // Sakana Fugu — Multi-Agent System as a Model: https://sakana.ai/fugu/
- vllm-sr/fusion: panel-style multi-model reasoning and synthesis.
- vllm-sr/flow: router-native workflow orchestration
- vllm-sr/remom: multi-round reasoning over one or multiple models.
I tried Sakana Fugu, boy is it hungry ... it blows up tokens like nothing I have ever seen. Not that impressed with the results I got from it however if I'm being honest. Now I'm bought into their buy 1 get 2nd month free so will keep trying it but may cancel after.
adchurch 8 hours ago [-]
Good questions. From what I can tell, vLLM semantic router is more optimized for one-off prompt/response workflows rather than agentic coding (I don't think it's cache aware).
As another commenter (https://news.ycombinator.com/item?id=48689994) pointed out, for one-off requests, I think it makes more sense to lock to one model whose behavior you understand very well. For dynamic requests like the ones going to a coding agent I think dynamic routing makes more sense but it does need to be cache aware.
matt_d 6 hours ago [-]
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nativeit 8 hours ago [-]
This would have been neat back when I could afford enough tokens to even set it up properly. Now I’ve had to increase my GH Copilot subscription just to cover the bare minimum updates to a few websites every month, and I no longer do any test driving, or even recreational coding projects. I don’t have hundreds of dollars a month to plow into these products, so I’m rationing use, looking for better local options, and being much more discerning about where these tools actually save time. Precarious time to be alive…
ValentineC 6 hours ago [-]
> Now I’ve had to increase my GH Copilot subscription
Maybe you should move away from a subscription that started charging by the token instead of by the request?
dools 2 hours ago [-]
I've been building a reasonably complicated project over the past week using deepseek v4 pro almost exclusively (a couple of k2.7 and 1 session with gpt5.5 to re-assess some architectural questions). Deepseek is super capable though if you're a coder. I don't even write "code" but I can tell when it's doing something dumb and tell it how to do it better, but other than that I'm not micro managing it or using it "just for auto complete" or whatever.
And it is SO fucking cheap.
newaccountman2 8 hours ago [-]
try OpenCode
jawon 4 hours ago [-]
I got Opus to knock out an MCP server that implements subagents running in pi and tell Opus to send work to DeepSeek. Or I tell it to ask GPT-5.5 for critiques. It's manual but saves a lot of tokens.
lubujackson 10 hours ago [-]
I notice Cursor already does something similar. Even if I have Opus 4.8 selected, it will trigger subagents using Composer 2.5. I like using Auto personally because it is pretty effective and deeply discounted, but at work I YOLO Opus high.
I imagine a solution like this will eventually be an enterprise-forced solution because there is no reason right now for individual developers to be selective about model pricing. Even more important is non-tech users who do stuff through MCPs like "give me a full overview of all analytics" and let it chug for half an hour.
adchurch 8 hours ago [-]
Oh interesting, didn't know Cursor did that! Totally makes sense though, routing subagents is def the easiest win, no need to have any cache awareness.
spqw 12 hours ago [-]
This + making sure common requests are saved as reusable skills and scripts would probably save a large part of my token usage
As prices increase we will see more of these tools to optimise and make the best use of token budget
adchurch 12 hours ago [-]
100%, from what we've seen, for a lot of big companies that 1. don't have subsidized usage and 2. are pushing AI adoption hard, figuring out token costs is P0 or P1 for their eng leadership
SoftTalker 11 hours ago [-]
So you're saying that since adopting AI/LLM tech many companies have their top engineering priority being optimizing the costs of that rather than ... addressing actual business needs?
Is this noticeably different than having your implementation planning phase break a larger task into sub-tasks, and recording the ideal model to use based on scope as part of the task definition?
adchurch 3 hours ago [-]
Yes because it's a model explicitly trained to make model selections! Opus probably doesn't have a great idea of when to send a task to DeepSeek vs. to Sonnet, for example.
jmalicki 10 hours ago [-]
> with no noticeable differences in quality or velocity.
