{
  "base_url": "http://127.0.0.1:39125/v1",
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    "max_completion_tokens": 9216,
    "reasoning_headroom_tokens": 8192,
    "visible_answer_tokens": 1024
  },
  "checks": [
    "smoke",
    "json",
    "tools",
    "streaming-tools",
    "tool-result",
    "image"
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      "stop",
      "tool_calls"
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    "errors": [],
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  "observations": [
    {
      "content": "```python\nsum(xs)\n```\n\nThat's it \u2014 Python's built-in `sum()` does exactly this. For example:\n\n```python\nxs = [1, 2, 3, 4]\ntotal = sum(xs)  # 10\n```\n\nIf you want it as a reusable one-line function:\n\n```python\ndef total(xs): return sum(xs)\n```\n\nOr, if you're avoiding built-ins for some reason (e.g., an exercise):\n\n```python\nfunctools.reduce(lambda acc, x: acc + x, xs, 0)\n```",
      "content_chars": 375,
      "content_excerpt": "```python\nsum(xs)\n```\n\nThat's it \u2014 Python's built-in `sum()` does exactly this. For example:\n\n```python\nxs = [1, 2, 3, 4]\ntotal = sum(xs)  # 10\n```\n\nIf you want it as a reusable one-line function:\n\n``",
      "finish_reason": "stop",
      "passed": true,
      "reasoning_chars": 600,
      "reasoning_excerpt": "The user wants a Python one-liner that returns the sum of a list `xs`. This is a very simple request. The most idiomatic way is:\n\n```python\nsum(xs)\n```\n\nThat's it. But maybe they want it as a lambda o",
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        "prompt_tokens_details": null,
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      },
      "validation_detail": "contains sum("
    },
    {
      "content": "{\"language\":\"python\",\"ok\":true}",
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      "content_excerpt": "{\"language\":\"python\",\"ok\":true}",
      "finish_reason": "stop",
      "passed": true,
      "reasoning_chars": 183,
      "reasoning_excerpt": "The user wants me to return ONLY a JSON object: {\"language\":\"python\",\"ok\":true}. No prose. This is a simple, direct request. I should just output the JSON object exactly as specified.",
      "reasoning_field": "reasoning",
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      "validation_detail": "parsed keys=['language', 'ok']"
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      "content": "",
      "content_chars": 0,
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      "finish_reason": "tool_calls",
      "passed": true,
      "reasoning_chars": 81,
      "reasoning_excerpt": "The user wants the weather in Oakland. I have a get_weather tool. Let me call it.",
      "reasoning_field": "reasoning",
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      "validation_detail": "valid tool_call get_weather(city='Oakland')"
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      "finish_reason": "tool_calls",
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      "validation_detail": "valid tool_call get_weather(city='Oakland')"
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      "finish_reason": "tool_calls",
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      "reasoning_excerpt": "The user wants the weather in Oakland. I have a get_weather tool. Let me call it.",
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      "validation_detail": "valid tool_call get_weather(city='Oakland')"
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      "finish_reason": "tool_calls",
      "passed": true,
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      "reasoning_excerpt": "The user wants the weather in Oakland. I have a get_weather tool. Let me call it.",
      "reasoning_field": "reasoning",
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      "finish_reason": "tool_calls",
      "passed": true,
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      "reasoning_excerpt": "The user wants to know the weather in Oakland. I have a get_weather tool available. Let me call it with the city \"Oakland\".",
      "reasoning_field": "stream_delta",
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      "sse_done": true,
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      },
      "validation_detail": "valid tool_call get_weather(city='Oakland')"
    },
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      "finish_reason": "tool_calls",
      "passed": true,
      "reasoning_chars": 123,
