{
  "description": "Expected share of each activity's knobits an AI model masters. source 'operators': operators' own agents in their work settings, self-reported, at least 5 operators; source 'benchmark': Skilirium's benchmark runs.",
  "benchmark_exported": "2026-10-29",
  "operators_exported": "2026-10-29T16:37:25.877Z",
  "min_operators": 5,
  "figures": [
    {
      "model": "gemini-4-argon",
      "competence": "data-modelling",
      "activity": "Capture data requirements",
      "learning_outcome": "LO-1",
      "expected_share_mastered": 0.878,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-4-argon",
      "competence": "data-modelling",
      "activity": "Define business terms",
      "learning_outcome": "LO-2",
      "expected_share_mastered": 0.88,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-4-argon",
      "competence": "data-modelling",
      "activity": "Document data rules as SBVR fact types and rule statements",
      "learning_outcome": "LO-3",
      "expected_share_mastered": 0.974,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-4-argon",
      "competence": "data-modelling",
      "activity": "Build the conceptual model and taxonomies",
      "learning_outcome": "LO-4",
      "expected_share_mastered": 0.999,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-4-argon",
      "competence": "data-modelling",
      "activity": "Derive the logical data model",
      "learning_outcome": "LO-5",
      "expected_share_mastered": 0.946,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-4-argon",
      "competence": "data-modelling",
      "activity": "Design the physical data model",
      "learning_outcome": "LO-6",
      "expected_share_mastered": 0.984,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-4-argon",
      "competence": "data-modelling",
      "activity": "Derive data quality rules",
      "learning_outcome": "LO-7",
      "expected_share_mastered": 0.948,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-4-argon",
      "competence": "data-modelling",
      "activity": "Generate and review DDL",
      "learning_outcome": "LO-8",
      "expected_share_mastered": 0.819,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-3.1-pro-preview",
      "competence": "data-modelling",
      "activity": "Capture data requirements",
      "learning_outcome": "LO-1",
      "expected_share_mastered": 0.52,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-3.1-pro-preview",
      "competence": "data-modelling",
      "activity": "Define business terms",
      "learning_outcome": "LO-2",
      "expected_share_mastered": 0.809,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-3.1-pro-preview",
      "competence": "data-modelling",
      "activity": "Document data rules as SBVR fact types and rule statements",
      "learning_outcome": "LO-3",
      "expected_share_mastered": 0.855,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-3.1-pro-preview",
      "competence": "data-modelling",
      "activity": "Build the conceptual model and taxonomies",
      "learning_outcome": "LO-4",
      "expected_share_mastered": 0.999,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-3.1-pro-preview",
      "competence": "data-modelling",
      "activity": "Derive the logical data model",
      "learning_outcome": "LO-5",
      "expected_share_mastered": 0.936,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-3.1-pro-preview",
      "competence": "data-modelling",
      "activity": "Design the physical data model",
      "learning_outcome": "LO-6",
      "expected_share_mastered": 0.844,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-3.1-pro-preview",
      "competence": "data-modelling",
      "activity": "Derive data quality rules",
      "learning_outcome": "LO-7",
      "expected_share_mastered": 0.859,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-3.1-pro-preview",
      "competence": "data-modelling",
      "activity": "Generate and review DDL",
      "learning_outcome": "LO-8",
      "expected_share_mastered": 0.613,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gpt-6-astra",
      "competence": "data-modelling",
      "activity": "Capture data requirements",
      "learning_outcome": "LO-1",
      "expected_share_mastered": 0.78,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gpt-6-astra",
      "competence": "data-modelling",
      "activity": "Define business terms",
      "learning_outcome": "LO-2",
      "expected_share_mastered": 0.936,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gpt-6-astra",
      "competence": "data-modelling",
      "activity": "Document data rules as SBVR fact types and rule statements",
      "learning_outcome": "LO-3",
      "expected_share_mastered": 0.937,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gpt-6-astra",
