Claude and GPT in the same support inbox

A support inbox is a useful place to understand how Claude and GPT can work in the same product. One incoming message might need a short category label. Another might need a careful draft based on a refund policy. Your application can send these tasks to different models without making customers choose between them.

This is called model routing: your software decides which model receives each request. You do not need a complex system to begin. A few explicit rules, a small set of test messages, and clear boundaries are enough for a first version.

§ Start with two jobs, not two personalities

Suppose your product needs to classify incoming messages and draft replies. Treat those as separate jobs. Classification should return a predictable label, such as billing or account access. Drafting should produce readable text grounded in the information you provide.

You could test Claude Sonnet 5.5 and GPT-6.1 Sol on both jobs. Do not assume either is automatically better at a particular task. Compare their results on examples from your own product, then assign each job based on accuracy, response time, and cost.

The useful distinction is between tasks, not brands. A model that writes a pleasant reply may still miss an important policy exception. A model that classifies reliably may need different instructions when writing for customers.

§ Keep one application interface

Your application needs a way to send instructions and receive answers. An API is that connection. An SDK is a library that handles much of the request code for you.

Vana API provides one OpenAI-compatible endpoint for accessing models from different providers. For a product built with an OpenAI SDK, this can keep the integration in one place while your application chooses a model for each task. Vana API also supports Anthropic Messages and Gemini native protocols, which is useful when an existing integration expects those request formats.

A shared endpoint does not make every model identical. Supported options, tool behavior, and output details can differ. Keep model-specific settings in a small adapter layer rather than scattering them throughout your application.

§ Make context explicit

Models do not share a conversation simply because they are connected to the same product. Your application must pass the information each task needs.

For classification, that might be the latest customer message and a list of allowed categories. For drafting, it might include the relevant conversation, the applicable policy, and the desired tone. Avoid sending the entire customer record when a few facts will do.

• Store conversation history in your application.
• Maintain one authoritative version of product policies.
• Send each model only the context needed for its assigned task.
• Check outputs before using them to trigger actions.

This makes switching models easier. It also reduces the chance that one step relies on information another step never received.

§ Choose simple routing and cautious fallbacks

Begin with a fixed rule: one model handles classification, and the other handles drafting. You can add more routing rules later if testing shows a clear benefit.

A fallback is a second attempt when the first request fails. It can help with temporary errors, but it should not silently bypass your safeguards. If a draft cannot be checked against the refund policy, switching models does not solve the missing evidence. Send that case to a person instead.

For actions such as issuing refunds, keep approval and execution separate from text generation. Neither a fluent answer nor a successful API response is authorization.

§ Measure before expanding

Record the chosen model, response time, token usage, and whether the result passed your checks. Review a small sample regularly. Look for incorrect labels, unsupported promises, and unnecessary retries.

Vana API bills per actual token usage with no monthly fee. That makes task-level usage worth tracking: sending the same conversation to both models on every turn can add cost without improving the result.

When you are ready to test this setup, create a Vana API account and start with a small, representative set of messages. Keep the routing simple until your measurements give you a concrete reason to change it.