alt Hacker NewsShow HN: Smart model routing directly in Claude, Codex and Cursor
We built a model router that plugs into coding agents (e.g. Claude Code, Codex, Cursor, etc.) and intelligently sends requests to the best model to serve them. Here's a quick demo of running it locally: https://www.youtube.com/watch?v=isKhAyivtfM.
At Weave, we write most of our code with AI, and it's been getting more expensive. This came to a head when Opus 4.7 was released and, thanks to its tokenizer changes, our costs shot up. We knew we didn't need Opus for everything but we didn't want to lose out on the intelligence for the cases where you really need it. So we decided to build a model router to handle this for us.
The Weave Router acts as an Anthropic/OpenAI endpoint specifically for coding agents. It looks at every inference request and intelligently (more on that in a sec) decides what model to send it to, handling all the translations required along the way. So it can use faster/cheaper models (e.g. DeepSeek v4, GLM 5.2, Kimi K2.6) when possible, and frontier models (Opus 4.8 & GPT 5.5 (& Fable whenever it's back)) when necessary.
How do we know what model to route to? We trained an RL model on tens of thousands (so far!) of agent traces. We reward the routing model when it selects an LLM that successfully completes the given task.
Here's an example: if you ask the router to plan a complex change, it will (probably) route that request to Opus 4.8. Subagents exploring the codebase to gather context will be routed to more suitable models (e.g. DeepSeek V4 Flash). Then when you have the plan ready to implement, it will be (most likely) be handed to a quicker model (e.g. GLM 5.2) to carry it out.
We've been using this internally for the last month or so. We've saved 40% on tokens vs. what we otherwise would have paid, with no noticeable differences in quality or velocity.
The router is source-available under Elastic License 2.0, so you can self-host it. Or if you prefer, you can also use our hosted version: weaverouter.com.
I'll be here to answer any questions you may have!
Comments
I see a great tension in the market today. On one hand you want agents to work reliably and that needs a lot of harness, computer use, model routine, tasks running longer etc. And on other hand you simply want to reduce your dependencies and costs. Agent building is very nascent and all the frontier companies are trying to build the best harnesses possible. As basic prompting, researching, coding gets mature, more and more of such tasks will be optimal for model routing open source etc etc but there is a chance that by that time frontier models again make costs and routing, low and effortless. Basically I believe everyone has started jumping to the. -- REAL PROBLEM IS COST v. REAL PROBLEM IS EFFICIENT, RELIABLE AGENTS/WORKFLOWS. It's going to be very interesting to see how this plays out.
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?
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.
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.
Looks interesting!
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
There are so many of these projects to wrangle AIs I think we might need an AI to go through, analysing each and amalgamating the good bits.
It makes me think of MakeFiles.
Make is sufficiently bad that everyone who has used it has considered writing a better way to do it. A good percentage of those people have done so.
On the other hand, make is also not so sufficiently bad that it cannot do its job. The choice becomes picking the thing that everyone has or one of the many many alternatives that proclaim their strengths and leave their weaknesses lurking to bite when they are least expected.
No single replacement to make dominates, and make lives on. I wonder if AI management is on a similar path.
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
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?
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 . . .
I generally believe the proxy route is best to understand any harness. I been building some thing similar.
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…
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.
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
Just curious how the router decides on which model to use. When I use Claude Code, I often ask Claude Code to decide itself if it should spawn a sub-agent to downgrade or upgrade the model. Claude Code is smart to know how much context and cache it has and will decide if it should use sub-agent with a lesser model (sometimes it costs more to re-fetch tokens with a Sonnet sub-agent if the parent agent already has the context).
> 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)
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.
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?
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.
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
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?
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?
"We reward the routing model when it selects an LLM that achieves the task successfully" sounds pretty oversimplified
Can't really win can ya? Scarce? They're driving up prices! Plentiful? It's all a big bubble!
This might be a stupid question, but can a extra added local llm help with the caching problem?
This is cool!
Will this use my Claude Pro/Max subscription? Or will it always use the API billing "pay as you go"?
Wont this kill the kv cache?
Also i am pretty sure neither open ai or anthropic leets you seed the agents own tokens.
I would rather just use OpenCode - leverage AI models, even can host locally or paid ones with ease.
Ahh been working on the same thing for a while now but haven't launched yet
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
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
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?
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)
1. https://github.com/instavm/murmur - Murmur
Curious what's the typical switching frequency in your experiments and experience. How do you control the tradeoff of cache matching and model efficiency?
> 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.
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?
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This is basically what I need, a router. I’m tired of changing intelligence & speed levels manually.
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 ?
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