# LLM Router with Jev: Route by Cost and Quality | QQuantum.ai > An LLM router that sends each AI request to the right model by difficulty, quality and budget. See our Jev-based design, fallback paths and a measured pilot. Source: https://qquantum.ai/technology/jev/model-routing [Home](https://qquantum.ai) [technology](https://qquantum.ai/technology) [jev](https://qquantum.ai/technology/jev) model-routing Jev use case · Model routing # The right model. For this request. An LLM router decides which model should answer each request, so a short classification is not billed like a difficult analysis. QQuantum.ai is building one that assesses the work before choosing: Jev supplies fast judgements about difficulty and context, and the routing policy balances quality, cost and capacity. It is for teams with mixed AI workloads; no production savings are claimed yet. Scope your model router In development · No production savings claimed All Jev: AI Tools & Fast Decisions → Introduction transcript. Qubi: Simple requests and difficult ones often pay the same expensive model price. We assess each request first, and check which models it is allowed to use. Routine work takes the low-cost route; when a check fails, it escalates. Then we count the full cost of every route, retries included, against quality. ## The opportunity ## Stop paying the same price for different work. A short classification and a difficult technical analysis should not automatically receive the same model budget. Our router design separates the decision about what a request needs from the model that ultimately answers it. Jev can assess task category, whether the request needs extended reasoning, and whether the available context appears sufficient. Your application then checks the allowed providers, token budget, current quota and latency requirements. This is an application design informed by TypeSafe’s [intent-routing pattern](https://docs.typesafe.ai/patterns/intent-routing). The router estimates requirements from the request and observable context. It does not inspect a model’s private thinking. Straightforward work can take a lower-cost route; uncertain or demanding requests can escalate to a stronger model or human review. ## Proposed routing flow POLICY BEFORE GENERATION 1. 01 Request task + relevant context 2. 02 Eligibility rules privacy + capacity 3. 03 Jev assessment complexity + confidence 4. 04 Model selection cheapest eligible route 5. 05 Quality check accept or escalate Privacy constraints run before any external call. If Jev itself is not an eligible processor, the request takes a permitted alternative. ## The cost model ## Count the gate. Count the retries. A cheap routing call is useful only if the complete system costs less at the required quality. Compare total spend per accepted answer: the router, selected generator, verification, retries and escalations. Include duplicated input when more than one model sees the request. For an illustrative routing request with 2,000 input tokens, the published Jev input price gives $0.000084 per assessment, or $8.40 for 100,000 assessments. Calculation: 2,000 × 100,000 ÷ 1,000,000 × $0.042. This excludes downstream model calls, retries, hosting, tax and engineering. It is arithmetic, not a measured saving. [Price checked 18 September 2026](https://docs.typesafe.ai/models). Reducing expensive calls may preserve a provider’s available capacity. It cannot increase your contracted limits or bypass rate limiting. A router needs per-provider budgets, backoff and an explicit unavailable state. ## Two ways to route PRE-ROUTING ### Choose before generating Use a small set of request features to choose an eligible model. This avoids paying a frontier model on every request. The risk is sending difficult work to a model that cannot handle it; track those mistakes explicitly. CASCADE ### Verify, then escalate Start with an economical generator, check its answer against the source, then escalate when the check fails. This spends more on difficult requests but can retain a cheaper path for routine work. TypeSafe demonstrates the second pattern in its [structured extraction cascade](https://docs.typesafe.ai/cookbooks/sde_cascade). That example is not a benchmark of our router. ## The pilot ## Prove the route before you trust the saving. Start with a labelled set of real requests and the current system as a baseline. Run the proposed router in shadow mode: record what it would choose without changing the user’s answer. Compare success rate, escalation rate, total API spend and response-time percentiles by task category. Use a separate holdout set to tune and then test the confidence gates. A confident decision can still be wrong, and a score is not an account permission. Pin the evaluated version and replay the holdout set when it changes. TypeSafe’s [confidence documentation](https://docs.typesafe.ai/confidence) explains the distinction between the distribution and its confidence summary. For a tiny workload or a task already handled well by one inexpensive model, the extra gate may add cost and latency. Our [hybrid routing guide](https://qquantum.ai/infrastructure/hybrid-model-routing) covers the broader deployment choices. Questions ## Questions about Jev Model Routing. ### How much will a Jev router save? That depends on the workload, selected models and escalation rate. We have not published production savings for our router. A pilot compares total cost per accepted answer with the existing system. ### Does routing bypass API usage limits? No. It can reduce unnecessary calls to constrained models, but every provider’s limits still apply. Capacity checks, budgets and backoff belong in the application. ### What happens if the router is uncertain? The policy chooses a tested fallback, requests clarification or sends the case for review. Timeout and provider errors also need explicit paths. Keep reading - [Model Build with Jev](https://qquantum.ai/technology/jev) - [Evidence Jev Benchmarks](https://qquantum.ai/technology/jev/benchmarks) - [Guide Modelling AI Cost](https://qquantum.ai/resources/ai-cost-model)