commit

analysis/context_windows_distribution.txt

@@ -0,0 +1,26 @@
+
+Context Windows
+===============
+
+Sample Size: 342
+
+Central Tendency:
+  Mean:   198193.359649
+  Median: 131072.000000
+
+Spread:
+  Min:     2824.000000
+  Max:     2000000.000000
+  Std Dev: 283137.428820
+  IQR:     167232.000000
+
+Quartiles:
+  Q1 (25th percentile): 32768.000000
+  Q2 (50th percentile): 131072.000000
+  Q3 (75th percentile): 200000.000000
+
+Additional Percentiles:
+  10th: 30200.000000
+  90th: 300000.000000
+  95th: 1045206.800000
+  99th: 1048576.000000

analysis/context_windows_stats.json

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+{
+  "distribution": {
+    "name": "Context Windows",
+    "count": 342,
+    "min": 2824,
+    "max": 2000000,
+    "mean": 198193.3596491228,
+    "median": 131072.0,
+    "std_dev": 283137.4288197088,
+    "q1": 32768.0,
+    "q3": 200000.0,
+    "iqr": 167232.0,
+    "p10": 30200.000000000004,
+    "p90": 300000.0,
+    "p95": 1045206.7999999995,
+    "p99": 1048576.0
+  },
+  "stratification": {
+    "name": "Context Windows",
+    "bins": [
+      {
+        "range": "2824.000000 - 5443.537545",
+        "count": 8,
+        "percentage": 2.3391812865497075
+      },
+      {
+        "range": "5443.537545 - 10492.953615",
+        "count": 18,
+        "percentage": 5.263157894736842
+      },
+      {
+        "range": "10492.953615 - 20226.199351",
+        "count": 8,
+        "percentage": 2.3391812865497075
+      },
+      {
+        "range": "20226.199351 - 38987.987100",
+        "count": 53,
+        "percentage": 15.497076023391813
+      },
+      {
+        "range": "38987.987100 - 75153.176912",
+        "count": 18,
+        "percentage": 5.263157894736842
+      },
+      {
+        "range": "75153.176912 - 144865.134626",
+        "count": 124,
+        "percentage": 36.25730994152047
+      },
+      {
+        "range": "144865.134626 - 279241.784480",
+        "count": 77,
+        "percentage": 22.514619883040936
+      },
+      {
+        "range": "279241.784480 - 538265.983741",
+        "count": 10,
+        "percentage": 2.923976608187134
+      },
+      {
+        "range": "538265.983741 - 1037560.584969",
+        "count": 8,
+        "percentage": 2.3391812865497075
+      },
+      {
+        "range": "1037560.584969 - 2000000.000000",
+        "count": 18,
+        "percentage": 5.263157894736842
+      }
+    ]
+  }
+}

analysis/context_windows_stratification.txt

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+
+Context Windows - Stratification
+=================================
+
+Range Distribution:
+                 2824.000000 - 5443.537545:      8 models (  2.3%)
+                5443.537545 - 10492.953615:     18 models (  5.3%)
+               10492.953615 - 20226.199351:      8 models (  2.3%)
+               20226.199351 - 38987.987100:     53 models ( 15.5%)
+               38987.987100 - 75153.176912:     18 models (  5.3%)
+              75153.176912 - 144865.134626:    124 models ( 36.3%)
+             144865.134626 - 279241.784480:     77 models ( 22.5%)
+             279241.784480 - 538265.983741:     10 models (  2.9%)
+            538265.983741 - 1037560.584969:      8 models (  2.3%)
+           1037560.584969 - 2000000.000000:     18 models (  5.3%)

analysis/input_pricing_distribution.txt

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+
+Input Token Pricing (per 1M tokens)
+===================================
+
+Sample Size: 294
+
+Central Tendency:
+  Mean:   1.932805
+  Median: 0.300000
+
+Spread:
+  Min:     0.005000
+  Max:     150.000000
+  Std Dev: 9.365441
+  IQR:     1.000000
+
+Quartiles:
+  Q1 (25th percentile): 0.100000
+  Q2 (50th percentile): 0.300000
+  Q3 (75th percentile): 1.100000
+
+Additional Percentiles:
+  10th: 0.042400
+  90th: 3.000000
+  95th: 5.350000
+  99th: 20.700000

