{
  "evaluation_id": "EVAL-20260317-021652",
  "question_id": "EVAL-20260317-021652",
  "question_text": "This SQL query takes 45 seconds on a table with 10M rows. Rewrite it to run in under 1 second. Explain your optimization strategy.\n\n```sql\nSELECT u.name, u.email,\n       (SELECT COUNT(*) FROM orders o WHERE o.user_id = u.id) as order_count,\n       (SELECT SUM(amount) FROM orders o WHERE o.user_id = u.id) as total_spent,\n       (SELECT MAX(created_at) FROM orders o WHERE o.user_id = u.id) as last_order\nFROM users u\nWHERE u.created_at > '2024-01-01'\nAND (SELECT COUNT(*) FROM orders o WHERE o.user_id = u.id) > 5\nORDER BY total_spent DESC\nLIMIT 100;\n```\n\nAssume standard B-tree indexes on primary keys only. What indexes would you add?",
  "category": "code",
  "timestamp": "2026-03-17T02:16:52.231Z",
  "display_date": "Mar 17, 2026",
  "winner": {
    "name": "Qwen 3.5 397B-A17B",
    "provider": "openrouter",
    "score": 9.55
  },
  "avg_score": 9.3425,
  "matrix_size": 53,
  "models_used": [
    {
      "id": "qwen3_32b",
      "name": "Qwen 3 32B",
      "provider": "openrouter"
    },
    {
      "id": "qwen3_coder_next",
      "name": "Qwen 3 Coder Next",
      "provider": "openrouter"
    },
    {
      "id": "qwen35_35b_a3b",
      "name": "Qwen 3.5 35B-A3B",
      "provider": "openrouter"
    },
    {
      "id": "qwen35_27b",
      "name": "Qwen 3.5 27B",
      "provider": "openrouter"
    },
    {
      "id": "qwen35_122b_a10b",
      "name": "Qwen 3.5 122B-A10B",
      "provider": "openrouter"
    },
    {
      "id": "qwen35_397b_a17b",
      "name": "Qwen 3.5 397B-A17B",
      "provider": "openrouter"
    },
    {
      "id": "qwen35_9b",
      "name": "Qwen 3.5 9B",
      "provider": "openrouter"
    },
    {
      "id": "qwen3_8b",
      "name": "Qwen 3 8B",
      "provider": "openrouter"
    }
  ],
  "rankings": {
    "qwen35_397b_a17b": {
      "display_name": "Qwen 3.5 397B-A17B",
      "provider": "openrouter",
      "average_score": 9.55,
      "score_count": 6,
      "min_score": 8.95,
      "max_score": 10,
      "rank": 1
    },
    "qwen35_27b": {
      "display_name": "Qwen 3.5 27B",
      "provider": "openrouter",
      "average_score": 9.51,
      "score_count": 7,
      "min_score": 8.65,
      "max_score": 10,
      "rank": 2
    },
    "qwen35_122b_a10b": {
      "display_name": "Qwen 3.5 122B-A10B",
      "provider": "openrouter",
      "average_score": 9.43,
      "score_count": 7,
      "min_score": 8.6,
      "max_score": 10,
      "rank": 3
    },
    "qwen3_coder_next": {
      "display_name": "Qwen 3 Coder Next",
      "provider": "openrouter",
      "average_score": 9.38,
      "score_count": 7,
      "min_score": 8.75,
      "max_score": 10,
      "rank": 4
    },
    "qwen35_9b": {
      "display_name": "Qwen 3.5 9B",
      "provider": "openrouter",
      "average_score": 9.34,
      "score_count": 6,
      "min_score": 8.55,
      "max_score": 10,
      "rank": 5
    },
    "qwen3_32b": {
      "display_name": "Qwen 3 32B",
      "provider": "openrouter",
      "average_score": 9.25,
      "score_count": 7,
      "min_score": 8.4,
      "max_score": 10,
      "rank": 6
    },
    "qwen3_8b": {
      "display_name": "Qwen 3 8B",
      "provider": "openrouter",
      "average_score": 9.22,
      "score_count": 6,
      "min_score": 8.15,
      "max_score": 10,
      "rank": 7
    },
    "qwen35_35b_a3b": {
      "display_name": "Qwen 3.5 35B-A3B",
      "provider": "openrouter",
      "average_score": 9.06,
      "score_count": 7,
      "min_score": 7.8,
      "max_score": 10,
      "rank": 8
    }
  },
  "judgments": [
    {
      "judge_key": "qwen35_9b",
      "judge_name": "Qwen 3.5 9B",
      "respondent_key": "qwen3_32b",
      "respondent_name": "Qwen 3 32B",
      "weighted_score": 8.4,
      "brief_justification": "The response correctly identifies correlated subqueries as the bottleneck and provides an accurate JOIN-based solution with appropriate indexes. Minor deductions for not addressing edge cases like users with no orders or database-specific execution plan variations."
