Understanding Task Results

Learn how to interpret Task outputs, analyse execution data, and understand performance metrics.

Response Structure

Complete Response Format

After running a task, you receive a comprehensive response containing:

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{
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  "task_id": "0195d1ff-1f05-437a-95ac-6de8969cb47b",
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  "task_revision_id": "0195d1ff-1f42-f14e-8b65-641baf9dc32e",
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  "response": {
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    "sentiment": "negative",
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    "image_match": true,
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    "image_description": "The image shows a severely damaged toaster..."
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  },
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  "run_data": {
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    "submitted": {
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      "customer_review": "My toaster exploded during breakfast..."
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    },
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    "files": [
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      "be8d9e69-9f2a-4bfd-bbf4-559d6b4eb5d0.jpeg"
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    ]
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  },
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  "id": "0195d207-32bb-d03d-cfdc-f4516e9222c8",
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  "created": "2025-03-26T10:37:15.687874Z",
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  "input_tokens": 2051,
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  "output_tokens": 130,
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  "total_tokens": 2181,
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  "input_processor_timing": 0.0001468900591135025,
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  "llm_call_timing": 4.773190421052277,
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  "charged_credits": "9.00"
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}

Key Response Components

Your model output is contained in the response field, while the rest of the task response contains other useful information including performance metrics and metadata.

Performance Metrics

Token Usage Analysis

Understanding token consumption in your results:

Timing Breakdown

Each execution provides timing metrics:

Cost Tracking

Each execution shows credits consumed (charged_credits):

🧮 Cost Factors

• Model selection - higher-end models consume more credits per token
• Token count - both input and output tokens contribute to cost

⚙️ Optimisation

• Compare model performance vs. credit consumption
• Simplify prompts to minimise unnecessary tokens
• Reduce output verbosity where possible

Next Steps