21 posts tagged with "routing"

We’re improving Fuse V2 by combining complexity and capability assessment to learn where each model succeeds, fails, and needs an upgrade.

We solved 23 of 25 SWE-bench Verified tasks with LiteLLM's experimental capability router for $11.15. With Opus 5 for all solver calls, we solved 23 tasks for $20.27. We spent 45% less, including classifier calls, with prompt caching on in both runs.

We've updated the Auto-Router to account for how Claude Code and Codex package requests. It now removes more harness context from classification, preserves encrypted delegated tasks, and shows exactly what the classifier received

Heuristic v1 scores seven prompt signals, including reasoning language, code, technical terms, and prompt length. You can tune those signals for the traffic your router serves.

An experimental router that picks a model per agent phase matched fixed Claude Opus-5 quality on a SWE-bench Verified subset for 46% less money.

The complexity router's LLM classifier can now be compressed more aggressively than your model calls. In internal testing, that cut classification costs a further 32% beyond what shared compression was already saving, with no change in routing accuracy.

Heuristic v2, LiteLLM's new AutoRouter classifier, is up to 45% more efficient than Heuristic v1: more tasks solved, at lower cost, in less time. Only the classifier changed.

LiteLLM Auto Router Fusion solved 14 of 21 Terminal-Bench tasks; Claude Fable-5 on its own solved 9. Fusion runs the task on several models in parallel and has one of them synthesize the candidate work into a single answer. Both arms ran the same 21 tasks.

The Auto-Router picks a model by asking how hard the request is. This release adds the questions that come right after: does the request fit the model we picked, and can that model even see it?

We have shown the Auto-Router saving 51% in production and 69% stacked on prompt caching. The question we hear next is always the same: "would it hold quality on my traffic?"

You can expect roughly 40% cost reductions from day one with the Auto Router, and more as the tier maps are tuned. One of our production users shared their statistics to show what that looks like at scale.

They rolled out the Auto-Router to 450+ users across dev, staging, and prod instances and saved $12,249 over 270k+ requests.

An auto router matched Claude Opus-5 solve rate on a 21 task subset of Terminal-Bench 2.0 at 27% lower cost. Every arm ran the same 21 tasks, so the comparisons are like for like.

We've made it easier than ever to setup and test your Auto-Router, and with customizable tier names + classifier system prompts you can go beyond complexity routing.

v1.97 makes three changes to the auto router.

• The LLM classifier now receives a window of prior conversation turns, defaulting to three. This improves accuracy of follow-up classifications from 14% to 78%, costs at most $0.61 per 1,000 requests, and no additional latency.
• A new Benchmarks view prices routed traffic against an all-frontier baseline and reports the difference, and those savings now also appear in the Cost Optimization totals.
• Session affinity is now off by default, following our previous post showing this was leading to worse quality without cost improvements.

Yes, you can use prompt caching with Auto-Routing. The two compound rather than cancel out. We measured it across five datasets, two of which report what the provider's cache actually did.

Auto routing promises a smaller bill without a worse answer. We measured both halves against a baseline that sends every request to claude-opus-5: 8,619 graded prompts and cost simulations over 14,000 real conversations.

Router plugins are now available on LiteLLM. Each plugin receives the routing context, enriches it, and hands it to the next before the router makes the final decision.

The push came from the autorouter discussion (#32168): teams wanted to layer their own signals (language detection, domain classification, tenant policy, budget caps) onto routing without waiting for each one to land in core. This plugin extension lets teams make these changes while keeping LiteLLM's routing core stable.

Auto Router v2 collapses complexity, semantic, and adaptive routing into a single auto_router/complexity_router. One config now covers heuristic scoring, LLM classification, lexical or semantic keyword rules, and Thompson-sampled tier pools.

The push came from the community. On discussion #32168, users pointed out that all three routing strategies should converge into a single Auto Router. One router with configurable signals and weights keeps the API simple while letting the routing engine evolve internally, instead of forcing you to pick a mode up front.

The operational half came from discussion #32172: predictable beats clever for debuggability. A fixed, versioned mapping from capability class to model is what makes "why did this response cost 4x today" answerable after the fact.

LiteLLM now supoports videos character, edit and extension apis.