    },
    {
      "judge_key": "qwen35_9b",
      "judge_name": "Qwen 3.5 9B",
      "respondent_key": "qwen3_coder_next",
      "respondent_name": "Qwen 3 Coder Next",
      "weighted_score": 9,
      "brief_justification": "Response correctly identifies correlated subquery problem and provides sound optimization strategies with proper index recommendations. Minor PostgreSQL-specific syntax (JOIN LATERAL) and slight over-specification of indexes don't significantly impact overall quality."
    },
    {
      "judge_key": "qwen35_9b",
      "judge_name": "Qwen 3.5 9B",
      "respondent_key": "qwen35_35b_a3b",
      "respondent_name": "Qwen 3.5 35B-A3B",
      "weighted_score": 8.35,
      "brief_justification": "The core optimization strategy (replacing correlated subqueries with JOIN+GROUP BY) is correct and well-explained. However, the partial index syntax shown is invalid in most SQL databases, and some database-specific considerations are missing. Overall strong response with actionable code and clear explanations."
    },
    {
      "judge_key": "qwen35_9b",
      "judge_name": "Qwen 3.5 9B",
      "respondent_key": "qwen35_27b",
      "respondent_name": "Qwen 3.5 27B",
      "weighted_score": 8.8,
      "brief_justification": "Response correctly identifies correlated subqueries as the bottleneck and provides valid JOIN/GROUP BY optimization with appropriate index recommendations. Minor room for improvement on explaining edge cases like partial index usage or query plan considerations."
    },
    {
      "judge_key": "qwen35_9b",
      "judge_name": "Qwen 3.5 9B",
      "respondent_key": "qwen35_122b_a10b",
      "respondent_name": "Qwen 3.5 122B-A10B",
      "weighted_score": 8.8,
      "brief_justification": "Response accurately identifies correlated subquery problem and provides correct JOIN-based solution with appropriate composite indexes. Minor deduction for not addressing potential NULL handling in LEFT JOIN or mentioning that ORDER BY should reference the alias properly. Well-structured with clear execution flow and practical additional considerations."
    },
    {
      "judge_key": "qwen35_9b",
      "judge_name": "Qwen 3.5 9B",
      "respondent_key": "qwen35_397b_a17b",
      "respondent_name": "Qwen 3.5 397B-A17B",
      "weighted_score": 9,
      "brief_justification": "Excellent response with technically sound optimization strategy (replacing correlated subqueries with JOINs and pre-aggregation). Minor inconsistency mentions 1M rows when table has 10M, but core solution is accurate. Comprehensive coverage includes indexes, execution plan comparison, and additional recommendations."
    },
    {
      "judge_key": "qwen3_8b",
      "judge_name": "Qwen 3 8B",
      "respondent_key": "qwen35_9b",
      "respondent_name": "Qwen 3.5 9B",
      "weighted_score": 9.4,
      "brief_justification": "The response correctly identifies performance bottlenecks, provides actionable optimizations, and explains technical details thoroughly. The optimized query and index strategy are practical and well-justified."
