Claude Platform Docs

Obter uso de tokens ao longo do tempo

beta.organization.analytics.usage_report.list(**kwargs) -> SyncPageCursor[BetaAnalyticsUsageReportTimeBucket]
GET/v1/organizations/analytics/usage_report

Obtém o uso de tokens ao longo do tempo em um intervalo de datas.

Retorna o uso de tokens agrupado por minuto, hora ou dia, opcionalmente detalhado por produto, modelo, janela de contexto, região de inferência ou velocidade. Disponível para organizações em um plano Claude Enterprise. Requer uma chave de API com o escopo read:analytics.

Parameters
starting_at: Union[str, datetime]⁠Query parameter

Start of range, inclusive. RFC 3339 tz-aware. Must be within the last 365 days and no earlier than 2026-01-01T00:00:00Z.

formatdate-time
bucket_width: Optional[Literal["1d", "1h", "1m"]]⁠Query parameter

Time bucket granularity.

default1d
One of the following:
"1d"
"1h"
"1m"
claude_tag_categories: Optional[List[BetaAnalyticsClaudeTagCategory]]⁠Query parameter

Filter to Claude Tag (Claude in Slack) usage in specific spend categories. Usage with no category never matches. dm usage is reported under the user's product rather than claude-tag, so combining this filter with products[]=claude-tag excludes it. Use group_by[]=claude_tag_category to break out per-category values.

maxItems100
One of the following:
"dm"
"engaged"
"monitoring"
"proactive"
"scheduled"
claude_tag_user_ids: Optional[Sequence[str]]⁠Query parameter

Filter to Claude Tag (Claude in Slack) usage attributed to specific Slack users, by Slack user ID (for example U0123ABCDEF), not claude.ai user ID. Usage that is not Claude Tag, and Claude Tag usage not attributed to a single user, never matches. Use group_by[]=claude_tag_user_id to break out per-user values.

maxItems100
context_windows: Optional[List[BetaAnalyticsContextWindow]]⁠Query parameter

Filter to specific context-window pricing tiers. Use group_by[]=context_window to break out per-tier values.

maxItems100
One of the following:
"0-200k"
"200k-1M"
ending_at: Optional[Union[str, datetime, null]]⁠Query parameter

End of range, exclusive. When omitted, defaults to the earlier of now and starting_at + 31 days. The range may span at most 31 days.

formatdate-time
group_by: Optional[List[Literal["claude_tag_category", "claude_tag_user_id", "context_window", 6 more]]]⁠Query parameter

Dimensions to break each time bucket out by. Defaults to no grouping (one total per bucket). Each bucket reports at most its top 100 groups; a group beyond that cap has no row in that bucket (there is no remainder row), so grouped buckets are not exhaustive when a dimension has more than 100 distinct values.

maxItems100
One of the following:
"claude_tag_category"
"claude_tag_user_id"
"context_window"
"inference_geo"
"model"
"product"
"rbac_group_id"
"slack_channel_id"
"speed"
inference_geos: Optional[List[BetaAnalyticsInferenceGeoFilter]]⁠Query parameter

Filter to specific inference regions. not_available matches rows where the region is unset. Use group_by[]=inference_geo to break out per-region values.

maxItems100
One of the following:
"global"
"not_available"
"us"
limit: Optional[int]⁠Query parameter

Maximum number of time buckets per page. Defaults and caps vary by bucket_width (1d: default 7, max 31; 1h: default 24, max 168; 1m: default 60, max 256).

minimum1
models: Optional[Sequence[str]]⁠Query parameter

Models to include. Defaults to all models. Use group_by[]=model to break out per-model values.

maxItems100
page: Optional[str]⁠Query parameter

Opaque cursor from a previous response's next_page field.

products: Optional[List[BetaAnalyticsProductFilter]]⁠Query parameter

Product surfaces to include. Defaults to all products. Use group_by[]=product to break out per-product values.

maxItems100
One of the following:
"chat"
"chat_cowork_unified"
"claude-tag"
"claude_code"
"claude_design"
"claude_in_chrome"
"cowork"
"office_agent"
rbac_group_ids: Optional[Sequence[str]]⁠Query parameter

Filter to usage attributed to specific RBAC groups. Accepts tagged RBAC group IDs (rbac_group_...) or bare group UUIDs. A row matches when the user belonged to any of the listed groups on the (UTC) day the usage occurred; usage with no group attribution never matches.

maxItems100
slack_channel_ids: Optional[Sequence[str]]⁠Query parameter

Filter to usage originating from specific Slack channels. Use group_by[]=slack_channel_id to break out per-channel values.

maxItems100
speeds: Optional[List[Literal["fast", "standard"]]]⁠Query parameter

Filter to fast or standard inference mode. Use group_by[]=speed to break out per-mode values.

maxItems100
One of the following:
"fast"
"standard"
user_ids: Optional[Sequence[str]]⁠Query parameter

Filter to specific users by tagged user ID.

maxItems100
Returns
class BetaAnalyticsUsageReportTimeBucket: ending_at; results; starting_at
Obter uso de tokens ao longo do tempo
import os
from datetime import datetime
from anthropic import Anthropic

client = Anthropic(
    api_key=os.environ.get(
        "ANTHROPIC_API_KEY"
    ),  # This is the default and can be omitted
)
page = client.beta.organization.analytics.usage_report.list(
    starting_at=datetime.fromisoformat("2019-12-27T18:11:19.117"),
)
page = page.data[0]
print(page.ending_at)
Returns Examples
Response 200
{
  "data": [
    {
      "ending_at": "2019-12-27T18:11:19.117Z",
      "results": [
        {
          "cache_creation": {
            "ephemeral_1h_input_tokens": 0,
            "ephemeral_5m_input_tokens": 0
          },
          "cache_read_input_tokens": 0,
          "claude_tag_category": "dm",
          "claude_tag_user_id": "U0123ABCDEF",
          "context_window": "0-200k",
          "inference_geo": "global",
          "model": "claude-opus-5",
          "output_tokens": 0,
          "product": "chat",
          "rbac_group_id": "rbac_group_012rppKaSVsmTo6NqRDXQXNF",
          "requests": 0,
          "server_tool_use": {
            "web_search_requests": 10
          },
          "slack_channel_id": "C0123ABCDEF",
          "speed": "fast",
          "uncached_input_tokens": 0
        }
      ],
      "starting_at": "2019-12-27T18:11:19.117Z"
    }
  ],
  "data_refreshed_at": "2019-12-27T18:11:19.117Z",
  "has_more": true,
  "next_page": "next_page",
  "organization_id": "org_013FP9SaFPBg7Kw7fetjn6cF"
}