analysis/input_pricing_stats.json

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+{
+  "distribution": {
+    "name": "Input Token Pricing (per 1M tokens)",
+    "count": 294,
+    "min": 0.005,
+    "max": 150.0,
+    "mean": 1.932804524217687,
+    "median": 0.3,
+    "std_dev": 9.365441176402692,
+    "q1": 0.09999999999999999,
+    "q3": 1.1,
+    "iqr": 1.0,
+    "p10": 0.04240000000000001,
+    "p90": 3.0,
+    "p95": 5.349999999999966,
+    "p99": 20.699999999999932
+  },
+  "stratification": {
+    "name": "Input Token Pricing",
+    "bins": [
+      {
+        "range": "0.005000 - 0.014018",
+        "count": 1,
+        "percentage": 0.3401360544217687
+      },
+      {
+        "range": "0.014018 - 0.039300",
+        "count": 23,
+        "percentage": 7.8231292517006805
+      },
+      {
+        "range": "0.039300 - 0.110181",
+        "count": 60,
+        "percentage": 20.408163265306122
+      },
+      {
+        "range": "0.110181 - 0.308900",
+        "count": 71,
+        "percentage": 24.149659863945576
+      },
+      {
+        "range": "0.308900 - 0.866025",
+        "count": 51,
+        "percentage": 17.346938775510203
+      },
+      {
+        "range": "0.866025 - 2.427967",
+        "count": 41,
+        "percentage": 13.945578231292515
+      },
+      {
+        "range": "2.427967 - 6.806986",
+        "count": 33,
+        "percentage": 11.224489795918368
+      },
+      {
+        "range": "6.806986 - 19.083895",
+        "count": 10,
+        "percentage": 3.4013605442176873
+      },
+      {
+        "range": "19.083895 - 53.503123",
+        "count": 3,
+        "percentage": 1.0204081632653061
+      },
+      {
+        "range": "53.503123 - 150.000000",
+        "count": 1,
+        "percentage": 0.3401360544217687
+      }
+    ]
+  }
+}

analysis/input_pricing_stratification.txt

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+
+Input Token Pricing - Stratification
+=====================================
+
+Range Distribution:
+                       0.005000 - 0.014018:      1 models (  0.3%)
+                       0.014018 - 0.039300:     23 models (  7.8%)
+                       0.039300 - 0.110181:     60 models ( 20.4%)
+                       0.110181 - 0.308900:     71 models ( 24.1%)
+                       0.308900 - 0.866025:     51 models ( 17.3%)
+                       0.866025 - 2.427967:     41 models ( 13.9%)
+                       2.427967 - 6.806986:     33 models ( 11.2%)
+                      6.806986 - 19.083895:     10 models (  3.4%)
+                     19.083895 - 53.503123:      3 models (  1.0%)
+                    53.503123 - 150.000000:      1 models (  0.3%)

analysis/output_input_ratio_distribution.txt

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+
+Output/Input Price Ratio
+========================
+
+Sample Size: 294
+
+Central Tendency:
+  Mean:   3.528242
+  Median: 4.000000
+
+Spread:
+  Min:     0.800000
+  Max:     11.666667
+  Std Dev: 1.909742
+  IQR:     2.166667
+
+Quartiles:
+  Q1 (25th percentile): 2.000000
+  Q2 (50th percentile): 4.000000
+  Q3 (75th percentile): 4.166667
+
+Additional Percentiles:
+  10th: 1.000000
+  90th: 5.000000
+  95th: 8.000000
+  99th: 8.450000