    },
    {
      "judge_key": "qwen3_8b",
      "judge_name": "Qwen 3 8B",
      "respondent_key": "qwen3_32b",
      "respondent_name": "Qwen 3 32B",
      "weighted_score": 9.6,
      "brief_justification": "The response correctly identifies correlated subquery inefficiencies, provides an optimized query with proper indexing strategy, and explains the rationale. It covers key optimizations but could have briefly mentioned alternative approaches like materialized views."
    },
    {
      "judge_key": "qwen3_8b",
      "judge_name": "Qwen 3 8B",
      "respondent_key": "qwen3_coder_next",
      "respondent_name": "Qwen 3 Coder Next",
      "weighted_score": 10,
      "brief_justification": "The response thoroughly analyzes the query's inefficiencies, provides two optimized solutions with clear explanations, identifies critical indexes, and includes performance metrics. It addresses all aspects of the question with actionable insights and technical depth."
    },
    {
      "judge_key": "qwen3_8b",
      "judge_name": "Qwen 3 8B",
      "respondent_key": "qwen35_35b_a3b",
      "respondent_name": "Qwen 3.5 35B-A3B",
      "weighted_score": 10,
      "brief_justification": "The response provides a correct optimization strategy by eliminating correlated subqueries, suggests effective indexes, and explains the performance improvements. It covers all aspects of the question with clear, actionable solutions and additional optimization options."
    },
    {
      "judge_key": "qwen3_8b",
      "judge_name": "Qwen 3 8B",
      "respondent_key": "qwen35_27b",
      "respondent_name": "Qwen 3.5 27B",
      "weighted_score": 10,
      "brief_justification": "The response correctly identifies and fixes the correlated subquery bottleneck, provides optimal indexes, and thoroughly explains the optimization strategy with technical depth and practical value."
    },
    {
      "judge_key": "qwen3_8b",
      "judge_name": "Qwen 3 8B",
      "respondent_key": "qwen35_122b_a10b",
      "respondent_name": "Qwen 3.5 122B-A10B",
      "weighted_score": 9.6,
      "brief_justification": "The response correctly identifies correlated subquery inefficiencies, provides an optimized JOIN-based solution, and suggests relevant indexes. It thoroughly explains the optimization strategy with clear technical reasoning and practical considerations."
    },
    {
      "judge_key": "qwen3_8b",
      "judge_name": "Qwen 3 8B",
      "respondent_key": "qwen35_397b_a17b",
      "respondent_name": "Qwen 3.5 397B-A17B",
      "weighted_score": 10,
      "brief_justification": "The response correctly identifies correlated subqueries as the main bottleneck, provides an optimized query with proper indexing strategy, and explains performance gains with clear metrics. All aspects of the question are thoroughly addressed with actionable recommendations."
    },
    {
      "judge_key": "qwen3_32b",
      "judge_name": "Qwen 3 32B",
      "respondent_key": "qwen35_9b",
      "respondent_name": "Qwen 3.5 9B",
      "weighted_score": 9.55,
      "brief_justification": "The response accurately identifies performance issues, provides a well-structured optimized query using CTE and JOINs, recommends appropriate indexes, and explains the benefits clearly. It also includes additional recommendations for scalability and maintainability."
    },
    {
      "judge_key": "qwen3_32b",
      "judge_name": "Qwen 3 32B",
      "respondent_key": "qwen3_8b",
      "respondent_name": "Qwen 3 8B",
      "weighted_score": 10,
      "brief_justification": "The response provides a technically accurate, comprehensive solution that replaces correlated subqueries with efficient aggregation, explains optimization strategies in depth, recommends appropriate indexes, and clearly demonstrates how these changes reduce execution time from 45 seconds to under 1 second."
    },
    {
      "judge_key": "qwen3_32b",
      "judge_name": "Qwen 3 32B",
      "respondent_key": "qwen3_coder_next",
      "respondent_name": "Qwen 3 Coder Next",
      "weighted_score": 10,
      "brief_justification": "The response thoroughly explains the inefficiency of correlated subqueries, provides two optimized query strategies with clear technical reasoning, and recommends precise indexes with justifications. It includes performance comparisons, edge case handling (e.g., NULL values), and actionable steps like `EXPLAIN ANALYZE` verification. The structure is logical, and the explanations are both technically accurate and accessible."
