{ "evaluation_id": "EVAL-20260318-161904", "question_id": "EVAL-20260318-161904", "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": "2026-03-18T16:19:04.487Z", "display_date": "Mar 18, 2026", "winner": { "name": "GPT-5.4", "provider": "openrouter", "score": 9.72 }, "avg_score": 8.27125, "matrix_size": 56, "models_used": [ { "id": "minimax_m25", "name": "MiniMax M2.5", "provider": "openrouter" }, { "id": "minimax_m21", "name": "MiniMax M2.1", "provider": "openrouter" }, { "id": "minimax_m2", "name": "MiniMax M2", "provider": "MiniMax" }, { "id": "minimax_m1", "name": "MiniMax M1", "provider": "openrouter" }, { "id": "minimax_01", "name": "MiniMax-01", "provider": "openrouter" }, { "id": "judge_claude_sonnet", "name": "Claude Sonnet 4.6", "provider": "openrouter" }, { "id": "judge_gpt54", "name": "GPT-5.4", "provider": "openrouter" }, { "id": "minimax_m27", "name": "MiniMax M2.7", "provider": "openrouter" } ], "rankings": { "judge_gpt54": { "display_name": "GPT-5.4", "provider": "openrouter", "average_score": 9.72, "score_count": 7, "min_score": 9.35, "max_score": 10, "rank": 1 }, "judge_claude_sonnet": { "display_name": "Claude Sonnet 4.6", "provider": "openrouter", "average_score": 9.66, "score_count": 7, "min_score": 7.8, "max_score": 10, "rank": 2 }, "minimax_m1": { "display_name": "MiniMax M1", "provider": "openrouter", "average_score": 9.39, "score_count": 7, "min_score": 7.2, "max_score": 10, "rank": 3 }, "minimax_m27": { "display_name": "MiniMax M2.7", "provider": "openrouter", "average_score": 9.09, "score_count": 7, "min_score": 6.95, "max_score": 10, "rank": 4 }, "minimax_m25": { "display_name": "MiniMax M2.5", "provider": "openrouter", "average_score": 8.96, "score_count": 7, "min_score": 7, "max_score": 10, "rank": 5 }, "minimax_01": { "display_name": "MiniMax-01", "provider": "openrouter", "average_score": 7.9, "score_count": 7, "min_score": 6.6, "max_score": 8.55, "rank": 6 }, "minimax_m21": { "display_name": "MiniMax M2.1", "provider": "openrouter", "average_score": 7.04, "score_count": 7, "min_score": 5.2, "max_score": 8.6, "rank": 7 }, "minimax_m2": { "display_name": "MiniMax M2", "provider": "MiniMax", "average_score": 4.41, "score_count": 7, "min_score": 2.1, "max_score": 7.6, "rank": 8 } }, "judgments": [ { "judge_key": "minimax_m27", "judge_name": "MiniMax M2.7", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 8.8, "brief_justification": "The response correctly identifies all performance bottlenecks (correlated subqueries, missing indexes, inefficient sorting) and provides a well-structured optimized solution using a CTE for pre-aggregation. Index recommendations are specific with covering index guidance. Minor deduction for depth as the response doesn't discuss query execution plan details or alternative approaches like window functions." }, { "judge_key": "minimax_m27", "judge_name": "MiniMax M2.7", "respondent_key": "minimax_m21", "respondent_name": "MiniMax M2.1", "weighted_score": 7.1, "brief_justification": "The rewritten query correctly replaces 4 correlated subqueries with a single aggregated JOIN, which is the right approach. However, the response is truncated and fails to provide the index recommendations requested, leaving the answer incomplete." }, { "judge_key": "minimax_m27", "judge_name": "MiniMax M2.7", "respondent_key": "minimax_m2", "respondent_name": "MiniMax M2", "weighted_score": 4.9, "brief_justification": "The response correctly identifies correlated subqueries as the main bottleneck but is severely incomplete—cuts off mid-sentence and never provides the rewritten optimized query or complete index recommendations, which were the core requirements." }, { "judge_key": "minimax_m27", "judge_name": "MiniMax M2.7", "respondent_key": "minimax_m1", "respondent_name": "MiniMax M1", "weighted_score": 10, "brief_justification": "Excellent response that correctly identifies the correlated subquery problem, provides an optimal JOIN-based rewrite, suggests proper covering indexes, and explains the optimization strategy with clear visual execution plan comparisons and performance metrics." }, { "judge_key": "minimax_m27", "judge_name": "MiniMax M2.7", "respondent_key": "minimax_01", "respondent_name": "MiniMax-01", "weighted_score": 8.55, "brief_justification": "The