      "competence": "data-modelling",
      "activity": "Build the conceptual model and taxonomies",
      "learning_outcome": "LO-4",
      "expected_share_mastered": 0.874,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gpt-6-astra",
      "competence": "data-modelling",
      "activity": "Derive the logical data model",
      "learning_outcome": "LO-5",
      "expected_share_mastered": 0.906,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gpt-6-astra",
      "competence": "data-modelling",
      "activity": "Design the physical data model",
      "learning_outcome": "LO-6",
      "expected_share_mastered": 0.97,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gpt-6-astra",
      "competence": "data-modelling",
      "activity": "Derive data quality rules",
      "learning_outcome": "LO-7",
      "expected_share_mastered": 0.983,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gpt-6-astra",
      "competence": "data-modelling",
      "activity": "Generate and review DDL",
      "learning_outcome": "LO-8",
      "expected_share_mastered": 0.813,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "claude-fable-5-1",
      "competence": "data-modelling",
      "activity": "Capture data requirements",
      "learning_outcome": "LO-1",
      "expected_share_mastered": 0.777,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "claude-fable-5-1",
      "competence": "data-modelling",
      "activity": "Define business terms",
      "learning_outcome": "LO-2",
      "expected_share_mastered": 0.992,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "claude-fable-5-1",
      "competence": "data-modelling",
      "activity": "Document data rules as SBVR fact types and rule statements",
      "learning_outcome": "LO-3",
      "expected_share_mastered": 0.976,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "claude-fable-5-1",
      "competence": "data-modelling",
      "activity": "Build the conceptual model and taxonomies",
      "learning_outcome": "LO-4",
      "expected_share_mastered": 0.999,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "claude-fable-5-1",
      "competence": "data-modelling",
      "activity": "Derive the logical data model",
      "learning_outcome": "LO-5",
      "expected_share_mastered": 0.945,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "claude-fable-5-1",
      "competence": "data-modelling",
      "activity": "Design the physical data model",
      "learning_outcome": "LO-6",
      "expected_share_mastered": 0.977,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "claude-fable-5-1",
      "competence": "data-modelling",
      "activity": "Derive data quality rules",
      "learning_outcome": "LO-7",
      "expected_share_mastered": 0.948,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "claude-fable-5-1",
      "competence": "data-modelling",
      "activity": "Generate and review DDL",
      "learning_outcome": "LO-8",
      "expected_share_mastered": 0.732,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-3.8-flash",
      "competence": "data-modelling",
      "activity": "Capture data requirements",
      "learning_outcome": "LO-1",
      "expected_share_mastered": 0.668,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-3.8-flash",
      "competence": "data-modelling",
      "activity": "Define business terms",
      "learning_outcome": "LO-2",
      "expected_share_mastered": 0.942,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-3.8-flash",
      "competence": "data-modelling",
      "activity": "Document data rules as SBVR fact types and rule statements",
      "learning_outcome": "LO-3",
      "expected_share_mastered": 0.933,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-3.8-flash",
      "competence": "data-modelling",
      "activity": "Build the conceptual model and taxonomies",
      "learning_outcome": "LO-4",
      "expected_share_mastered": 0.999,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-3.8-flash",
      "competence": "data-modelling",
      "activity": "Derive the logical data model",
      "learning_outcome": "LO-5",
      "expected_share_mastered": 0.943,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-3.8-flash",
      "competence": "data-modelling",
      "activity": "Design the physical data model",
      "learning_outcome": "LO-6",
      "expected_share_mastered": 0.923,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-3.8-flash",
      "competence": "data-modelling",
      "activity": "Derive data quality rules",
      "learning_outcome": "LO-7",
      "expected_share_mastered": 0.939,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-3.8-flash",
      "competence": "data-modelling",
      "activity": "Generate and review DDL",
      "learning_outcome": "LO-8",