analysis/output_input_ratio_stats.json

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+{
+  "distribution": {
+    "name": "Output/Input Price Ratio",
+    "count": 294,
+    "min": 0.8,
+    "max": 11.666666666666664,
+    "mean": 3.5282415447399473,
+    "median": 4.0,
+    "std_dev": 1.9097416372501397,
+    "q1": 2.0,
+    "q3": 4.166666666666667,
+    "iqr": 2.166666666666667,
+    "p10": 1.0,
+    "p90": 5.0,
+    "p95": 8.0,
+    "p99": 8.449999999999989
+  },
+  "stratification": {
+    "name": "Output/Input Price Ratio",
+    "bins": [
+      {
+        "range": "0.800000 - 1.045865",
+        "count": 40,
+        "percentage": 13.60544217687075
+      },
+      {
+        "range": "1.045865 - 1.367292",
+        "count": 7,
+        "percentage": 2.380952380952381
+      },
+      {
+        "range": "1.367292 - 1.787504",
+        "count": 21,
+        "percentage": 7.142857142857142
+      },
+      {
+        "range": "1.787504 - 2.336860",
+        "count": 20,
+        "percentage": 6.802721088435375
+      },
+      {
+        "range": "2.336860 - 3.055050",
+        "count": 38,
+        "percentage": 12.925170068027212
+      },
+      {
+        "range": "3.055050 - 3.993963",
+        "count": 20,
+        "percentage": 6.802721088435375
+      },
+      {
+        "range": "3.993963 - 5.221433",
+        "count": 121,
+        "percentage": 41.156462585034014
+      },
+      {
+        "range": "5.221433 - 6.826144",
+        "count": 7,
+        "percentage": 2.380952380952381
+      },
+      {
+        "range": "6.826144 - 8.924032",
+        "count": 17,
+        "percentage": 5.782312925170068
+      },
+      {
+        "range": "8.924032 - 11.666667",
+        "count": 2,
+        "percentage": 0.6802721088435374
+      }
+    ]
+  }
+}

analysis/output_input_ratio_stratification.txt

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+
+Output/Input Price Ratio - Stratification
+==========================================
+
+Range Distribution:
+                       0.800000 - 1.045865:     40 models ( 13.6%)
+                       1.045865 - 1.367292:      7 models (  2.4%)
+                       1.367292 - 1.787504:     21 models (  7.1%)
+                       1.787504 - 2.336860:     20 models (  6.8%)
+                       2.336860 - 3.055050:     38 models ( 12.9%)
+                       3.055050 - 3.993963:     20 models (  6.8%)
+                       3.993963 - 5.221433:    121 models ( 41.2%)
+                       5.221433 - 6.826144:      7 models (  2.4%)
+                       6.826144 - 8.924032:     17 models (  5.8%)
+                      8.924032 - 11.666667:      2 models (  0.7%)

analysis/output_pricing_distribution.txt

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+
+Output Token Pricing (per 1M tokens)
+====================================
+
+Sample Size: 294
+
+Central Tendency:
+  Mean:   7.074562
+  Median: 1.000000
+
+Spread:
+  Min:     0.010000
+  Max:     600.000000
+  Std Dev: 37.152968
+  IQR:     3.700000
+
+Quartiles:
+  Q1 (25th percentile): 0.300000
+  Q2 (50th percentile): 1.000000
+  Q3 (75th percentile): 4.000000
+
+Additional Percentiles:
+  10th: 0.103000
+  90th: 10.000000
+  95th: 15.000000
+  99th: 75.350000

analysis/output_pricing_stats.json

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+{
+  "distribution": {
+    "name": "Output Token Pricing (per 1M tokens)",
+    "count": 294,
+    "min": 0.01,
+    "max": 600.0,
+    "mean": 7.074561746938776,
+    "median": 1.0,
+    "std_dev": 37.152967606908874,
+    "q1": 0.3,
+    "q3": 4.0,
+    "iqr": 3.7,
+    "p10": 0.10300000000000001,
+    "p90": 10.0,
+    "p95": 15.0,
+    "p99": 75.34999999999997
+  },
+  "stratification": {
+    "name": "Output Token Pricing",
+    "bins": [
+      {
+        "range": "0.010000 - 0.030048",
+        "count": 4,
+        "percentage": 1.3605442176870748
+      },
+      {
+        "range": "0.030048 - 0.090288",
+        "count": 15,
+        "percentage": 5.1020408163265305
+      },
+      {
+        "range": "0.090288 - 0.271297",
+        "count": 48,
+        "percentage": 16.3265306122449
+      },
+      {
+        "range": "0.271297 - 0.815193",
+        "count": 69,
+        "percentage": 23.46938775510204
+      },
+      {
+        "range": "0.815193 - 2.449490",
+        "count": 70,
+        "percentage": 23.809523809523807
+      },
+      {
+        "range": "2.449490 - 7.360219",
+        "count": 37,
+        "percentage": 12.585034013605442
+      },
+      {
+        "range": "7.360219 - 22.115964",
+        "count": 38,
+        "percentage": 12.925170068027212
+      },
+      {
+        "range": "22.115964 - 66.453981",
+        "count": 7,
+        "percentage": 2.380952380952381
+      },
+      {
+        "range": "66.453981 - 199.680716",
+        "count": 5,
+        "percentage": 1.7006802721088436
+      },
+      {
+        "range": "199.680716 - 600.000000",
+        "count": 0,
+        "percentage": 0.0
+      }
+    ]
+  }
+}