Have you done any A/B tests on this with evidence? (That's one thing I'd be very interested to see for claims like this - I'm not necessarily doubting you, it just seems like it could be useful to understand claims of quality/efficiency)
adchurch 8 hours ago [-]
Great question! Our main product quantifies engineering productivity & quality so I think we're uniquely qualified to answer this - our velocity has only gone up and our quality (bugs introduced, code turnover) has not budged per our own analysis.
jmalicki 5 hours ago [-]
> our velocity has only gone up
That is super curious - using more low quality cheaper models increased your velocity? My prior would have been slightly reduced velocity but massive reduction in token costs made it worthwhile.
Is that due to the faster inference time?
notatoad 9 hours ago [-]
Is this talking to claude code, or to claude api (and paying api rates)? programatically routing requests through claude code sounds like a good way to get banned, just like the opencode and openclaw users.
adchurch 8 hours ago [-]
If you have a Claude sub with subsidized usage we use that. If not you pay API prices.
ValentineC 6 hours ago [-]
Is that because you start by running it inside Claude Code? I don't see how Claude would allow any other harness to call them for their subscription, after all that OpenClaw hullabaloo.
pradeep1177 10 hours ago [-]
So, how are you handling read/write caching? I mean, if I keep routing the next prompt based on the task weights? How about if I'm sending every 5th query to opus, which do expensive write cache?
adchurch 8 hours ago [-]
We consider the cost of missing the cache when making each routing decision after the initial one. Discussed in a bit more depth here: https://news.ycombinator.com/item?id=48689448
asdev 10 hours ago [-]
Large model companies will likely build this and make it better. It'll also be cheaper overall since they'll be subsidizing token cost if you use them directly vs third party router paying API costs
adchurch 10 hours ago [-]
I would argue they do not have a good incentive to build this and make it better. Why would Anthropic route Claude Code traffic to DeepSeek (at 20% of the cost)?
asdev 7 hours ago [-]
They'll route traffic to Haiku or one of their cheaper models, not third parties. Overall cost will end up being cheaper than whatever you are doing
thandv 8 hours ago [-]
This might be a stupid question, but can a extra added local llm help with the caching problem?
adchurch 8 hours ago [-]
We haven't experimented with routing to local LLMs much. Technically they benefit from the cache too although it's more a question of latency than cost. But tbh I haven't seen great results in the wild from working with local LLMs for coding - curious if you've had any success with them?
k9294 12 hours ago [-]
What about request caching? If you swap to a cheaper model mid execution it might cost more that to make multiple requests to the already cached provider?
Can't really win can ya? Scarce? They're driving up prices! Plentiful? It's all a big bubble!
alansaber 12 hours ago [-]
"We reward the routing model when it selects an LLM that achieves the task successfully" sounds pretty oversimplified
adchurch 12 hours ago [-]
Indeed it is :) I skipped over talking about all the RL machinery, network design, reward function design, state representations, etc. because really the intuition is that we tell the model when it accomplishes its goal, and then it learns over time how to get better at making the right decisions in order to accomplish its goal.
Happy to talk about this in some more depth if there's anything specific you're curious about!
reliablereason 11 hours ago [-]
Wont this kill the kv cache?
Also i am pretty sure neither open ai or anthropic leets you seed the agents own tokens.
Will this use my Claude Pro/Max subscription? Or will it always use the API billing "pay as you go"?
adchurch 12 hours ago [-]
Yep it uses the Claude sub if possible and falls back to API billing only if you don't have a Claude sub or it's out of usage! Same deal for Codex
suyash 12 hours ago [-]
I would rather just use OpenCode - leverage AI models, even can host locally or paid ones with ease.
adchurch 12 hours ago [-]
We integrate with OpenCode too! OpenCode provides the harness, then the router selects the right model for the task.
We haven't yet set up local model routing though, that's really interesting - have you had any success using local models for coding tasks? Tbh I haven't heard many success stories from using local models yet
treexs 10 hours ago [-]
Ahh been working on the same thing for a while now but haven't launched yet
gopher_space 8 hours ago [-]
A lot of people are working on the same thing because nobody's come up with a definition of "thing" that people agree on yet. Your project would be valuable just for adding another point of view to the conversaion.
adchurch 8 hours ago [-]
Cool, interested to see your approach when you do launch! I think it's a really interesting problem
_pdp_ 13 hours ago [-]
Cool.. but I still don't get how this is going to save money. It seems to me that it might actually burn more money just because the whole system now seems to be coming from different LLMs.