    },
    {
      "judge_key": "qwen3_32b",
      "judge_name": "Qwen 3 32B",
      "respondent_key": "qwen35_35b_a3b",
      "respondent_name": "Qwen 3.5 35B-A3B",
      "weighted_score": 9.35,
      "brief_justification": "The response correctly replaces correlated subqueries with a JOIN and GROUP BY, provides appropriate indexes, and explains the optimization strategy thoroughly. The additional options like materialized views and partial indexes enhance completeness. Minor edge cases in the partial index example slightly reduce the correctness score."
    },
    {
      "judge_key": "qwen3_32b",
      "judge_name": "Qwen 3 32B",
      "respondent_key": "qwen35_27b",
      "respondent_name": "Qwen 3.5 27B",
      "weighted_score": 9.8,
      "brief_justification": "The response correctly eliminates correlated subqueries with a JOIN and GROUP BY, adds necessary indexes (user_id, created_at, covering index), and explains performance improvements through index-only scans and reduced complexity. The optimization strategy is thorough and actionable."
    },
    {
      "judge_key": "qwen3_32b",
      "judge_name": "Qwen 3 32B",
      "respondent_key": "qwen35_122b_a10b",
      "respondent_name": "Qwen 3.5 122B-A10B",
      "weighted_score": 10,
      "brief_justification": "The response provides a comprehensive, correct optimization strategy with clear explanations, appropriate index suggestions, and detailed performance analysis. It thoroughly addresses all aspects of the question with actionable solutions."
    },
    {
      "judge_key": "qwen3_32b",
      "judge_name": "Qwen 3 32B",
      "respondent_key": "qwen35_397b_a17b",
      "respondent_name": "Qwen 3.5 397B-A17B",
      "weighted_score": 10,
      "brief_justification": "The response correctly eliminates correlated subqueries through aggregation and JOINs, provides precise index recommendations, and explains performance gains with execution plan analysis. It offers actionable optimizations and additional strategies like materialized views, demonstrating comprehensive technical depth."
    },
    {
      "judge_key": "qwen3_coder_next",
      "judge_name": "Qwen 3 Coder Next",
      "respondent_key": "qwen35_9b",
      "respondent_name": "Qwen 3.5 9B",
      "weighted_score": 10,
      "brief_justification": "The response correctly identifies all major performance issues (correlated subqueries, redundant computation, missing indexes), proposes a highly efficient CTE+JOIN rewrite that eliminates repeated work, recommends appropriate indexes (including composite ones for aggregation), and justifies the optimization with concrete metrics and reasoning—making it factually accurate, thorough, clear, insightful, and highly actionable."
    },
    {
      "judge_key": "qwen3_coder_next",
      "judge_name": "Qwen 3 Coder Next",
      "respondent_key": "qwen3_8b",
      "respondent_name": "Qwen 3 8B",
      "weighted_score": 10,
      "brief_justification": "The response correctly eliminates correlated subqueries via pre-aggregation, moves the filter to HAVING, recommends optimal covering indexes, and explains the performance gains with precise technical reasoning—fully addressing all aspects of the optimization problem."
    },
    {
      "judge_key": "qwen3_coder_next",
      "judge_name": "Qwen 3 Coder Next",
      "respondent_key": "qwen3_32b",
      "respondent_name": "Qwen 3 32B",
      "weighted_score": 10,
      "brief_justification": "The response correctly identifies correlated subqueries as the performance bottleneck, proposes an optimal rewrite using pre-aggregation with a JOIN, explains the optimization rationale thoroughly, and recommends appropriate composite indexes—covering all aspects of the question with technical accuracy and actionable insight."