analysis/output_pricing_stratification.txt

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+
+Output Token Pricing - Stratification
+======================================
+
+Range Distribution:
+                       0.010000 - 0.030048:      4 models (  1.4%)
+                       0.030048 - 0.090288:     15 models (  5.1%)
+                       0.090288 - 0.271297:     48 models ( 16.3%)
+                       0.271297 - 0.815193:     69 models ( 23.5%)
+                       0.815193 - 2.449490:     70 models ( 23.8%)
+                       2.449490 - 7.360219:     37 models ( 12.6%)
+                      7.360219 - 22.115964:     38 models ( 12.9%)
+                     22.115964 - 66.453981:      7 models (  2.4%)
+                    66.453981 - 199.680716:      5 models (  1.7%)
+                   199.680716 - 600.000000:      0 models (  0.0%)

analysis/pricing_analysis.py

@@ -0,0 +1,259 @@
+#!/usr/bin/env python3
+"""
+OpenRouter API Pricing Analysis
+Analyzes context windows, token pricing, and output/input ratios
+"""
+
+import json
+import numpy as np
+from pathlib import Path
+from typing import Dict, List, Any
+import statistics
+
+def load_data(file_path: str) -> List[Dict[str, Any]]:
+    """Load the models data from JSON file"""
+    with open(file_path, 'r') as f:
+        return json.load(f)
+
+def calculate_distribution_stats(values: List[float], name: str) -> Dict[str, Any]:
+    """Calculate comprehensive distribution statistics"""
+    if not values:
+        return {"error": "No data"}
+
+    values = sorted(values)
+    n = len(values)
+
+    stats = {
+        "name": name,
+        "count": n,
+        "min": min(values),
+        "max": max(values),
+        "mean": statistics.mean(values),
+        "median": statistics.median(values),
+        "std_dev": statistics.stdev(values) if n > 1 else 0,
+    }
+
+    # Quartiles
+    stats["q1"] = np.percentile(values, 25)
+    stats["q3"] = np.percentile(values, 75)
+    stats["iqr"] = stats["q3"] - stats["q1"]
+
+    # Additional percentiles
+    stats["p10"] = np.percentile(values, 10)
+    stats["p90"] = np.percentile(values, 90)
+    stats["p95"] = np.percentile(values, 95)
+    stats["p99"] = np.percentile(values, 99)
+
+    return stats
+
+def calculate_stratification(values: List[float], name: str) -> Dict[str, Any]:
+    """Calculate stratification by creating bins/ranges"""
+    if not values:
+        return {"error": "No data"}
+
+    values_array = np.array(values)
+
+    # Determine appropriate bins based on data range
+    min_val = values_array.min()
+    max_val = values_array.max()
+
+    # Create stratification bins
+    if max_val - min_val < 1:
+        # For very small ranges (like some pricing)
+        bins = np.linspace(min_val, max_val, 11)
+    else:
+        # Use log scale for wide ranges
+        if min_val > 0:
+            bins = np.logspace(np.log10(max(min_val, 0.0001)), np.log10(max_val), 11)
+        else:
+            bins = np.linspace(min_val, max_val, 11)
+
+    hist, bin_edges = np.histogram(values_array, bins=bins)
+
+    stratification = {
+        "name": name,
+        "bins": []
+    }
+
+    for i in range(len(hist)):
+        stratification["bins"].append({
+            "range": f"{bin_edges[i]:.6f} - {bin_edges[i+1]:.6f}",
+            "count": int(hist[i]),
+            "percentage": float(hist[i] / len(values) * 100)
+        })
+
+    return stratification
+
+def analyze_context_windows(models: List[Dict[str, Any]]) -> tuple:
+    """Analyze context window distribution"""
+    context_lengths = [
+        m['context_length']
+        for m in models
+        if m.get('context_length') and m['context_length'] > 0
+    ]
+
+    distribution = calculate_distribution_stats(context_lengths, "Context Windows")
+    stratification = calculate_stratification(context_lengths, "Context Windows")
+
+    return distribution, stratification
+
+def analyze_input_pricing(models: List[Dict[str, Any]]) -> tuple:
+    """Analyze input token pricing distribution"""
+    input_prices = [
+        m['prompt_price_per_1m_tokens']
+        for m in models
+        if m.get('prompt_price_per_1m_tokens') is not None and m['prompt_price_per_1m_tokens'] > 0
+    ]
+
+    distribution = calculate_distribution_stats(input_prices, "Input Token Pricing (per 1M tokens)")
+    stratification = calculate_stratification(input_prices, "Input Token Pricing")
+
+    return distribution, stratification
+
+def analyze_output_pricing(models: List[Dict[str, Any]]) -> tuple:
+    """Analyze output token pricing distribution"""
+    output_prices = [
+        m['completion_price_per_1m_tokens']
+        for m in models
