Looking at all these releases it’s not a surprise that model routing is the fastest growing segment in AI right now.
There are 10+ LLM companies, each with dozens of models of different modalities, each model with multiple size variants, then different “thinking” levels, then agentic modes, “pro” modes, a “fast” option, standard vs flex vs batch execution. And of course each end combination has a different input/output/cache token price.
Companies that say “give me a prompt and I’ll route it to the most ideal and cost effective model and setting for you” are capturing a ton of value from a gap that model developers don’t seem to understand exists.
I think what the parent is saying is that the model itself has the best context for whether a portion of a request should be routed. The specifics of that routing (e.g., should you route to KimiK2) are something that can be trained, finetuned, or even included in a model's startup context.
This doesn't seem quite right. For one, I don't need all the intelligence of an expensive model like Opus 5 to do the relatively simple task of choosing a correct model for a task. Additionally, since this isn't something Anthropic would ever put effort into doing well, you could tune a model to do better and faster than Opus 5 does out of the box.
My experience is the opposite - for many cases it’s not very obvious how good a model needs to be to solve it. Worse models tend to just follow their first instincts without proper reasoning
And also btw you don’t need a
routing company to decide, you can do it on your harness. And yeah my Fable has zero issues delegating to Terra instead of Opus.
Are you thinking of single shot? For agentic use cases, your ability to manage cache and context becomes much more important. Price per individual request becomes less about tokens and more about cache.
Sure Claude would comply, but Anthropic has no financial (or other) incentive to optimize this, so there’s no reason to expect it to be particularly good.
It would be like asking the clerk at a Whole Foods which grocery store in the city sells the cheapest eggs. He’d probably answer - he might not even say Whole Foods - but WF is hardly teaching all their staff the best methods to answer this question in training. (Heh, training.)
Yes. I have Claude route to codex all the time as part of a dev process where fable is manager and it oversees the work of sidekicks and subagents. It’s happy to comply.
Which is why 3rd party routers which do route between different models may have an edge. It means they can compete on cost, and it’s definitely not clear that the architectural optimization is always going to be higher quality or cheaper. It might be, but everything changes constantly, so locking into a single model family/company is very much not ideal
The only metric that matters is f(cost, time, task). If Anthropic has a SOTA model, it can easily distill every model in the pareto frontier and have the SOTA model route appropriately. They haven't felt the 'sting' yet to optimize that (still growing their ARR at a crazy growth rate)
Ant can top any benchmark that measures f(cost, time, task). The only entities that can beat them in costs are infrastructure providers who can do optimization at that layer. But a pure Model company can *never* compete with Anthropic on f(cost, time, task) if they continue to have SOTA models
Imo the main issue behind model routing is you need to figure out how much intelligence a new task takes, which is a very non trivial problem. Presumably, a organization knows this about their own tasks and is better suited to built in-house compared to outsourcing to a vendor.
> Model Routing will always be done better by models themselves.
> The models themselves will get better at this and frontier companies will simply give that capability
I would never trust something like model routing to the same company that would profit from it, and that goes for telling models doing their own routing when that could easily be trained into the model to make things more expensive. Sort of a conflict of interest.
Models are first and foremost trained by corporations.
Model routing by the model itself requires the model to pull in a lot of context and it's likely more efficiently just done by people with the context already in their head, even assuming the model is perfect at routing (which last I checked, Claude definitely isn't). I wouldn't trust ML model routers.
Because they're trying very hard not to understand it.
Otherwise the expensive-yet-powerful model probably won't see much revenue. How much money is there in bleeding edge scientific research? There's a lot, but there's even more existing capital in paying people people to do college level paperwork, and the bulk of those traffic gets routed to the cheapest model.
You mostly don't need super powerful AGI to replace the paper pushers, but the frontier labs are trying to position themselves as being uniquely capable of producing super powerful AGI, and also be the ones replacing office workers.
Not sure how it will work out for them, but I think model routing is going to poke holes in that narrative. That's why I think they're trying very hard not to understand model routing exists.
It wasn’t long ago at all that the chief problem was “can AI even help me with this” (cost be damned). Until a time when the answer is an unmitigated “yes, obviously”, the frontier labs have everything to lose and nothing to gain on routing, because if they screw it up you might incorrectly decide “nope, it can’t help yet” due to a poor routing decision.
Is there really that much money in bleeding edge scientific research though?
This is what terrifies me about this whole ordeal economically. Maybe we get AGI and it is not worth anything close to what we thought it was for those who have a bet on it.
I think of what was the direct, economic value in the betting sense of quantum mechanics or relativity? Huge value at the systems level of society but as you scale down towards the individual the value is more and more dispersed to the point I would think any pool of bets would have all not paid off.
You can't monopolize and commoditize relativity.
I almost think there is a kind of dutch book against the AI equity holder because even in the best case scenario the bet doesn't pay off anything close to what is expected for an individual bet.
Who are the customers though? Honest question, I'd like to understand it.