    },
    {
      "judge_key": "qwen3_coder_next",
      "judge_name": "Qwen 3 Coder Next",
      "respondent_key": "qwen35_35b_a3b",
      "respondent_name": "Qwen 3.5 35B-A3B",
      "weighted_score": 10,
      "brief_justification": "The response correctly rewrites the query using JOIN + GROUP BY to eliminate expensive correlated subqueries, proposes optimal covering indexes, explains the complexity improvement, and adds valuable advanced options like materialized views and partial indexes—all with clear, actionable, and technically sound reasoning."
    },
    {
      "judge_key": "qwen3_coder_next",
      "judge_name": "Qwen 3 Coder Next",
      "respondent_key": "qwen35_27b",
      "respondent_name": "Qwen 3.5 27B",
      "weighted_score": 10,
      "brief_justification": "The response correctly rewrites the query using JOIN + GROUP BY instead of correlated subqueries, identifies all critical indexes—including a crucial covering index—and provides a thorough, technically accurate explanation of each optimization's rationale and performance impact, including complexity analysis and I/O considerations."
    },
    {
      "judge_key": "qwen3_coder_next",
      "judge_name": "Qwen 3 Coder Next",
      "respondent_key": "qwen35_122b_a10b",
      "respondent_name": "Qwen 3.5 122B-A10B",
      "weighted_score": 10,
      "brief_justification": "The response correctly identifies correlated subqueries as the performance bottleneck, proposes an equivalent but optimized query using JOINs and GROUP BY, recommends appropriate composite indexes, and provides clear, actionable optimization rationale with performance metrics and execution flow—demonstrating deep understanding of SQL query optimization principles."
    },
    {
      "judge_key": "qwen3_coder_next",
      "judge_name": "Qwen 3 Coder Next",
      "respondent_key": "qwen35_397b_a17b",
      "respondent_name": "Qwen 3.5 397B-A17B",
      "weighted_score": 10,
      "brief_justification": "The response correctly rewrites the query to eliminate four correlated subqueries, replaces them with a single aggregated join, provides appropriate covering indexes, and explains the optimization strategy with concrete performance metrics and execution plan comparisons—demonstrating deep understanding of SQL performance principles."
    },
    {
      "judge_key": "qwen35_35b_a3b",
      "judge_name": "Qwen 3.5 35B-A3B",
      "respondent_key": "qwen3_8b",
      "respondent_name": "Qwen 3 8B",
      "weighted_score": 8.6,
      "brief_justification": "The response correctly identifies correlated subqueries as the main bottleneck and provides a valid optimization using JOIN with GROUP BY. Index recommendations are sound, though the composite index column order could vary by database. The explanation is well-structured with clear sections, though it could mention query execution plan analysis or database-specific optimizations for additional depth."
    },
    {
      "judge_key": "qwen35_35b_a3b",
      "judge_name": "Qwen 3.5 35B-A3B",
      "respondent_key": "qwen3_32b",
      "respondent_name": "Qwen 3 32B",
      "weighted_score": 9.55,
      "brief_justification": "The response correctly identifies the performance bottleneck and provides a logically sound rewrite with appropriate covering indexes. It is clear and actionable, though the specific time guarantee depends on hardware and database engine specifics."
    },
    {
      "judge_key": "qwen35_35b_a3b",
      "judge_name": "Qwen 3.5 35B-A3B",
      "respondent_key": "qwen3_coder_next",
      "respondent_name": "Qwen 3 Coder Next",
      "weighted_score": 8.8,
      "brief_justification": "The response correctly identifies correlated subqueries as the performance bottleneck and provides valid optimized queries with appropriate indexes. Minor deduction for PostgreSQL-specific LATERAL syntax without broader database compatibility notes, and the index recommendations could be more nuanced for different SQL dialects."
    },
    {
      "judge_key": "qwen35_35b_a3b",
      "judge_name": "Qwen 3.5 35B-A3B",
      "respondent_key": "qwen35_27b",
      "respondent_name": "Qwen 3.5 27B",
      "weighted_score": 8.65,
      "brief_justification": "The response correctly identifies correlated subqueries as the main bottleneck and provides accurate optimization strategies with appropriate indexes. The technical explanations of index-only scans and complexity analysis are sound, though it could benefit from mentioning EXPLAIN plan verification and database-specific considerations."