+        if m.get('completion_price_per_1m_tokens') is not None and m['completion_price_per_1m_tokens'] > 0
+    ]
+
+    distribution = calculate_distribution_stats(output_prices, "Output Token Pricing (per 1M tokens)")
+    stratification = calculate_stratification(output_prices, "Output Token Pricing")
+
+    return distribution, stratification
+
+def analyze_output_input_ratio(models: List[Dict[str, Any]]) -> tuple:
+    """Analyze output/input token price ratio"""
+    ratios = []
+
+    for m in models:
+        input_price = m.get('prompt_price_per_1m_tokens', 0)
+        output_price = m.get('completion_price_per_1m_tokens', 0)
+
+        # Calculate ratio only for non-free models
+        if input_price > 0 and output_price > 0:
+            ratio = output_price / input_price
+            ratios.append(ratio)
+
+    distribution = calculate_distribution_stats(ratios, "Output/Input Price Ratio")
+    stratification = calculate_stratification(ratios, "Output/Input Price Ratio")
+
+    return distribution, stratification
+
+def format_distribution_report(dist: Dict[str, Any]) -> str:
+    """Format distribution statistics as readable text"""
+    if "error" in dist:
+        return f"Error: {dist['error']}"
+
+    report = f"""
+{dist['name']}
+{'=' * len(dist['name'])}
+
+Sample Size: {dist['count']:,}
+
+Central Tendency:
+  Mean:   {dist['mean']:.6f}
+  Median: {dist['median']:.6f}
+
+Spread:
+  Min:     {dist['min']:.6f}
+  Max:     {dist['max']:.6f}
+  Std Dev: {dist['std_dev']:.6f}
+  IQR:     {dist['iqr']:.6f}
+
+Quartiles:
+  Q1 (25th percentile): {dist['q1']:.6f}
+  Q2 (50th percentile): {dist['median']:.6f}
+  Q3 (75th percentile): {dist['q3']:.6f}
+
+Additional Percentiles:
+  10th: {dist['p10']:.6f}
+  90th: {dist['p90']:.6f}
+  95th: {dist['p95']:.6f}
+  99th: {dist['p99']:.6f}
+"""
+    return report
+
+def format_stratification_report(strat: Dict[str, Any]) -> str:
+    """Format stratification as readable text"""
+    if "error" in strat:
+        return f"Error: {strat['error']}"
+
+    report = f"""
+{strat['name']} - Stratification
+{'=' * (len(strat['name']) + 18)}
+
+Range Distribution:
+"""
+
+    for bin_data in strat['bins']:
+        report += f"  {bin_data['range']:>40s}: {bin_data['count']:>6,} models ({bin_data['percentage']:>5.1f}%)\n"
+
+    return report
+
+def main():
+    # Load data
+    data_file = Path(__file__).parent.parent / 'raw' / 'models.json'
+    models = load_data(data_file)
+
+    print(f"Loaded {len(models)} models\n")
+
+    # Create output directory
+    output_dir = Path(__file__).parent
+    output_dir.mkdir(exist_ok=True)
+
+    # Analyze each metric
+    analyses = {
+        'context_windows': analyze_context_windows(models),
+        'input_pricing': analyze_input_pricing(models),
+        'output_pricing': analyze_output_pricing(models),
+        'output_input_ratio': analyze_output_input_ratio(models)
+    }
+
+    # Generate reports for each analysis
+    for metric_name, (distribution, stratification) in analyses.items():
+        # Create individual report files
+        dist_report = format_distribution_report(distribution)
+        strat_report = format_stratification_report(stratification)
+
+        # Write distribution report
+        dist_file = output_dir / f"{metric_name}_distribution.txt"
+        with open(dist_file, 'w') as f:
+            f.write(dist_report)
+        print(f"Created: {dist_file}")
+
+        # Write stratification report
+        strat_file = output_dir / f"{metric_name}_stratification.txt"
+        with open(strat_file, 'w') as f:
+            f.write(strat_report)
+        print(f"Created: {strat_file}")
+
+        # Also save as JSON for programmatic access
+        json_file = output_dir / f"{metric_name}_stats.json"
+        with open(json_file, 'w') as f:
+            json.dump({
+                'distribution': distribution,
+                'stratification': stratification
+            }, f, indent=2)
+        print(f"Created: {json_file}")
+
+    # Create comprehensive summary report
+    summary_file = output_dir / "summary_report.txt"
+    with open(summary_file, 'w') as f:
+        f.write("OpenRouter API Pricing Analysis - Summary Report\n")
+        f.write("=" * 60 + "\n\n")
+        f.write(f"Dataset: {len(models)} models analyzed\n\n")
+
+        for metric_name, (distribution, stratification) in analyses.items():
+            f.write("\n" + "=" * 60 + "\n")
+            f.write(format_distribution_report(distribution))
+            f.write("\n")
+            f.write(format_stratification_report(stratification))
+            f.write("\n")
+
+    print(f"\nCreated: {summary_file}")
+    print("\nAnalysis complete!")
+
+if __name__ == "__main__":
+    main()