      "expected_share_mastered": 0.659,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gpt-6.1-sol",
      "competence": "data-modelling",
      "activity": "Capture data requirements",
      "learning_outcome": "LO-1",
      "expected_share_mastered": 0.726,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gpt-6.1-sol",
      "competence": "data-modelling",
      "activity": "Define business terms",
      "learning_outcome": "LO-2",
      "expected_share_mastered": 0.936,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gpt-6.1-sol",
      "competence": "data-modelling",
      "activity": "Document data rules as SBVR fact types and rule statements",
      "learning_outcome": "LO-3",
      "expected_share_mastered": 0.936,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gpt-6.1-sol",
      "competence": "data-modelling",
      "activity": "Build the conceptual model and taxonomies",
      "learning_outcome": "LO-4",
      "expected_share_mastered": 0.875,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gpt-6.1-sol",
      "competence": "data-modelling",
      "activity": "Derive the logical data model",
      "learning_outcome": "LO-5",
      "expected_share_mastered": 0.888,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gpt-6.1-sol",
      "competence": "data-modelling",
      "activity": "Design the physical data model",
      "learning_outcome": "LO-6",
      "expected_share_mastered": 0.92,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gpt-6.1-sol",
      "competence": "data-modelling",
      "activity": "Derive data quality rules",
      "learning_outcome": "LO-7",
      "expected_share_mastered": 0.982,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gpt-6.1-sol",
      "competence": "data-modelling",
      "activity": "Generate and review DDL",
      "learning_outcome": "LO-8",
      "expected_share_mastered": 0.784,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "claude-sonnet-5-5",
      "competence": "data-modelling",
      "activity": "Capture data requirements",
      "learning_outcome": "LO-1",
      "expected_share_mastered": 0.754,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "claude-sonnet-5-5",
      "competence": "data-modelling",
      "activity": "Define business terms",
      "learning_outcome": "LO-2",
      "expected_share_mastered": 0.985,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "claude-sonnet-5-5",
      "competence": "data-modelling",
      "activity": "Document data rules as SBVR fact types and rule statements",
      "learning_outcome": "LO-3",
      "expected_share_mastered": 0.997,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "claude-sonnet-5-5",
      "competence": "data-modelling",
      "activity": "Build the conceptual model and taxonomies",
      "learning_outcome": "LO-4",
      "expected_share_mastered": 0.937,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "claude-sonnet-5-5",
      "competence": "data-modelling",
      "activity": "Derive the logical data model",
      "learning_outcome": "LO-5",
      "expected_share_mastered": 0.947,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "claude-sonnet-5-5",
      "competence": "data-modelling",
      "activity": "Design the physical data model",
      "learning_outcome": "LO-6",
      "expected_share_mastered": 0.94,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "claude-sonnet-5-5",
      "competence": "data-modelling",
      "activity": "Derive data quality rules",
      "learning_outcome": "LO-7",
      "expected_share_mastered": 0.924,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "claude-sonnet-5-5",
      "competence": "data-modelling",
      "activity": "Generate and review DDL",
      "learning_outcome": "LO-8",
      "expected_share_mastered": 0.759,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-3.5-flash-lite",
      "competence": "data-modelling",
      "activity": "Capture data requirements",
      "learning_outcome": "LO-1",
      "expected_share_mastered": 0.531,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-3.5-flash-lite",
      "competence": "data-modelling",
      "activity": "Define business terms",
      "learning_outcome": "LO-2",
      "expected_share_mastered": 0.678,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-3.5-flash-lite",
      "competence": "data-modelling",
      "activity": "Document data rules as SBVR fact types and rule statements",
      "learning_outcome": "LO-3",
      "expected_share_mastered": 0.53,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-3.5-flash-lite",
      "competence": "data-modelling",
      "activity": "Build the conceptual model and taxonomies",
      "learning_outcome": "LO-4",
      "expected_share_mastered": 0.921,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-3.5-flash-lite",
