## See Also

- [Getting Started](https://python.useinstructor.com/getting-started/index.md) - Quick start guide
- [from_provider Guide](https://python.useinstructor.com/concepts/from_provider/index.md) - Detailed client configuration
- [Response Models](https://python.useinstructor.com/concepts/models/index.md) - Working with Pydantic models
- [Raw Response](https://python.useinstructor.com/concepts/raw_response/index.md) - Access original LLM responses

The easiest way to get usage for non streaming requests is to access the raw response.

```python
import instructor

from pydantic import BaseModel

client = instructor.from_provider("openai/gpt-4.1-mini")


class UserExtract(BaseModel):
    name: str
    age: int


user, completion = client.create_with_completion(
    response_model=UserExtract,
    messages=[
        {"role": "user", "content": "Extract jason is 25 years old"},
    ],
)

print(completion.usage)
"""
CompletionUsage(
    completion_tokens=10,
    prompt_tokens=79,
    total_tokens=89,
    completion_tokens_details=CompletionTokensDetails(
        accepted_prediction_tokens=None,
        audio_tokens=0,
        reasoning_tokens=0,
        rejected_prediction_tokens=None,
    ),
    prompt_tokens_details=PromptTokensDetails(audio_tokens=0, cached_tokens=0),
)
"""
```

You can catch an IncompleteOutputException whenever the context length is exceeded and react accordingly, such as by trimming your prompt by the number of exceeding tokens.

```python
from instructor.core.exceptions import IncompleteOutputException
import instructor
from pydantic import BaseModel

client = instructor.from_provider("openai/gpt-4.1-mini")


class UserExtract(BaseModel):
    name: str
    age: int


try:
    client.create_with_completion(
        response_model=UserExtract,
        messages=[
            {"role": "user", "content": "Extract jason is 25 years old"},
        ],
    )
except IncompleteOutputException as e:
    token_count = e.last_completion.usage.total_tokens  # type: ignore
    # your logic here
```