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原始链接: https://news.ycombinator.com/item?id=40922739

这个 RouteLLM 框架听起来确实很有前途，特别是对于成本优化而言。这让我想起了 KNN-router 项目（[https://github.com/pulzeai-oss/knn-router](https://github.co...)，它使用 k-近邻方法来路由我喜欢这些类型的解决方案，因为它们解决了使用多个 LLM 的实际挑战、速率限制、每个代币的成本，甚至只是为工作选择正确的模型都可能是一个真正令人头痛的问题。例如，KNN-router 允许您定义自己的路由查询逻辑，因此您可以考虑模型准确性、响应时间和成本等因素，您甚至可以在主要模型不可用时设置后备模型。这很酷。看到这类工具的出现，因为它表明人们开始认真思考如何建立强大的、具有成本效益的法学硕士管道，随着越来越多的公司开始将法学硕士纳入他们的产品和服务，这将变得至关重要。

This RouteLLM framework sounds really promising, especially for cost optimization. It reminds me of the KNN-router project ([https://github.com/pulzeai-oss/knn-router](https://github.co...), which uses a k-nearest neighbors approach to route queries to the most appropriate models.

What I like about these kinds of solutions is that they address the practical challenges of using multiple LLMs. Rate limits, cost per token, and even just choosing the right model for the job can be a real headache.

KNN-router, for example, lets you define your own logic for routing queries, so you can factor in things like model accuracy, response time, and cost. You can even set up fallback models for when your primary model is unavailable.

It's cool to see these kinds of tools emerging because it shows that people are starting to think seriously about how to build robust, cost-effective LLM pipelines. This is going to be crucial as more and more companies start incorporating LLMs into their products and services.

Cost is a plus but at least what I see is that getting good response time is even bigger. Something like OpenAI Azure instances are inconsistent and it is far too normal to get a 40sec lag with responses with gpt4-o.

I don't really get who these are for - do people use them in their projects?

I don't find success just using a prompt against some other model without having some way to evaluate it and usually updating it for that model.

> Trained routers are provided out of the box, which we have shown to reduce costs by up to 85%

The answer is here. This is a cost-saving tool.

All companies and their moms want to be in the GenAI game but have strict budgets. Tools like this help to keep GenAI projects within budget.

For the company I work for, one of the most important aspects is ensuring we can fallback to different models in case of content filtering since they are not equally sensitive/restrict.

From what I understand, it's from people using it in their workflows - say, Claude but keep hitting the rate limits, so they have to wait until Claude says "you got 10 messages left until 9pm", so when they hit that, or before they switch to (maybe) ChatGPT manually.

With the router thingy, it keeps a record, so you know every query where you stand, and can switch to another model automatically instead of interrupting workflow?

I may be explaining this very badly, but I think that's one use-case for how these LLM Routers help.

We get rate limited when using Azure's OpenAI API. As a gov contractor working with AI, I have limited means for getting access to frontier LLMs. So routing tools that can fail over to another model can be useful.

You may have a variety of model types/sizes, fine tunes, etc, that serve different purposes - optimizing for cost/speed/specificity of task. At least that's the general theory with routing. This one only seems to optimize for cost/quality.

Interesting that it is generalizable to other pairs. That implies some sort of prompt property or characteristic that could be widely used.

I don’t think using different models is the right approach though. They behave differently. Better to use a big and small one from same family. Or alternatively using this to drive whether to give the ai more “thinking time” via chain of thought or agents.

I like their "LLM isovalue" graph, and the idea that different vendors can be forced to partake in the same synergy/distillation scheme. Vendors dislike these schemes, but they're probably OK with them as long as they're niche.

I've been using OpenRouter only for personal use, not for its router functionality, so I can use the API of various models (or open-source models) without signing up and prepaying/paying a subscription on all their websites.

I believe OpenRouter also provides an API that does the same thing as RouteLLM. Again, you only have to pay OpenRouter, not every model's service you use.

OpenRouter is also interesting solution but I almost end up using like one or two LLMs and I rarely feel the need to switch between different LLMs so I ask why I am even using openrouter in the first place.

not true at all, you could have a efficient cheap model which is generally terrible at most things but has a savant like capacity for categorizing tasks by requirement and difficulty. Even easier when you dont need to support multiple languages and a truly staggering breadth of domains, like a conventional llm does. You could train a really small model to reject out of domain requests and partition the rest, running at a fraction of the cost of a more capable model.

In this paper, they tried a couple different methods for determining how similar the incoming request is to requests that they have scored in their dataset. Actually, one of the best methods they used does not involve using a model at all to evaluate the incoming query (similarity-weighted ranking) although it _does_ use pre-trained embeddings.

Using this, they were able to produce quite good results applying this similarity measurement to unseen queries using a standard benchmark. The leap of faith here is assuming that the same query similarity method will continue to bear fruit when extended to queries that aren't benchmarkable.

The weak-to-strong assumption is that it is easier to eval the result of a task than to generate it. If it is wrong, human can not make a stronger intelligence than us.

The tool currently allows only one set of strong and weak models.

I’d be really good to allow more than two models and change dynamically based on multiple constraints like latency, reasoning complexity, costs, etc.

actually ModelBox(model.box) offers that, the autorouter function can dynamically switch to different models according to latency, geo-position and costs.

I think unify.ai (like openrouter) does that - it has several paramters you can choose from.

But the underlying "how to choose a model that's smart enough but not too smart" seems difficult to understand.

Some of that is already possible, since it can generate a difficulty score for a prompt that could be manually mapped between models based on ranges.

You're reading it right. They have developed a system that automatically decides which model is sufficient, depending on your inputs, saving you costs even within one conversation stream.

The OpenAI-compatible API allows you to talk to the router like a regular GPT model.

solution for a non-critical problem imho

im open to differing opinions but after dealing with langchain, premature optimization for non-critical problems is rampant in this space rn

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