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

# Smart Routing

> Automatically route prompts to the right model based on complexity

Smart Routing selects the best model for each prompt automatically. Simple questions go to a fast, cost-efficient model; complex or multi-step tasks go to a more capable one. You use the same OpenAI-compatible API. Just change the model name.

## Models

| Model              | What it does                                                         |
| ------------------ | -------------------------------------------------------------------- |
| `lyceum/router`    | Classifies each prompt and routes to the optimal model automatically |
| `lyceum/simple`    | Always routes to a fast, cost-efficient model                        |
| `lyceum/complex`   | Always routes to a high-capability model                             |
| `lyceum/reasoning` | Always routes to a reasoning model                                   |

Use `lyceum/router` when you want automatic cost/quality optimisation. Use the others when you want explicit control over which tier handles your requests.

## Usage

The API is identical to any other [serverless inference](/docs/inference/serverless) call. Only the model name changes.

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

client = OpenAI(
    api_key="lk_...",
    base_url="https://api.lyceum.technology/api/v2/external",
)

resp = client.chat.completions.create(
    model="lyceum/router",
    messages=[{"role": "user", "content": "What is the capital of France?"}],
)
print(resp.choices[0].message.content)
```

Streaming works the same way:

```python theme={null}
stream = client.chat.completions.create(
    model="lyceum/router",
    messages=[{"role": "user", "content": "Explain gradient descent step by step."}],
    stream=True,
)
for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="", flush=True)
```

## Score endpoint

To inspect the complexity score for a prompt without triggering a full inference request, use the score endpoint directly. It returns a value between 0 and 1. Lower means simpler, higher means more complex.

```python theme={null}
import httpx

response = httpx.post(
    "https://api.lyceum.technology/api/v2/external/serverless/route",
    headers={"Authorization": "Bearer lk_..."},
    json={"input": "What is the capital of France?"},
)
print(response.json())  # {"score": 0.12}
```

```bash theme={null}
curl https://api.lyceum.technology/api/v2/external/serverless/route \
  -H "Authorization: Bearer lk_..." \
  -H "Content-Type: application/json" \
  -d '{"input": "What is the capital of France?"}'
```

This is useful for understanding how the router classifies your prompts, or for building custom routing logic on top of the score.

## Billing

Requests are billed at the rate of whichever model the router selects. `lyceum/simple`, `lyceum/complex`, and `lyceum/reasoning` always route to the same tier, so their cost is predictable.

<Card title="Serverless Inference" icon="cloud" href="/docs/inference/serverless">
  Learn more about pay-per-request inference.
</Card>