      "reasoning_excerpt": "The user wants to know the weather in Oakland. I have a get_weather tool available. Let me call it with the city \"Oakland\".",
      "reasoning_field": "reasoning",
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      },
      "validation_detail": "valid tool_call get_weather(city='Oakland')"
    },
    {
      "content": "OAKLAND 72F",
      "content_chars": 11,
      "content_excerpt": "OAKLAND 72F",
      "finish_reason": "stop",
      "passed": true,
      "reasoning_chars": 151,
      "reasoning_excerpt": "The user wants me to reply exactly \"OAKLAND 72F\" using the tool result. The tool result showed Oakland at 72\u00b0F, sunny. They want an exact reply format.",
      "reasoning_field": "reasoning",
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      "test": "tool-result-continuation",
      "usage": {
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      },
      "validation_detail": "tool result retained"
    },
    {
      "content": "# Image Description\n\nThis image shows a **status display screen** with a dark navy background and three lines of text in different colors:\n\n## Key Information\n\n| Element | Value | Color |\n|---------|-------|-------|\n| **Status** | ANVIL READY | Green |\n| **Code** | 42917 | White |\n| **Temperature** | 42 \u00b0C | Light blue |\n\n## Interpretation\n\nThis appears to be a **machine or equipment status readout**, likely from an industrial system, forge, or monitoring interface:\n\n- **\"ANVIL READY\"** (in green) \u2014 indicates the equipment/anvil is operational and in a ready state. The green color typically signals a normal/positive status.\n- **\"CODE 42917\"** (in white) \u2014 likely an identification number, batch code, job reference, or system code.\n- **\"TEMPERATURE 42 C\"** (in light blue) \u2014 a current temperature reading of 42\u00b0C (approximately 108\u00b0F), which is warm but not extreme for industrial equipment.\n\nThe clean, high-contrast design suggests this is a digital dashboard or HMI (Human-Machine Interface) display meant to be read at a glance.",
      "content_chars": 1040,
      "content_excerpt": "# Image Description\n\nThis image shows a **status display screen** with a dark navy background and three lines of text in different colors:\n\n## Key Information\n\n| Element | Value | Color |\n|---------|-",
      "expected": [
        "42917"
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      "finish_reason": "stop",
      "image": {
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        "mime": "image/png",
        "sha256": "a8d5158019fb2b08ee9f6a153860ab2342384644f7c53ab58fdd45658124e83e"
      },
      "passed": true,
      "reasoning_chars": 658,
      "reasoning_excerpt": "The user wants me to inspect this image and describe what it shows, reporting the most important labels, values, and status.\n\nThe image shows:\n- A dark navy/dark blue background\n- Green text at top: \"",
      "reasoning_field": "reasoning",
      "reasoning_tokens": null,
      "seconds": 10.854,
      "test": "image",
      "usage": {
        "completion_tokens": 403,
        "prompt_tokens": 333,
        "prompt_tokens_details": null,
        "total_tokens": 736
      },
      "validation_detail": "all expected text present"
    }
  ],
  "ocr_expect": [],
  "passed": true,
  "results": [
    {
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      "name": "smoke (short coding)",
      "passed": true
    },
    {
      "detail": "parsed keys=['language', 'ok'] finish='stop' visible=31 reasoning_chars=183 reasoning_tokens=None",
      "name": "structured JSON",
      "passed": true
    },
    {
      "detail": "4/4 clean (sample: 6.8s valid tool_call get_weather(city='Oakland') finish='tool_calls' visible=0 reasoning_chars=81 reasoning_tokens=None)",
      "name": "shared-prefix tool batch x4",
      "passed": true
    },
    {
      "detail": "6.1s done=True events=37 valid tool_call get_weather(city='Oakland') finish='tool_calls'",
      "name": "streaming tool call",
      "passed": true
    },
    {
      "detail": "1.5s tool result retained finish='stop' visible=11 reasoning_chars=151 reasoning_tokens=None",
      "name": "tool-result continuation",
      "passed": true
    },
    {
      "detail": "10.9s all expected text present finish='stop' visible=1040 reasoning_chars=658 reasoning_tokens=None",
      "name": "general image understanding",
      "passed": true
    }
  ],
  "schema_version": "preflight/v2",
  "thinking": {
    "chat_template_kwargs": null,
    "mode": "default",
    "reasoning_effort": "max"
  },
  "video_expect": [],
  "video_input": null
}