For me, anything other than current best available SOTA for any task is unacceptable. The only routing rule I need is "the most powerful model I still have flat-priced quota available for". I mean, why settle for less?
There are two types of users: those who are able to use subsidized rates, and those who need to use API rates due to audit requirements, enterprise billing, etc.
Model routing for subsidized users takes the form of a "use Opus 5 subagents for implementation" type of system prompt. You lean into a single provider, build tooling around that, and your savings are far beyond anything multi-provider routing can get you.
on top of that there is also additional factor: speed - sometimes if task is easy you do care to finish it faster.
There is also matter about convenience - when I ask some small easy question often I don't bother to switch the model or forget in prompt to ask faster/cheaper subagent.
It's very common to use a lesser model for a lesser task, resulting in same quality output. End result: save money while being faster. In many cases, it's a pure win-win.
But in other many cases you have to redo the work directly or indirectly, and you are more expensive (for now) than even the most expensive models, so sounds like a total lose.
I'm not a customer of those routing systems, but I quite often use different Claude models for different tasks. While most tasks were Opus 4.8, I often used 4.8 to make a plan, prompts, and package kit to setup Fable for a bigger project, then run it on Fable. Or, for broad single-task searches Sonnet with or without "Research []" turned on seemed to work best both faster, lower overhead, and less verbose answers (when I didn't want it).
And you do not have flat priced quota for Fable 5, right? Because nobody has, as far as I know. So you'll probably not route any task to the "current best available SOTA".
Also: quota. Implies you do not have unlimited access even for flat prices. Which in turn implies that as soon as you hit the quota on the most expensive flat price plan, even you will suddenly discover the magic of economically sensible behavior.
Not everyone is you. Other people probably have a range of tasks that can accomplished with different models.
Certainly if I'm confident that I'm going to get what I need from a faster model, that's what I want to use, rather than wasting time grinding away for the sake of saying of the same answer came from a SOTA model.
Given that every chatbot does offer a range of models, it seems clear people do choose among options.
The mental effort in estimating what model would be better is so not worth it.
I just want to switch to Claude Code, tell it to turn a .csv into a BigQuery table then cmd+tab to something else while it runs. Thinking "oh this is probably an easy task, I can /model to Sonnet to save $0.0004" is silly.
The game changes when it's not just "Opus vs Sonnet", but "Opus vs GLM". The amount saved is way more than even $0.04. And it's not only money but speed. Some providers can serve GLM crazy fast - I'll even go outside of my subscription to pay extra money for the quick results.
not just that -- "less" ($$$) can also result in indistinguishable quality for some tasks/inputs. I'd argue this is the primary reason, secondary being speed.
I generally agree. Perhaps there's only a 5% chance that it would write better code or find a bug that it wouldn't have with a lesser model, but the economics of bugs is strong enough that preventing a single bug is worth hundreds of dollars.
lol I had to get ChatGpt to explain to me the difference between 5.6 sol, 5.6 Terra, 5.6 Luna, 5.5, 5.4 mini, 5.3 spark, and then there is low, medium, high, extra high, max, ultra, and pro… I still don’t really know, it feels like ordering hot wings.
And it's not just that model routing is much cheaper: no longer than yesterday we got a post showing that routing between K3 and Fable 5 was more SOTA than either of those.
If that is true, model routing is here to stay.
It also seems to validate the minimalist approach of pi.dev, where sub-agents from the same company is not the preferred approach (pi.dev believes in neither sub-agents all from the same company nor MCP even you can do it if you want for pi.dev's philosophy is to do add any functionality you want to a minimal harness).
Now of course we'll get for a few weeks all the Anthropic fanbois and shills explaining that "sure, K3 was basically at the level of Fable 5 but now that Opus 5 is out, open-weights models are six months behind".
How does model routing work if the prompt is static? Do the routers attempt to tweak the prompt to make it the best version for that model? For example, reading the “migration” guides for Claude Opus 4.6 -> Opus 5 is exhausting. Adding in all of the other models complicates it even more. Especially across vendors.
I never signed up because I found the 5.5% fee on token usage to be a "screw you" tactic. Still do not understand why they are popular with the other options out there.
what's the threshold for model routing where you're willing to trust the router?
For coding my own work I don't trust the model router, and it would have to be shown to be to save a real dollar amount.
From a buying perspective it's a hard sell to save x but lose out on bugs you are probably introducing at an unquantifiable severity and frequency. How much is it worth to hedge your bets by doing every single inference request on the frontier model?
How much will it cost to go back later and fix things, but also the meta question of how to be able to decide on a hypothetical unknowable? (You'll never know how much better or worse your code was gonna be, it's untestable at a project level)
There are 10+ LLM companies, each with dozens of models of different modalities, each model with multiple size variants, then different “thinking” levels, then agentic modes, “pro” modes, a “fast” option, standard vs flex vs batch execution. And of course each end combination has a different input/output/cache token price.
Companies that say “give me a prompt and I’ll route it to the most ideal and cost effective model and setting for you” are capturing a ton of value from a gap that model developers don’t seem to understand exists.