Also, small LLMs are prone to stop before completion, throw errors and produce loops. Is this factored in the design of the tool? I am not sure.
edit: spellcheck
adchurch 12 hours ago [-]
It saves money because some agent sessions can be entirely handled by a smaller model (also relevant: subagents use fresh context windows so a subagent with a simple task can be routed to a smaller model even if the main agent needs a frontier model).
Totally right about small LLMs btw, that's why we trained this on real agent sessions where we forced it to use different models. If the routing model sees small models can't handle a certain type of task then they won't be assigned. (Also as a fallback we have some guardrails that will have a bigger model come in to "rescue" a smaller model if it gets stuck)
Reuben_Santoso 10 hours ago [-]
this is impressive. genuinely better than most people appraoches with using LLM as another judge to help route. which just uses more tokens than saves
adchurch 10 hours ago [-]
Appreciate the kind words! Lmk if you have any feedback on it from using!
arendtio 12 hours ago [-]
What is the difference from Cursors 'auto' mode?
adchurch 12 hours ago [-]
Fun fact: Cursor's "auto" mode is just Composer (or at least it was last time I checked). So it's different in the sense that it actually does route to more than 1 model
debarshri 12 hours ago [-]
It is funny. We are building something similar.
adchurch 12 hours ago [-]
Oh cool, feel free to reach out to me at andrew@workweave.ai if you ever want to share notes! We've learned a lot in the process of building this so far :)
mkagenius 12 hours ago [-]
We have created Murmur[1] which kind of works with your existing subscription (having API key is not mandatory). You can just tag @copilot @codex from claude code to delegate work to them. (it can also do it on its own too btw)
Very interesting - curious how you've used it yourself so far? I can imagine one use case would be having e.g. GPT 5.5 review Opus 4.8's work?
mkagenius 11 hours ago [-]
Useful in splitting a big task - some parts are easy so give it to say Gemini. Some are harder so give it to gpt 5.5 and so on.
Also the throughput kind of increases since providers are different.
emilio_srg2 12 hours ago [-]
but this means you work with API pricing rather than subscription pricing. Isn’t it better to use claude or codex CLI etc directly in terms of cost?
adchurch 12 hours ago [-]
If you have a Claude/Codex subscription then we use that (and account for the subsidized price accordingly when making routing decisions) instead of API billing. So you get the best of both worlds: subsidized usage for frontier models + save by using open/smaller models when it's genuinely better.
In practice, lots of ppl are using this to make their Claude sub limits go further!
emilio_srg2 12 hours ago [-]
I see but didn’t they severely limited the usage allowed with `claude -p`
adchurch 12 hours ago [-]
But we're not routing via `claude -p`, if you have sub usage available + it's the right choice to route to a Claude model, then the router is approximately a transparent passthrough. So it gets billed like normal `claude` usage rather than `claude -p`.
slopinthebag 12 hours ago [-]
> At Weave, we write ~all our code with AI
This is probably not a very effective way of marketing imo. At least, it turns me completely off.
adchurch 11 hours ago [-]
Fair enough, not meant to be marketing just a statement of fact. Would have turned me off too 18 months ago but times change...
ai_slop_hater 12 hours ago [-]
Isn't this more expensive than always using the same model, since, as I understand, by routing to different models you give up on cache?
adchurch 12 hours ago [-]
If you statelessly route each new request: yes it does end up being more expensive!
So our routing is cache-aware. It will have a much higher threshold to switch from one model to another if there's already some cache for the first model. Experimentally this solves the problem (like I said we've saved 40% ourselves vs. what we would have otherwise paid).
barmazoid 8 hours ago [-]
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kumiko_studio 10 hours ago [-]
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randomuser558 11 hours ago [-]
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james-mxtech 6 hours ago [-]
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gmziven 12 hours ago [-]
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iluvcommunism 12 hours ago [-]
This is basically what I need, a router. I’m tired of changing intelligence & speed levels manually.
adchurch 11 hours ago [-]
Nice, let me know any feedback you have from trying it out!
bijowo1676 11 hours ago [-]
How come data privacy and confidentiality is not an issue with services like these?