analysis/summary_report.txt

@@ -0,0 +1,185 @@
+OpenRouter API Pricing Analysis - Summary Report
+============================================================
+
+Dataset: 342 models analyzed
+
+
+============================================================
+
+Context Windows
+===============
+
+Sample Size: 342
+
+Central Tendency:
+  Mean:   198193.359649
+  Median: 131072.000000
+
+Spread:
+  Min:     2824.000000
+  Max:     2000000.000000
+  Std Dev: 283137.428820
+  IQR:     167232.000000
+
+Quartiles:
+  Q1 (25th percentile): 32768.000000
+  Q2 (50th percentile): 131072.000000
+  Q3 (75th percentile): 200000.000000
+
+Additional Percentiles:
+  10th: 30200.000000
+  90th: 300000.000000
+  95th: 1045206.800000
+  99th: 1048576.000000
+
+
+Context Windows - Stratification
+=================================
+
+Range Distribution:
+                 2824.000000 - 5443.537545:      8 models (  2.3%)
+                5443.537545 - 10492.953615:     18 models (  5.3%)
+               10492.953615 - 20226.199351:      8 models (  2.3%)
+               20226.199351 - 38987.987100:     53 models ( 15.5%)
+               38987.987100 - 75153.176912:     18 models (  5.3%)
+              75153.176912 - 144865.134626:    124 models ( 36.3%)
+             144865.134626 - 279241.784480:     77 models ( 22.5%)
+             279241.784480 - 538265.983741:     10 models (  2.9%)
+            538265.983741 - 1037560.584969:      8 models (  2.3%)
+           1037560.584969 - 2000000.000000:     18 models (  5.3%)
+
+
+============================================================
+
+Input Token Pricing (per 1M tokens)
+===================================
+
+Sample Size: 294
+
+Central Tendency:
+  Mean:   1.932805
+  Median: 0.300000
+
+Spread:
+  Min:     0.005000
+  Max:     150.000000
+  Std Dev: 9.365441
+  IQR:     1.000000
+
+Quartiles:
+  Q1 (25th percentile): 0.100000
+  Q2 (50th percentile): 0.300000
+  Q3 (75th percentile): 1.100000
+
+Additional Percentiles:
+  10th: 0.042400
+  90th: 3.000000
+  95th: 5.350000
+  99th: 20.700000
+
+
+Input Token Pricing - Stratification
+=====================================
+
+Range Distribution:
+                       0.005000 - 0.014018:      1 models (  0.3%)
+                       0.014018 - 0.039300:     23 models (  7.8%)
+                       0.039300 - 0.110181:     60 models ( 20.4%)
+                       0.110181 - 0.308900:     71 models ( 24.1%)
+                       0.308900 - 0.866025:     51 models ( 17.3%)
+                       0.866025 - 2.427967:     41 models ( 13.9%)
+                       2.427967 - 6.806986:     33 models ( 11.2%)
+                      6.806986 - 19.083895:     10 models (  3.4%)