    },
    {
      "judge_key": "qwen35_35b_a3b",
      "judge_name": "Qwen 3.5 35B-A3B",
      "respondent_key": "qwen35_122b_a10b",
      "respondent_name": "Qwen 3.5 122B-A10B",
      "weighted_score": 9.2,
      "brief_justification": "The response correctly identifies correlated subqueries as the performance bottleneck and provides a valid JOIN/GROUP BY optimization. Index recommendations are appropriate and well-explained. Minor deduction for not addressing potential NULL handling in aggregates or edge cases with the LIMIT clause on sorted aggregates."
    },
    {
      "judge_key": "qwen35_27b",
      "judge_name": "Qwen 3.5 27B",
      "respondent_key": "qwen35_9b",
      "respondent_name": "Qwen 3.5 9B",
      "weighted_score": 8.55,
      "brief_justification": "The response correctly identifies correlated subqueries as the main bottleneck and provides a valid CTE-based optimization strategy with appropriate index recommendations. The technical content is accurate, well-structured, and immediately actionable with copy-pasteable SQL. Minor deductions for not discussing database-specific optimizer behaviors or covering all edge cases in the index strategy."
    },
    {
      "judge_key": "qwen35_27b",
      "judge_name": "Qwen 3.5 27B",
      "respondent_key": "qwen3_8b",
      "respondent_name": "Qwen 3 8B",
      "weighted_score": 8.55,
      "brief_justification": "The response provides a solid optimization strategy by replacing correlated subqueries with a single aggregation, which is the correct approach. The index recommendations are well-reasoned, though some SQL dialect differences exist."
    },
    {
      "judge_key": "qwen35_27b",
      "judge_name": "Qwen 3.5 27B",
      "respondent_key": "qwen3_32b",
      "respondent_name": "Qwen 3 32B",
      "weighted_score": 8.6,
      "brief_justification": "The response correctly identifies correlated subqueries as the bottleneck and provides a valid JOIN-based optimization with appropriate indexes. It could have mentioned that users without orders would be excluded by the INNER JOIN, and could have discussed index selectivity or execution plan analysis in more detail."
    },
    {
      "judge_key": "qwen35_27b",
      "judge_name": "Qwen 3.5 27B",
      "respondent_key": "qwen3_coder_next",
      "respondent_name": "Qwen 3 Coder Next",
      "weighted_score": 8.75,
      "brief_justification": "The response correctly identifies correlated subqueries as the bottleneck and provides valid optimization strategies with accurate index recommendations. Minor technical imprecision exists (e.g., covering index claims for SUM(amount) without including amount in the index), but overall the solution is sound and well-structured."
    },
    {
      "judge_key": "qwen35_27b",
      "judge_name": "Qwen 3.5 27B",
      "respondent_key": "qwen35_35b_a3b",
      "respondent_name": "Qwen 3.5 35B-A3B",
      "weighted_score": 7.8,
      "brief_justification": "The core optimization strategy (JOIN + GROUP BY replacing correlated subqueries) is correct and well-explained, but there are bugs: the materialized view query references 'created_at' which isn't in the view, and the partial index syntax with subquery in WHERE clause isn't valid in most databases. The response is well-structured and comprehensive but contains implementation errors that would cause failures."
    },
    {
      "judge_key": "qwen35_27b",
      "judge_name": "Qwen 3.5 27B",
      "respondent_key": "qwen35_122b_a10b",
      "respondent_name": "Qwen 3.5 122B-A10B",
      "weighted_score": 8.6,
      "brief_justification": "Technically accurate diagnosis of the N+1 query problem and provides a valid JOIN-based solution with appropriate indexes. Well-structured with clear before/after comparison. Could expand more on edge cases like NULL handling and database-specific behaviors."