Do people voluntarily use these proxies/routers, knowing their prompts, outputs and code will be seen by other people ?
I get it might be ok for personal projects, but for anything that makes money and is a part of business... this must be big no-no ?
victorbjorklund 11 hours ago [-]
It is a router that runs locally.
adchurch 11 hours ago [-]
It's a real concern! We take this stuff super seriously (https://trust.mycroft.io/weave) and tbh most of our customers opt for the hosted version because it's much simpler on their end + they're already trusting us with a bunch of sensitive data.
But of course since the source is available you can also run it locally or self host
Could your router handle this?
1. Reiterate the cache issue from other comments already here. there is a lot of optimisation in harnesses around caching and a proxy model blows that up
2. Coding agents are model aware - they already route code discovery to mini / flash models, planning to heavy models, workflow design to ultra, implementation to mid / high etc. They know when they're exploring, planning, implementing, reviewing etc. and which model class to select and when it fails.
With a proxy you're breaking this control loop and feedback. It doesn't know, for ex. that it just attempted with deepseek v4 and it failed, lets try Opus?
3. How are you going to RL improvements and prevent the router becoming stale? You only have access to your own internal prompts and ~thousands of samples.
This is RL'd on one orgs codebase. There are going to be a lot of prompts you haven't seen before and have no insight to on how to route correctly, and you have no insight into users HF to improve your own model. Orgs aren't going to share their traces with you, so you need other sources to train on and improve
There are also new model releases every week that you need to keep up with - whats the story going to be here
4. Publish evals by running terminalbench / deepswe bench. Show us the performance / cost / time chart vs the other agent and model sets. If you can show gains there, you have a very simple value prop to sell where you can charge for a % of the saved costs
1. Agree it's important, fwiw the proxy model doesn't blow this up though - only incurs a 1 time cost when switching models and we're aware of that when making routing decisions
2. The agents are model aware yes but they are not incentivized to optimize too heavily here (in particular they don't use OS models even when they would be better). I think that's where this router comes in and brings genuine improvement.
3. Two parts here: 1 is continuing to grow our golden dataset over time, 2 is using reward signals from production traffic (on a per-customer basis or, if allowed, across all users)
4. Yes we have these internally, great callout that we should publish! Will do + will link from the repo soon. (Fwiw I think these benchmarks are useful but don't fully capture vibes - you should try it out yourself for that!)
How does this router translate to $$$ when developing?
This is the key thing that other routers we've seen miss: they're stateless so for a coding agent use case you end up spending more money due to all the cache misses.
In practice you just pick one and stick with it until the API stops or you hit performance issues.
It looks like much more context is required to decide on the best model (e.g., summarizing logs might use a cheap model, whereas you likely want Opus/Mythos/GPT 5.6 to debug multithreading logic). In an agentic system, a decision about the model may be embedded in the decision to orchestrate the model.
But intuitively I think it makes sense that a model can learn what model to route things to if it has all the relevant info, and experimentally it works pretty well in our experience
Hard to quantify this ofc but that's what I've felt vibes wise from using this for the last month.
It's also possible that it's the 1m context versus the 200k context (Copilot's limit) doing some of the work here.
Perhaps you're just not the best use case. It may work better when Average Joe is the one prompting.
Based on what OP said elsewhere in the discussion "threshold to switch to another model will be higher" means that essentially you reduce the workflow into two models at most. The two model primitive, one planner and one executor, is already sufficient for such a use case.
For lower than 2 models, it's just a simple single model cache-preserving conversation which arguably doesn't need another layer. For larger than 2 models, you are likely paying a large aggregate cache penalty that negates most of the gains
1. Small models can carry out a good number of requests e2e 2. Small model for part of a request + cache miss < big model for entire request in many cases 3. Subagents
For our own usage we've saved 40% so far (that is of course including costs of uncached requests when switching models)
For example, if my task was "refactor this component to decouple all messy nesting", the problem router can't possibly know what is being referred to. This works for clear cut and dry problems but not for ambiguous problems. Most of the real world problems carry a lot of ambiguity.
