{
  "dataset": "batchwatch batch API queue-time measurements",
  "window": "30d",
  "generated_at": "2026-10-10T13:18:22.280Z",
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "attribution": "batchwatch — CC BY 4.0",
  "source_url": "https://batchwatch.dev/data.csv",
  "schema": {
    "provider": "Provider slug, e.g. \"openai\".",
    "provider_name": "Human-readable provider name.",
    "model": "Model id, e.g. \"gpt-4o\".",
    "mode": "Measurement mode: \"batch\" or \"sync\".",
    "window": "Rolling window the figures cover (always \"30d\").",
    "n": "Number of completed jobs behind the figures (sample size).",
    "contributors": "Distinct independent third-party sources behind the figures.",
    "p50_s": "Median queue time in seconds (null when not yet established).",
    "p90_s": "Ninetieth-percentile queue time in seconds.",
    "p95_s": "Ninety-fifth-percentile queue time in seconds.",
    "max_s": "Slowest completion observed in the window, in seconds.",
    "newest_at": "Unix seconds of the newest measurement in the window.",
    "oldest_at": "Unix seconds of the oldest measurement in the window.",
    "computed_at": "Unix seconds the rollup was computed.",
    "requested_service_tier": "Pricing tier the figures are for: \"standard\" (batch and full-price sync) or \"flex\" (the half-price synchronous tier). Its own series, never pooled with standard. Flex latency is measured on a fixed 8-input-token probe, the same fixed prompt size our batch probes use. A flex call is synchronous, so the figure is the model's own work and it grows with the prompt: read it as the floor a larger prompt starts from. A batch figure is dominated by queue time -- the wait to be scheduled -- so the same extra work is a much smaller part of it. The refusal rate is unaffected by prompt size: admission control happens before inference, so this probe measures it exactly as well as a large one would."
  },
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  ],
  "count": 10,
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