Chandler · usage
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Prompt

Total dollar amount processed through the Pay feature year to date 2026

Outcome
success
Cost
$1.3908
excludes embeddings
Latency
87.7 s
Query attempts
2
Rows returned
1
Tokens
1,568,354 / 3,525
in / out

Request

request_id
1e2d69ec-e6a1-4cef-9d48-1ba7c7e6b2ce
When
2026-09-08 07:01
Tool
ask
User
yordan@dext.com · Yordan Todorov Ivanov
Identity
google
Session
ed232386-48ed-4577-964f-71a2fde0a871
Model
gpt-5.6-sol

Route taken

Topic
Product Usage - Accounts
Omni model
e407bba6-4fae-4079-ac4e-cd6cf8fdf6f8
Match method
agent loop
Nudge shown
feedback_nudge
Question shape
not classified
Caveat raised
—
Sharper question offered
—
Shown to user
How did I do with this report?

Per-step detail — each LLM call, each Omni request, each rejected draft — is traced to stdout only and is not stored, so it cannot be shown here. Query attempts counts run_query executions, not model turns.

SQL that ran

SELECT
  COUNT(DISTINCT omni_dbt__eds_all_accounts."DERIVED_ACCOUNT_ID") AS "Accounts with Payments Access in 2026 YTD"
FROM PROD."EDS_ALL_ACCOUNTS" AS omni_dbt__eds_all_accounts
INNER JOIN PROD."EDS_CALENDAR_DAY" AS omni_dbt__eds_calendar_day
  ON omni_dbt__eds_calendar_day."DATE_DAY" >= omni_dbt__eds_all_accounts."VALID_FROM" AND omni_dbt__eds_calendar_day."DATE_DAY" <= omni_dbt__eds_all_accounts."VALID_TO"
WHERE omni_dbt__eds_calendar_day."DATE_DAY" >= DATE_FROM_PARTS(2026, 1, 1)
  AND omni_dbt__eds_calendar_day."DATE_DAY" < DATE_FROM_PARTS(2026, 9, 9)
  AND omni_dbt__eds_all_accounts."HAS_PAYMENTS_ACCESS" = TRUE
  AND (omni_dbt__eds_all_accounts."IS_DEXT_DEMO" = FALSE OR omni_dbt__eds_all_accounts."IS_DEXT_DEMO" IS NULL)
  AND omni_dbt__eds_all_accounts."IS_SUSPENDED_DEXT" = FALSE
  AND omni_dbt__eds_all_accounts."SALESFORCE_ACCOUNT_TYPE" <> 'Reseller'
LIMIT 10

Feedback

No feedback linked to this request.

Cost is the LLM completion spend LiteLLM priced for each call, summed per request. Chandler is running on the Codex CLI, which draws ChatGPT plan quota rather than per-token API billing, so this figure is not money paid — it is what the same traffic would have cost on the API, priced from the token counts Codex reports. Read it as the size of the bill avoided. Two known limits: embedding spend is not recorded, so retrieval and matching cost is missing, and because the figure is one sum per request it cannot be split by model within a request — a request's classifier and agent calls can use different models while llm_model holds only one name. Only authenticated calls are logged, USAGE_LOG_ENABLED can switch logging off, and log writes are fail-soft — this is not a complete record of traffic. Questions are grouped into sessions: a session is exact when the caller echoed its id back to us and otherwise inferred from a 30-minute gap in that user’s activity, so a grouping is only as good as the source shown on the session itself. A call with no attributable user gets no session at all; those questions are listed separately rather than dropped.