So: we can reasonably cluster similar problems together and learn how models handle them, and the entire system doesn't fail if the initial decision is off.
I'm just trying to figure out why on the fly routing would beat testing and tuning and locking models and versions for each class of call, with evals and auto tunes running to explore more possible models for commonly run classes of prompt over time . . .
I guess that sounds a lot like moving your evals and auto tunes to a third-party, but I don't have the time, budget, or inclination to create a system like this out of whole cloth and then keep it relevant.
I could see something that provides on-the-fly routing information being useful, but actual decision-making is too dependent on context.
Out of curiosity, how does it compare with vLLM Semantic Router?
For reference:
https://vllm-semantic-router.com/
https://github.com/vllm-project/semantic-router
vLLM Semantic Router: Signal Driven Decision Routing for Mixture-of-Modality Models, https://arxiv.org/abs/2603.04444
https://github.com/vllm-project/semantic-router
For instance, does it offer similar algorithms:
- vllm-sr/auto: efficient, fast, balanced routing, similar in spirit to Fugu // Sakana Fugu — Multi-Agent System as a Model: https://sakana.ai/fugu/ - vllm-sr/fusion: panel-style multi-model reasoning and synthesis. - vllm-sr/flow: router-native workflow orchestration - vllm-sr/remom: multi-round reasoning over one or multiple models.
FWIW, it does look good on https://routeworks.github.io/leaderboard
Ref.
RouterArena: An Open Platform for Comprehensive Comparison of LLM Routers, https://arxiv.org/abs/2510.00202, https://github.com/RouteWorks/RouterArena
As another commenter (https://news.ycombinator.com/item?id=48689994) pointed out, for one-off requests, I think it makes more sense to lock to one model whose behavior you understand very well. For dynamic requests like the ones going to a coding agent I think dynamic routing makes more sense but it does need to be cache aware.
Maybe you should move away from a subscription that started charging by the token instead of by the request?
And it is SO fucking cheap.
I imagine a solution like this will eventually be an enterprise-forced solution because there is no reason right now for individual developers to be selective about model pricing. Even more important is non-tech users who do stuff through MCPs like "give me a full overview of all analytics" and let it chug for half an hour.
As prices increase we will see more of these tools to optimise and make the best use of token budget
Have you done any A/B tests on this with evidence? (That's one thing I'd be very interested to see for claims like this - I'm not necessarily doubting you, it just seems like it could be useful to understand claims of quality/efficiency)
That is super curious - using more low quality cheaper models increased your velocity? My prior would have been slightly reduced velocity but massive reduction in token costs made it worthwhile.
Is that due to the faster inference time?
Happy to talk about this in some more depth if there's anything specific you're curious about!
Also i am pretty sure neither open ai or anthropic leets you seed the agents own tokens.
Will this use my Claude Pro/Max subscription? Or will it always use the API billing "pay as you go"?
We haven't yet set up local model routing though, that's really interesting - have you had any success using local models for coding tasks? Tbh I haven't heard many success stories from using local models yet
Also, small LLMs are prone to stop before completion, throw errors and produce loops. Is this factored in the design of the tool? I am not sure.
edit: spellcheck
Totally right about small LLMs btw, that's why we trained this on real agent sessions where we forced it to use different models. If the routing model sees small models can't handle a certain type of task then they won't be assigned. (Also as a fallback we have some guardrails that will have a bigger model come in to "rescue" a smaller model if it gets stuck)
1. https://github.com/instavm/murmur - Murmur
Also the throughput kind of increases since providers are different.
In practice, lots of ppl are using this to make their Claude sub limits go further!
This is probably not a very effective way of marketing imo. At least, it turns me completely off.
So our routing is cache-aware. It will have a much higher threshold to switch from one model to another if there's already some cache for the first model. Experimentally this solves the problem (like I said we've saved 40% ourselves vs. what we would have otherwise paid).
Do people voluntarily use these proxies/routers, knowing their prompts, outputs and code will be seen by other people ?
I get it might be ok for personal projects, but for anything that makes money and is a part of business... this must be big no-no ?
But of course since the source is available you can also run it locally or self host