beta_usage.py — anthropic-sdk-python Source File

Source Code

# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.

from typing import Optional
from typing_extensions import Literal

from ..._models import BaseModel
from .beta_cache_creation import BetaCacheCreation
from .beta_iterations_usage import BetaIterationsUsage
from .beta_server_tool_usage import BetaServerToolUsage

__all__ = ["BetaUsage"]


class BetaUsage(BaseModel):
    cache_creation: Optional[BetaCacheCreation] = None
    """Breakdown of cached tokens by TTL"""

    cache_creation_input_tokens: Optional[int] = None
    """The number of input tokens used to create the cache entry."""

    cache_read_input_tokens: Optional[int] = None
    """The number of input tokens read from the cache."""

    inference_geo: Optional[str] = None
    """The geographic region where inference was performed for this request."""

    input_tokens: int
    """The number of input tokens which were used."""

    iterations: Optional[BetaIterationsUsage] = None
    """Per-iteration token usage breakdown.

    Each entry represents one sampling iteration, with its own input/output token
    counts and cache statistics. This allows you to:

    - Determine which iterations exceeded long context thresholds (>=200k tokens)
    - Calculate the true context window size from the last iteration
    - Understand token accumulation across server-side tool use loops
    """

    output_tokens: int
    """The number of output tokens which were used."""

    server_tool_use: Optional[BetaServerToolUsage] = None
    """The number of server tool requests."""

    service_tier: Optional[Literal["standard", "priority", "batch"]] = None
    """If the request used the priority, standard, or batch tier."""

    speed: Optional[Literal["standard", "fast"]] = None
    """The inference speed mode used for this request."""