+                     19.083895 - 53.503123:      3 models (  1.0%)
+                    53.503123 - 150.000000:      1 models (  0.3%)
+
+
+============================================================
+
+Output Token Pricing (per 1M tokens)
+====================================
+
+Sample Size: 294
+
+Central Tendency:
+  Mean:   7.074562
+  Median: 1.000000
+
+Spread:
+  Min:     0.010000
+  Max:     600.000000
+  Std Dev: 37.152968
+  IQR:     3.700000
+
+Quartiles:
+  Q1 (25th percentile): 0.300000
+  Q2 (50th percentile): 1.000000
+  Q3 (75th percentile): 4.000000
+
+Additional Percentiles:
+  10th: 0.103000
+  90th: 10.000000
+  95th: 15.000000
+  99th: 75.350000
+
+
+Output Token Pricing - Stratification
+======================================
+
+Range Distribution:
+                       0.010000 - 0.030048:      4 models (  1.4%)
+                       0.030048 - 0.090288:     15 models (  5.1%)
+                       0.090288 - 0.271297:     48 models ( 16.3%)
+                       0.271297 - 0.815193:     69 models ( 23.5%)
+                       0.815193 - 2.449490:     70 models ( 23.8%)
+                       2.449490 - 7.360219:     37 models ( 12.6%)
+                      7.360219 - 22.115964:     38 models ( 12.9%)
+                     22.115964 - 66.453981:      7 models (  2.4%)
+                    66.453981 - 199.680716:      5 models (  1.7%)
+                   199.680716 - 600.000000:      0 models (  0.0%)
+
+
+============================================================
+
+Output/Input Price Ratio
+========================
+
+Sample Size: 294
+
+Central Tendency:
+  Mean:   3.528242
+  Median: 4.000000
+
+Spread:
+  Min:     0.800000
+  Max:     11.666667
+  Std Dev: 1.909742
+  IQR:     2.166667
+
+Quartiles:
+  Q1 (25th percentile): 2.000000
+  Q2 (50th percentile): 4.000000
+  Q3 (75th percentile): 4.166667
+
+Additional Percentiles:
+  10th: 1.000000
+  90th: 5.000000
+  95th: 8.000000
+  99th: 8.450000
+
+
+Output/Input Price Ratio - Stratification
+==========================================
+
+Range Distribution:
+                       0.800000 - 1.045865:     40 models ( 13.6%)
+                       1.045865 - 1.367292:      7 models (  2.4%)
+                       1.367292 - 1.787504:     21 models (  7.1%)
+                       1.787504 - 2.336860:     20 models (  6.8%)
+                       2.336860 - 3.055050:     38 models ( 12.9%)
+                       3.055050 - 3.993963:     20 models (  6.8%)
+                       3.993963 - 5.221433:    121 models ( 41.2%)
+                       5.221433 - 6.826144:      7 models (  2.4%)
+                       6.826144 - 8.924032:     17 models (  5.8%)
+                      8.924032 - 11.666667:      2 models (  0.7%)
+