What is AI model routing? Matching each request to the right model

Many AI products send every request to the same model. Rewording a sentence and reviewing the risks in an acquisition agreement both go to one flagship model, because that is the simplest thing to build. It works, but it pays for frontier capability on requests that never use it.

AI model routing is the alternative: decide, request by request, how much capability the task needs, and send it to a model that meets that bar. This article explains how routers decide, where they go wrong, and how EcoRouter approaches it.

What AI model routing means

An AI model router (often called an LLM router) sits between the person or application asking a question and a pool of language models. For each request it estimates what the task demands, then picks one model to answer it.

Routing is not load balancing or provider failover, which move traffic between copies of the same model. It changes how much capability a request gets, and it makes that choice automatically rather than asking users to pick from a menu.

The goal is not the cheapest model. It is the least capable model that can still do the job well — and recognizing the requests where that means the most capable model available.

Why one model for everything wastes compute and money

Language models differ enormously in size. The computation needed per token grows roughly in proportion to the number of parameters a model activates for that token; a common rule of thumb is about two floating-point operations per active parameter per token¹². A model with ten times the active parameters does roughly ten times the arithmetic for every token it reads and writes.

That difference shows up in price. Providers typically sell a family of models, from small, fast tiers to flagships, and the list price per token at the top of a family ranges from several times to around a hundred times the price at the bottom, depending on the provider and on whether you compare input or output tokens³.

Meanwhile, many everyday requests are routine: rewriting, formatting, extracting fields, classifying, answering a simple factual question. A much smaller model handles these well, so sending them to a flagship spends compute and money on capability the task never needed.

The reverse is also true. Multi-step reasoning, difficult math, complex code and high-stakes analysis can genuinely need the strongest model available. A router that gets these wrong saves nothing: it produces a weaker answer and pushes the cost onto the person who has to ask again.

How routers make the decision

Most routers combine some of these signals.

Task type

Rewriting and extraction sit at the light end; analysis, research, proofs and complex coding sit at the heavy end. Task type is usually inferred from the wording, the presence of code or mathematical notation, and any attached material.

Complexity, not length

Length is a tempting proxy for difficulty and a poor one. A 3,000-word email that needs a friendlier tone is easy; "prove that this series converges" is short and hard. Good routers look at reasoning language, the number of instructions and constraints, multi-step structure, domain and expected answer length, and treat raw length as one weak signal among many.

Hard requirements before preferences

If a request needs a large context window, image input or structured JSON output, models that cannot provide it should be excluded before anything else is compared. The same applies to a minimum capability level: filter out models below the bar first, then choose among the rest, so a low price can never outweigh insufficient capability.

Rules, learned routers and cascades

Routing logic comes in a few broad styles:

• Heuristic routers score the request with deterministic signals. They are fast, cheap, predictable and easy to audit, but only as good as their rules.
• Learned routers train a model to predict whether a smaller model's answer will be good enough. On their benchmarks, RouteLLM reported cutting costs by more than half in some settings without reducing response quality, and Hybrid LLM reported up to 40% fewer calls to the large model with no drop in quality⁴⁵.
• Cascades try a cheaper model first and move to a stronger one only if the answer fails a check. FrugalGPT, an early and widely cited example, reported matching the best single model it tested at a fraction of the cost on its benchmarks⁶.

These are benchmark results under each paper's own conditions, not general expectations for every workload.

The tradeoffs

• Quality risk is asymmetric. Over-routing costs a little extra; under-routing costs a bad answer. A sensible router errs upward when unsure.
• Routing must be cheap. Calling a large model to decide which model to call can erase the savings and add latency before the first word appears.
• Escalation is not free. If a cheap first attempt fails and is retried on a stronger model, the total can exceed starting strong. A high escalation rate means the router starts too low.
• Savings need a baseline. Any saving is a comparison against something, usually a fixed reference model. Who chose that reference, and when, matters as much as the arithmetic.

How EcoRouter routes a request

EcoRouter's approach is set out in its methodology. In outline:

1. Deterministic analysis. Each request is classified by task type and scored for complexity from 0 to 100 using word lists and structural signals such as code blocks and mathematical notation. No language model is called to make the routing decision, and prompt length contributes only a small, capped share of the score.
2. A hard capability floor. The analysis becomes minimum requirements: reasoning, writing, coding and math levels, context window, and a minimum tier. Financial, legal or medical subject matter combined with a request for judgment ("review this agreement and tell me what we're committing to") is floored at the advanced tier. Models below the floor are excluded before ranking, in every mode, and each exclusion is recorded with a reason.
3. "Enough" beats "most." In Eco and Balanced modes, among eligible models, the quality-fit score rises as a model clears the requirements, then stops rising once it has comfortable headroom. Past that point cost, estimated compute and speed decide, and an explicit penalty discourages choosing a tier above the one the request needs.
4. Modes. Eco favors lighter adequate models, Balanced (the default) weighs capability and efficiency together, and Max chooses the most capable eligible model, even for simple requests. Eligibility filters still apply in Max, and if the chosen model is unavailable the request falls back to a comparable one.

So "Rewrite this sentence to sound friendlier" goes to an efficient model. "Help me build a go-to-market strategy for a software startup" needs multi-step reasoning and a long answer, so it is floored at a mid-capability model. "Analyze an acquisition structure and model dilution under three financing scenarios" triggers the high-stakes rule and goes to advanced reasoning. Under each answer, EcoRouter explains the path it took and names the capability tier, not the vendor.

EcoRouter also keeps two failure paths apart. If the selected model cannot be reached, it falls back to a comparable model at the same tier or below, so an outage can never silently promote a request to a more expensive model. Moving to a stronger model is reserved for evidence that an answer was not good enough.

Routing rules are also tested before they ship: a fixed set of hand-written prompts runs through the router as a build gate, and the build fails if any case is routed below its minimum accepted tier.

Practical takeaway

Five questions to ask of any router, including one you build:

1. Does it filter on hard capability requirements before optimizing for cost?
2. Does it avoid calling an expensive model just to decide where a request goes?
3. Does it err upward when unsure, and can you see its escalation rate?
4. Is every savings figure stated against a named, fixed baseline, with its assumptions?
5. Can it explain each individual decision after the fact?

To see one in action, ask EcoRouter a question and open the receipt under the answer, or read how the EcoRouter API brings the same routing to your own product.

References

1. arXiv (Kaplan et al., OpenAI), "Scaling Laws for Neural Language Models" (2020). https://arxiv.org/abs/2001.08361 (opens in a new tab) ↩

2. Epoch AI (Josh You), "How much energy does ChatGPT use?" (7 February 2025). https://epoch.ai/gradient-updates/how-much-energy-does-chatgpt-use (opens in a new tab) ↩

3. Provider pricing pages, standard API rates, accessed 11 October 2026: OpenAI, "Pricing" (API docs). https://developers.openai.com/api/docs/pricing (opens in a new tab) ; Anthropic, "Pricing". https://claude.com/pricing (opens in a new tab) ; Google, "Gemini Developer API pricing". https://ai.google.dev/gemini-api/docs/pricing (opens in a new tab) ↩

4. arXiv (Ong et al.), "RouteLLM: Learning to Route LLMs with Preference Data" (2024). https://arxiv.org/abs/2406.18665 (opens in a new tab) ↩

5. Ding et al., "Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing", ICLR 2024. https://arxiv.org/abs/2404.14618 (opens in a new tab) ↩

6. arXiv (Chen, Zaharia and Zou), "FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance" (2023). https://arxiv.org/abs/2305.05176 (opens in a new tab) ↩