One Model per Task: A Practical Routing Walkthrough

A single model is convenient when you start building an AI feature. As the workload grows, that convenience can become a poor default: extracting a date, drafting an explanation, and reviewing a complex change do not require the same approach.

The alternative is task-based model routing. Give each task a clear contract, choose a model that meets it, and reserve more expensive processing for cases that justify it. This walkthrough shows how to introduce that pattern without building a complicated orchestration system.

§ Step 1: Split the workflow into distinct tasks

Start with the work your application actually performs, not a list of available models. For a support assistant, the workflow might include classifying an incoming message, retrieving relevant documentation, and drafting a response.

Write down the input and expected output for each model-assisted task:

• Classification: a customer message becomes one category from a fixed list.
• Extraction: a message becomes structured fields, with missing values explicitly marked.
• Drafting: supplied evidence becomes a customer-facing answer.

Keep deterministic operations outside the model. Looking up an account, checking a required field, or comparing two numbers usually belongs in application code.

§ Step 2: Define what passing means

Before choosing models, establish acceptance criteria. A classifier should return an allowed label. An extractor should preserve values accurately. A drafting task should use the supplied evidence and avoid inventing policy details.

Collect a small evaluation set from representative inputs, removing sensitive information where necessary. Include straightforward cases, ambiguous requests, missing context, and unusually long messages. Record an expected result or a review rubric for each example.

This creates a useful question: which candidate meets the task’s requirements at an acceptable cost and latency? That is more actionable than asking which model is best overall.

§ Step 3: Choose a candidate for each role

Begin with a small configuration. For example, evaluate Gemini 2.5 Flash for classification and extraction, and Claude Sonnet 5.5 for evidence-based drafting. Treat these as starting hypotheses, not guarantees about quality, speed, or price.

Run both candidates against your examples where useful. If one model meets every task’s requirements economically, keep it. Task-based routing is a way to make deliberate decisions, not a requirement to use different models everywhere.

Vana API lets you access multiple providers through one OpenAI-compatible endpoint. It also supports Anthropic Messages and Gemini native protocols, so you can retain a suitable client interface while testing different model choices.

§ Step 4: Implement explicit routing rules

Put model selection in application configuration rather than scattering it throughout prompts. Map stable task names, such as classify_ticket and draft_reply, to the model identifiers available in your Vana API account.

At request time, select the task first, then load its model, prompt template, output requirements, and token limit. Verify which request parameters and structured-output features the selected model supports; a shared interface does not make every capability identical.

Start with rules you can explain. A fixed-label classification request goes to the classification route. A response requiring synthesis across several documents goes to the drafting route. Avoid asking another model to make every routing decision before simple rules have proved insufficient.

§ Step 5: Add validation and bounded escalation

Validate outputs before using them. Check that labels belong to the allowed set, required fields exist, and generated references match the supplied material.

When validation fails, use a bounded recovery path: retry once with specific feedback, escalate to a separately evaluated model, or request human review. Do not silently repeat requests until something looks plausible.

For consequential actions, such as changing billing details, keep authorization and confirmation in application logic regardless of which model produced the recommendation.

§ Step 6: Measure and revise

Log the task, selected model, prompt version, token usage, latency, validation result, and escalation outcome. Compare the total cost per accepted result, including retries, rather than judging only the first request.

Vana API bills per real token with no monthly fee, making measured usage relevant to this comparison. Create an account to test a small routing setup against your own evaluation set.

Review the results regularly. Keep routes that deliver reliable outputs, simplify those that add little value, and retest changes before deployment. The goal is not more models. It is a clear, measurable fit between each task and the model handling it.