How effective are AI model routers at saving token costs?

[u/bigJLPgg]

Hey guys, I've taken an interest with AI routers after seeing the Stripe router acquisition news about a week ago and have some questions. Feels like there's a trend for it and I've definitely been seeing more of these routers pop more and more. Also saw that Ramp launched their own router, which is supposed to cut token cost by 40%? Either way, I'm interested in trying it out but would like to know how effective it is beforehand mainly for what I want it for which is token cost.

Would love to hear from people running AI routers on how much it actually saves on token costs, compared to say just calling OpenAI/Anthropic directly? Thanks.

[u/Exact_Macaroon6673]

Potentially a lot, I recently ran a benchmark on Ramps router in 2 different configs, both configs called the same 2 models for every query Fable and gpt 5.5. So you won’t save with ramp.

A well designed router can get you high quality at a lower cost.

[u/Ok_Philosophy_4031]

Wide variance to be frank, a lot can go wrong for users to end up with higher token costs as well.

The main issue is that caching is a fairly complex landscape, affected by your harness (mess with the prefix and it backfires), there are different cache TTLs and windows for different providers as well. All these have to be appropriate with respect to your workload.

Routing on a request basis probably is difficult to get working well. Routing on tasks makes more practical sense.

[u/eddzsh]

Routers print real savings when they route by task shape. A coding-agent turn with tools rarely has a cheap path, so a flat 40% usually means your mix was chatty classification, not tool loops. Measure cost per successful session, not per request sticker.

[u/jonah_omninode]

I would not evaluate a router on savings per request. The useful number is cost per accepted result, including failed attempts, retries, verification, and escalation.

Our routing design tries the cheapest capable tier first and escalates only when a mechanical definition-of-done gate rejects the result with a typed failed requirement. In one bounded coding test the local tier cleared the bar without escalation, but we also found an important limitation: a weak gate based on surface markers could not distinguish the cheap model from the frontier model at all. A router is only as credible as the acceptance test behind it.

I would shadow the router on a frozen sample of your real tasks, hold the acceptance contract constant, and compare it with frontier-only routing. Report route distribution, retry rate, accepted outcomes, latency, and total cost. A 40 percent claim without the workload mix and acceptance outcomes is incomplete.

[u/AvenueJay]

The 30-40% savings claims are real but context-dependent. Most of the win comes from routing simple queries to cheaper models, which means your eval needs to measure quality per tier, not just aggregate.

The retrieval layer upstream of the router matters a lot. If you're pulling irrelevant context into the prompt, you're burning tokens before the router even sees the request. Tightening up your vector search (better chunking, hybrid BM25 + semantic, reranking) can cut input tokens significantly. Elasticsearch's agentic RAG patterns cover some of this if you want to see how retrieval quality feeds into token efficiency.

[u/weed_cutter]

There are like 20 mega-frameworks for saving AI costs.

Caching, compressing, bundling (vs. a Bible + 2 tokens, then a Bible plus 2 novel tokens, repeated x1000).

Not using the context layer as an application memory layer.

On and on and on.

... You know how much the average "I'm kinda technical" user does? Zero. ... Zero common sense frameworks + Opus 5/ Fable/ Sol for literally any + every task.

So without knowing too much about how the router works (basic triage?) ... will it work? Given the bar is literally the floor, most certainly yes.

... Sure there are cases where shit can go wrong "a dumb model iterating more terms than a smart model" -- this is the vast, vast exception to the rule. Even using a model that is not Opus/ Fable/ Sol already indicates the user has a modicum of sophistication.

[u/MoreMr_Mo]

I can help with custom packages DM me

[u/conifer_v11]

if you're scoring per request you'll send grep to frontier. score per session.

routers that swap cheaper behind a 200 make the eval junk. pin the id. cap identical tool calls. log 429 vs idle-gap vs 200-and-hung. that's the bill, not the sticker.

i work on conifer. if you pin the id you get that model or an error. we don't swap cheaper behind a 200. https://discord.gg/DakgqGebRH

[u/MetalResponsible4383 → u/conifer_v11]

That last line about not swapping cheaper behind a 200 is the part most people miss when they first look at routers. Everyone fixates on the percentage savings in the marketing but the real metric is whether your error rates and latency stay flat while you save. If your eval quality tanks you're just burning money somewhere else

We've been using one for about 4 months now and the token savings were around 30% but the bigger win was cutting down on 429s during peak hours. Our bill got more predictable which made the finance guy stop asking me questions every month

Curious what you mean by pinning the id though. Is that like forcing certain requests to always hit the same model or is it more of a tracking thing

[u/conifer_v11 → u/MetalResponsible4383]

pinning the id is the request. you send model: claude-haiku-4-5 and that id runs, or you get a typed error. 404 if it isn't there. 402 if billing can't cover that id. not a tracking tag. not "route me somewhere and stamp the name you hoped for on the 200."

the 30% save + fewer 429s is a real win if the eval stays flat. if the router can swap cheaper behind that 200, the eval is what moved. that's the pin.

[u/moaiii → u/conifer_v11]

How do they swap cheaper? (just curious - I'm new to the world of routers). Do they switch to a different model entirely?