{
  "title": "AccessRoute HK service budgets and timing evidence",
  "basis": "Technical report section 23, tables 2–6; historical list-price snapshot checked 2026-10-11. Token counts are workload budgets, not billed usage.",
  "currency": "USD",
  "price_date": "2026-10-11",
  "models": [
    {
      "id": "T",
      "name": "Qwen3.8-Max",
      "input_per_million": 2.0,
      "output_per_million": 6.0
    },
    {
      "id": "V",
      "name": "Qwen3-VL-Plus",
      "input_per_million": 0.2,
      "output_per_million": 1.6
    }
  ],
  "calls": [
    {
      "id": "preferences",
      "name": "Preference parsing",
      "model": "T",
      "input": 1200,
      "output": 180,
      "usd": 0.00348
    },
    {
      "id": "landmark_observation",
      "name": "Landmark observation",
      "model": "V",
      "input": 2400,
      "output": 320,
      "usd": 0.000992
    },
    {
      "id": "obstacle_observation",
      "name": "Obstacle observation",
      "model": "V",
      "input": 2600,
      "output": 380,
      "usd": 0.001128
    },
    {
      "id": "landmark_matching",
      "name": "Landmark evidence matching",
      "model": "T",
      "input": 1800,
      "output": 240,
      "usd": 0.00504
    },
    {
      "id": "obstacle_matching",
      "name": "Obstacle evidence matching",
      "model": "T",
      "input": 2200,
      "output": 260,
      "usd": 0.00596
    },
    {
      "id": "guidance",
      "name": "Cantonese guidance",
      "model": "T",
      "input": 450,
      "output": 70,
      "usd": 0.00132
    }
  ],
  "workflows": [
    {
      "id": "preferences",
      "name": "Understand preferences",
      "composition": [
        "preferences"
      ]
    },
    {
      "id": "localization",
      "name": "Locate from a landmark photo",
      "composition": [
        "landmark_observation",
        "landmark_matching"
      ]
    },
    {
      "id": "obstacle",
      "name": "Match an obstacle photo",
      "composition": [
        "obstacle_observation",
        "obstacle_matching"
      ]
    },
    {
      "id": "guidance",
      "name": "Write one guidance sentence",
      "composition": [
        "guidance"
      ]
    }
  ],
  "journeys": [
    {
      "name": "Ordinary journey",
      "preferences": 1,
      "guidance": 13,
      "localization": 0,
      "obstacle": 0,
      "calls": 14,
      "input": 7050,
      "output": 1090,
      "usd": 0.02064
    },
    {
      "name": "With location assistance",
      "preferences": 1,
      "guidance": 13,
      "localization": 1,
      "obstacle": 0,
      "calls": 16,
      "input": 11250,
      "output": 1650,
      "usd": 0.026672
    },
    {
      "name": "With both photo workflows",
      "preferences": 1,
      "guidance": 15,
      "localization": 1,
      "obstacle": 1,
      "calls": 20,
      "input": 16950,
      "output": 2430,
      "usd": 0.0364
    }
  ],
  "timing": [
    {
      "item": "Text API connectivity sample",
      "value": "About 3.4 s",
      "kind": "Observed sample",
      "scope": "One successful solar-energy text request, HTTP 200; live integration note dated 2026-10-04. This request differs from the navigation workflows."
    },
    {
      "item": "Model request timeout",
      "value": "60 s / call",
      "kind": "Configuration",
      "scope": "The provider aborts an individual model request after 60,000 ms. This limit describes a request deadline."
    },
    {
      "item": "Navigation workflow latency",
      "value": "Median / p95 pending",
      "kind": "Measurement pending",
      "scope": "The saved integration note records successful workflow outcomes without a latency distribution. Photo workflows contain two sequential model calls plus local processing."
    }
  ],
  "component_targets": [
    {
      "metric": "Preference field accuracy",
      "criterion": "Each requested field matches its reference value.",
      "target": "97.5%"
    },
    {
      "metric": "Complete-request accuracy",
      "criterion": "All requested fields and retained profile values match.",
      "target": "94.0%"
    },
    {
      "metric": "Numeric-condition accuracy",
      "criterion": "Slope, width, units, and normalized values match.",
      "target": "99.0%"
    },
    {
      "metric": "Landmark-name accuracy",
      "criterion": "The photo observation identifies the annotated name.",
      "target": "94.0%"
    },
    {
      "metric": "Shop-identifier accuracy",
      "criterion": "The normalized shop identifier matches exactly.",
      "target": "95.0%"
    },
    {
      "metric": "Visible-floor accuracy",
      "criterion": "The observation matches the annotated visible floor.",
      "target": "96.0%"
    },
    {
      "metric": "Obstacle-presence accuracy",
      "criterion": "The predicted presence or absence matches the image label.",
      "target": "95.0%"
    },
    {
      "metric": "Obstacle-type accuracy",
      "criterion": "The predicted category matches the reference category.",
      "target": "93.0%"
    },
    {
      "metric": "Location top-1 accuracy",
      "criterion": "The first candidate matches the reference public anchor.",
      "target": "92.0%"
    },
    {
      "metric": "Location top-3 accuracy",
      "criterion": "The first three candidates include the reference anchor.",
      "target": "98.0%"
    },
    {
      "metric": "Obstacle-target accuracy",
      "criterion": "The first candidate identifies the correct facility or passage.",
      "target": "95.0%"
    },
    {
      "metric": "Structured-output accuracy",
      "criterion": "The response satisfies the task schema on the first call.",
      "target": "99.5%"
    },
    {
      "metric": "Guidance fact accuracy",
      "criterion": "Action, facility, distance, and floors match the route facts.",
      "target": "99.0%"
    },
    {
      "metric": "Cantonese expression accuracy",
      "criterion": "A fluent speaker accepts grammar, wording, and action clarity.",
      "target": "96.0%"
    }
  ],
  "workflow_targets": [
    {
      "metric": "Language-to-route accuracy",
      "criterion": "Confirmed fields match the reference and the route satisfies them.",
      "target": "92.0%"
    },
    {
      "metric": "Photo-to-position accuracy",
      "criterion": "Confirmation selects the reference scene, floor, and public node.",
      "target": "90.0%"
    },
    {
      "metric": "Obstacle-to-reroute accuracy",
      "criterion": "Confirmation closes the correct object and the route avoids it.",
      "target": "91.0%"
    },
    {
      "metric": "Lift-required journey accuracy",
      "criterion": "Every floor change uses an eligible lift throughout the journey.",
      "target": "99.0%"
    },
    {
      "metric": "Sheltered-route accuracy",
      "criterion": "The route meets the active shelter objective and access conditions.",
      "target": "98.0%"
    },
    {
      "metric": "Guidance-to-step accuracy",
      "criterion": "Every instruction matches its active segment after progress or rerouting.",
      "target": "98.0%"
    }
  ]
}