    },
    {
      "judge_key": "qwen35_27b",
      "judge_name": "Qwen 3.5 27B",
      "respondent_key": "qwen35_397b_a17b",
      "respondent_name": "Qwen 3.5 397B-A17B",
      "weighted_score": 8.95,
      "brief_justification": "The response correctly identifies correlated subqueries as the performance bottleneck and provides a valid optimization strategy using aggregation and proper indexing. The SQL logic is sound, though there's a minor concern that the HAVING clause in the derived table filters before the user creation date check, which could theoretically differ from the original query's short-circuit evaluation order. Otherwise, the technical content is accurate and well-reasoned."
    },
    {
      "judge_key": "qwen35_122b_a10b",
      "judge_name": "Qwen 3.5 122B-A10B",
      "respondent_key": "qwen35_9b",
      "respondent_name": "Qwen 3.5 9B",
      "weighted_score": 9,
      "brief_justification": "Response provides accurate optimization strategy with correct query rewrite and appropriate indexes. Minor issue: composite index for MAX(created_at) could be optimized as (user_id, created_at) instead of including amount. Well-structured, comprehensive, and immediately actionable."
    },
    {
      "judge_key": "qwen35_122b_a10b",
      "judge_name": "Qwen 3.5 122B-A10B",
      "respondent_key": "qwen3_8b",
      "respondent_name": "Qwen 3 8B",
      "weighted_score": 8.15,
      "brief_justification": "The response correctly identifies correlated subqueries as the main bottleneck and provides a valid optimization strategy with JOIN + GROUP BY. Index recommendations are sound but could be more precise (e.g., users index could be simpler since id is PK). Well-structured and actionable, though could include query plan analysis and database-specific considerations."
    },
    {
      "judge_key": "qwen35_122b_a10b",
      "judge_name": "Qwen 3.5 122B-A10B",
      "respondent_key": "qwen3_32b",
      "respondent_name": "Qwen 3 32B",
      "weighted_score": 8.6,
      "brief_justification": "The response correctly identifies correlated subqueries as the bottleneck and provides a valid JOIN-based rewrite with sound index recommendations. Minor gaps include not mentioning that filtering users by created_at before the JOIN could further optimize, and the composite index on orders could be simplified to just user_id for the GROUP BY operation."
    },
    {
      "judge_key": "qwen35_122b_a10b",
      "judge_name": "Qwen 3.5 122B-A10B",
      "respondent_key": "qwen3_coder_next",
      "respondent_name": "Qwen 3 Coder Next",
      "weighted_score": 9.55,
      "brief_justification": "Response correctly identifies correlated subquery inefficiency and provides accurate optimization strategies with appropriate indexes. Minor deduction for not noting LATERAL is PostgreSQL-specific, but overall technically sound with excellent structure and actionable recommendations."
    },
    {
      "judge_key": "qwen35_122b_a10b",
      "judge_name": "Qwen 3.5 122B-A10B",
      "respondent_key": "qwen35_35b_a3b",
      "respondent_name": "Qwen 3.5 35B-A3B",
      "weighted_score": 8.4,
      "brief_justification": "Core optimization (replacing correlated subqueries with JOIN + GROUP BY) is correct and would achieve the performance goal. However, the materialized view has a bug (missing created_at column) and the partial index syntax with subquery is invalid in most databases, slightly reducing accuracy."
    },
    {
      "judge_key": "qwen35_122b_a10b",
      "judge_name": "Qwen 3.5 122B-A10B",
      "respondent_key": "qwen35_27b",
      "respondent_name": "Qwen 3.5 27B",
      "weighted_score": 9.8,
      "brief_justification": "The response provides a technically accurate query rewrite and index strategy that directly addresses the performance bottlenecks. The explanation clearly details the I/O and complexity improvements, making it highly actionable for a developer."
    },
    {
      "judge_key": "qwen35_122b_a10b",
      "judge_name": "Qwen 3.5 122B-A10B",
      "respondent_key": "qwen35_397b_a17b",
      "respondent_name": "Qwen 3.5 397B-A17B",
      "weighted_score": 9.35,
      "brief_justification": "The response correctly eliminates correlated subqueries with proper JOIN aggregation, suggests appropriate indexes, and provides clear execution plan comparisons. Minor deduction for not addressing database-specific variations or edge cases like NULL handling in aggregates."
