> ## Documentation Index
> Fetch the complete documentation index at: https://docs.belvedir.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Route Your Calls

> Point your existing OpenAI-compatible or Anthropic client at Belvedir with your Belvedir key. Two lines change; each call keeps the model it already names.

Belvedir can execute your LLM calls. Point your client at Belvedir and use your Belvedir key in place of the provider key. Two lines change; the client works exactly as before.

## OpenAI-compatible clients

Set the base URL to `https://platform.belvedir.ai/api/v1/route` and the key to your `bv_live_` key.

```ts theme={null}
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://platform.belvedir.ai/api/v1/route",
  apiKey: process.env.BELVEDIR_API_KEY, // was OPENAI_API_KEY
});

const res = await client.chat.completions.create({
  model: "x-ai/grok-4.6", // unchanged: the model this call already used
  messages,
});
```

```python theme={null}
from openai import OpenAI

client = OpenAI(
    base_url="https://platform.belvedir.ai/api/v1/route",
    api_key=os.environ["BELVEDIR_API_KEY"],  # was OPENAI_API_KEY
)

res = client.chat.completions.create(
    model="anthropic/claude-sonnet-5",  # unchanged
    messages=messages,
)
```

Do this for every LLM client in your app. Keep the model each call already names and change only the base URL and key. OpenAI ids stay bare (`gpt-5.2`); every other provider's id is written provider-prefixed (`x-ai/grok-4.6`, `anthropic/claude-sonnet-5`). If a call site used a provider-native SDK (xAI, Google), swap it for the OpenAI client pointed at Belvedir and write the model in the prefixed spelling; bare ids are normalized too, so a missed one still routes. The full catalog is on [Available Models](/inference/models).

## Anthropic SDK clients

Anthropic SDK call sites do not swap to the OpenAI client. Point the Anthropic client at `https://platform.belvedir.ai/api` instead: requests pass through to the Messages API untranslated, with prompt caching, thinking, beta headers and native streaming intact. That surface is model-pinned, so routing stays with the OpenAI-shaped endpoint. Details on [Messages](/api-reference/messages).

```ts theme={null}
import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic({
  baseURL: "https://platform.belvedir.ai/api",
  apiKey: process.env.BELVEDIR_API_KEY,
});
```

## What happens

The first call naming a model creates a router anchored on it, listed on the **Routers** page as "From your code". That model is the ceiling: it answers every conversational call, so people always talk to the model your code named. With **Smart routing** on (the default), cheaper tiers take the machine-shaped tasks underneath it (classification, extraction, formatting, tool loops), so you pay less when a smaller model is enough for the busywork. Turned off, every call serves exactly the model it names.

Every project also has an auto router; `model: "auto"` hands a call to it outright. The response's `model` field tells you what actually ran, and the `x-belvedir-*` response headers say why. How routers, tiers and **Automatic updates** work is on [Model Routing](/inference/mixture-of-models).

## Billing

Belvedir runs the call and bills your organization per token at the rates on the Pricing page (**Inference → Pricing**), attributed to the calling API key. Every response carries `usage.cost`, the amount billed for that call.

## Next steps

* [Model Routing](/inference/mixture-of-models): tiers, task-group routes, **Smart routing** and **Automatic updates**.
* [Available Models](/inference/models): every model and its identifier.
* [Capabilities and Limits](/inference/capabilities): what each surface supports and every hard limit.
* [Chat completions](/api-reference/chat-completions): the full request and response contract.