      "competence": "data-modelling",
      "activity": "Derive the logical data model",
      "learning_outcome": "LO-5",
      "expected_share_mastered": 0.655,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-3.5-flash-lite",
      "competence": "data-modelling",
      "activity": "Design the physical data model",
      "learning_outcome": "LO-6",
      "expected_share_mastered": 0.684,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-3.5-flash-lite",
      "competence": "data-modelling",
      "activity": "Derive data quality rules",
      "learning_outcome": "LO-7",
      "expected_share_mastered": 0.658,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gemini-3.5-flash-lite",
      "competence": "data-modelling",
      "activity": "Generate and review DDL",
      "learning_outcome": "LO-8",
      "expected_share_mastered": 0.555,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gpt-6-luna",
      "competence": "data-modelling",
      "activity": "Capture data requirements",
      "learning_outcome": "LO-1",
      "expected_share_mastered": 0.587,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gpt-6-luna",
      "competence": "data-modelling",
      "activity": "Define business terms",
      "learning_outcome": "LO-2",
      "expected_share_mastered": 0.731,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gpt-6-luna",
      "competence": "data-modelling",
      "activity": "Document data rules as SBVR fact types and rule statements",
      "learning_outcome": "LO-3",
      "expected_share_mastered": 0.871,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gpt-6-luna",
      "competence": "data-modelling",
      "activity": "Build the conceptual model and taxonomies",
      "learning_outcome": "LO-4",
      "expected_share_mastered": 0.999,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gpt-6-luna",
      "competence": "data-modelling",
      "activity": "Derive the logical data model",
      "learning_outcome": "LO-5",
      "expected_share_mastered": 0.918,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gpt-6-luna",
      "competence": "data-modelling",
      "activity": "Design the physical data model",
      "learning_outcome": "LO-6",
      "expected_share_mastered": 0.895,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gpt-6-luna",
      "competence": "data-modelling",
      "activity": "Derive data quality rules",
      "learning_outcome": "LO-7",
      "expected_share_mastered": 0.89,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "gpt-6-luna",
      "competence": "data-modelling",
      "activity": "Generate and review DDL",
      "learning_outcome": "LO-8",
      "expected_share_mastered": 0.639,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "claude-haiku-5-5",
      "competence": "data-modelling",
      "activity": "Capture data requirements",
      "learning_outcome": "LO-1",
      "expected_share_mastered": 0.704,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "claude-haiku-5-5",
      "competence": "data-modelling",
      "activity": "Define business terms",
      "learning_outcome": "LO-2",
      "expected_share_mastered": 0.823,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "claude-haiku-5-5",
      "competence": "data-modelling",
      "activity": "Document data rules as SBVR fact types and rule statements",
      "learning_outcome": "LO-3",
      "expected_share_mastered": 0.933,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "claude-haiku-5-5",
      "competence": "data-modelling",
      "activity": "Build the conceptual model and taxonomies",
      "learning_outcome": "LO-4",
      "expected_share_mastered": 0.999,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "claude-haiku-5-5",
      "competence": "data-modelling",
      "activity": "Derive the logical data model",
      "learning_outcome": "LO-5",
      "expected_share_mastered": 0.94,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "claude-haiku-5-5",
      "competence": "data-modelling",
      "activity": "Design the physical data model",
      "learning_outcome": "LO-6",
      "expected_share_mastered": 0.921,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "claude-haiku-5-5",
      "competence": "data-modelling",
      "activity": "Derive data quality rules",
      "learning_outcome": "LO-7",
      "expected_share_mastered": 0.948,
      "source": "benchmark",
      "runs": 3
    },
    {
      "model": "claude-haiku-5-5",
      "competence": "data-modelling",
      "activity": "Generate and review DDL",
      "learning_outcome": "LO-8",
      "expected_share_mastered": 0.638,
      "source": "benchmark",
      "runs": 3
    }
  ],
  "named_agents": [],
  "url": "https://skilirium.com/ai-models#statistics"
}