    },
    {
      "judge_key": "qwen35_397b_a17b",
      "judge_name": "Qwen 3.5 397B-A17B",
      "respondent_key": "qwen35_9b",
      "respondent_name": "Qwen 3.5 9B",
      "weighted_score": 9.55,
      "brief_justification": "The response accurately diagnoses the correlated subquery bottleneck and provides a valid, significantly faster rewrite with appropriate indexing strategies. Minor deduction because aggregating all orders in a CTE before filtering users may be less efficient than filtering users first depending on the database optimizer's handling of CTE materialization."
    },
    {
      "judge_key": "qwen35_397b_a17b",
      "judge_name": "Qwen 3.5 397B-A17B",
      "respondent_key": "qwen3_8b",
      "respondent_name": "Qwen 3 8B",
      "weighted_score": 10,
      "brief_justification": "The response correctly identifies correlated subqueries as the bottleneck and provides a standard, efficient JOIN-based solution with appropriate covering indexes. The explanation is technically accurate, well-structured, and directly actionable."
    },
    {
      "judge_key": "qwen35_397b_a17b",
      "judge_name": "Qwen 3.5 397B-A17B",
      "respondent_key": "qwen3_32b",
      "respondent_name": "Qwen 3 32B",
      "weighted_score": 10,
      "brief_justification": "The response accurately diagnoses the correlated subquery bottleneck and provides a logically equivalent, optimized query with appropriate covering indexes. It clearly explains the complexity reduction and offers actionable, well-structured advice."
    },
    {
      "judge_key": "qwen35_397b_a17b",
      "judge_name": "Qwen 3.5 397B-A17B",
      "respondent_key": "qwen3_coder_next",
      "respondent_name": "Qwen 3 Coder Next",
      "weighted_score": 9.55,
      "brief_justification": "Accurately diagnoses the correlated subquery bottleneck and provides valid, optimized SQL solutions with strong indexing strategies. Minor deduction on correctness because the covering index for aggregation omits the 'amount' column required for true index-only scans on SUM."
    },
    {
      "judge_key": "qwen35_397b_a17b",
      "judge_name": "Qwen 3.5 397B-A17B",
      "respondent_key": "qwen35_35b_a3b",
      "respondent_name": "Qwen 3.5 35B-A3B",
      "weighted_score": 9.55,
      "brief_justification": "The core optimization correctly replaces correlated subqueries with JOINs and suggests appropriate covering indexes. A minor deduction for the partial index example which uses unsupported subquery syntax in most SQL dialects."
    },
    {
      "judge_key": "qwen35_397b_a17b",
      "judge_name": "Qwen 3.5 397B-A17B",
      "respondent_key": "qwen35_27b",
      "respondent_name": "Qwen 3.5 27B",
      "weighted_score": 9.55,
      "brief_justification": "The solution effectively eliminates correlated subqueries and proposes valid indexes, though recommending both a foreign key index and a superset covering index is slightly redundant. The explanation clearly articulates the performance bottlenecks and how the optimizations resolve them."
    },
    {
      "judge_key": "qwen35_397b_a17b",
      "judge_name": "Qwen 3.5 397B-A17B",
      "respondent_key": "qwen35_122b_a10b",
      "respondent_name": "Qwen 3.5 122B-A10B",
      "weighted_score": 9.8,
      "brief_justification": "The model correctly identifies the N+1 query issue and provides a logically equivalent JOIN-based solution with optimal covering indexes. The explanation is comprehensive, well-structured, and includes actionable scaling advice."
    }
  ],
  "meta": {
    "source": "The Multivac (app.themultivac.com)",
    "methodology": "10x10 blind peer matrix evaluation",
    "criteria": "correctness, completeness, clarity, depth, usefulness",
    "self_judgments": "excluded from rankings",
    "license": "Open data — cite as: The Multivac (2026)"
  }
}