response correctly identifies correlated subqueries as the main bottleneck and provides a solid JOIN-based rewrite with appropriate index recommendations. Minor deduction: the rewritten query has a redundant IS NOT NULL condition that slightly alters semantics compared to the original, and the indexing suggestions could be consolidated into fewer composite indexes." }, { "judge_key": "minimax_m27", "judge_name": "MiniMax M2.7", "respondent_key": "judge_claude_sonnet", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 9.8, "brief_justification": "The response provides a correct, logically equivalent rewrite using CTE aggregation instead of correlated subqueries, explains the four key performance killers (correlated subqueries, WHERE subquery, missing indexes, computed ORDER BY), recommends appropriate covering indexes with detailed justifications, and includes execution plan comparisons that clearly demonstrate why the transformation from ~2M subquery executions to 1 will achieve the target of under 1 second." }, { "judge_key": "minimax_m27", "judge_name": "MiniMax M2.7", "respondent_key": "judge_gpt54", "respondent_name": "GPT-5.4", "weighted_score": 9.55, "brief_justification": "The response provides a correct rewritten query that eliminates N+4 correlated subqueries by aggregating orders once, includes practical index recommendations with both minimum and advanced covering indexes, and appropriately suggests a summary table approach for guaranteed sub-second performance at scale. The explanation is well-structured and actionable, with clear reasoning for each optimization." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "minimax_m27", "respondent_name": "MiniMax M2.7", "weighted_score": 10, "brief_justification": "Excellent response that correctly identifies the correlated subquery problem, provides a properly optimized JOIN-based solution, recommends the ideal composite index (user_id, amount, created_at), and explains the O(n×m) to O(n+m) complexity improvement with clear visual diagrams and performance comparisons." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "minimax_m21", "respondent_name": "MiniMax M2.1", "weighted_score": 6.55, "brief_justification": "The rewritten query correctly optimizes the correlated subqueries using JOIN with pre-aggregation, which is the right approach. However, the response is incomplete (cut off mid-explanation) and fails to address the index recommendations explicitly requested in the question." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "minimax_m2", "respondent_name": "MiniMax M2", "weighted_score": 4.5, "brief_justification": "The response correctly identifies correlated subqueries as the main performance issue and begins explaining the O(N·M) complexity, but it is severely incomplete - it cuts off mid-sentence and fails to provide the rewritten optimized query, complete optimization strategy, or index recommendations that were explicitly requested." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "minimax_m1", "respondent_name": "MiniMax M1", "weighted_score": 9.55, "brief_justification": "The response correctly identifies the correlated subquery problem, provides a valid optimized solution using pre-aggregation with JOIN, and suggests appropriate composite indexes. The explanation is thorough with visual diagrams, execution plan comparisons, and quantified performance improvements." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "minimax_01", "respondent_name": "MiniMax-01", "weighted_score": 8.35, "brief_justification": "The response correctly identifies correlated subqueries as the main issue and provides a valid JOIN-based rewrite. The indexing recommendations are appropriate. Minor deduction for using LEFT JOIN with IS NULL filter (inefficient) instead of INNER JOIN, and the composite index suggestions could be more targeted." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "judge_claude_sonnet", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 10, "brief_justification": "Excellent response that correctly transforms correlated subqueries into a single aggregation CTE, explains each optimization step with detailed reasoning, provides specific covering index recommendations with justification, and includes execution plan comparisons showing the dramatic performance improvement from millions of subquery executions to a single index-only scan." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "judge_gpt54", "respondent_name": "GPT-5.4", "weighted_score": 9.8, "brief_justification": "Excellent solution that correctly eliminates the N+1 correlated subquery problem with a proper JOIN and aggregation. Provides comprehensive index recommendations including covering indexes, and offers advanced options like materialized views for guaranteed sub-second performance at scale." }, { "judge_key": "minimax_m21", "judge_name": "MiniMax M2.1", "respondent_key": "minimax_m27", "respondent_name": "MiniMax M2.7", "weighted_score": 9.15, "brief_justification": "Excellent optimization solution with correctly replaced correlated subqueries using JOIN aggregation, proper index recommendations (including covering index), clear complexity analysis (O(n×m) to O(n+m)), and practical alternatives. Minor deduction: the composite index suggestion could be slightly more precise about index selectivity." }, { "judge_key": "minimax_m21", "judge_name": "MiniMax M2.1", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 8.6, "brief_justification": "The response correctly identifies all performance issues (correlated subqueries, missing indexes, inefficient sorting) and provides a valid optimized solution using a CTE for pre-aggregation. Index recommendations are appropriate and well-explained. Minor deduction for slightly incomplete index listing (truncated last row) and the optional index being cut off." }, { "judge_key": "minimax_m21", "judge_name": "MiniMax M2.1", "respondent_key": "minimax_m2", "respondent_name": "MiniMax M2", "weighted_score": 2.1, "brief_justification": "Response is severely incomplete - cuts off mid-sentence and fails to provide the optimized query, index recommendations, or complete optimization strategy. Only begins analysis of the problem." }, { "judge_key": "minimax_m21", "judge_name": "MiniMax M2.1", "respondent_key": "minimax_m1", "respondent_name": "MiniMax M1", "weighted_score": 10, "brief_justification": "Comprehensive solution with accurate problem diagnosis, correct SQL rewrite using pre-aggregation, precise index recommendations, excellent visual explanations, and quantitative performance analysis. Fully addresses all aspects of the optimization question." }, { "judge_key": "minimax_m21", "judge_name": "MiniMax M2.1", "respondent_key": "minimax_01", "respondent_name": "MiniMax-01", "weighted_score": 8.05, "brief_justification": "The rewritten query correctly replaces correlated subqueries with JOINs and aggregation. Index recommendations are appropriate but could be more prioritized. Missing some deeper technical considerations like covering indexes and execution plan analysis." }, { "judge_key": "minimax_m21", "judge_name": "MiniMax M2.1", "respondent_key": "judge_claude_sonnet", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 10, "brief_justification": "Excellent response with accurate problem diagnosis, well-structured optimized query, comprehensive index recommendations with detailed reasoning, and clear execution plan comparisons. Transforms O(n²) correlated subqueries into O(n) aggregation with covering indexes." }, { "judge_key": "minimax_m21", "judge_name": "MiniMax M2.1", "respondent_key": "judge_gpt54", "respondent_name": "GPT-5.4", "weighted_score": 10, "brief_justification": "The response provides a correct query rewrite eliminating N+1 correlated subqueries, explains the optimization strategy thoroughly, offers multiple index options from basic to covering, and suggests a materialized view for guaranteed sub-second performance at scale. Well-structured with actionable SQL and clear explanations." }, { "judge_key": "minimax_m2", "judge_name": "MiniMax M2", "respondent_key": "minimax_m27", "respondent_name": "MiniMax M2.7", "weighted_score": 9.4, "brief_justification": "Excellent optimization that correctly replaces 4 correlated subqueries with a single aggregation pass. Provides comprehensive index recommendations with clear explanations, addresses complexity differences (O(n×m) vs O(n+m)), and offers practical alternatives. Minor deduction for completeness because the LIMIT without ORDER BY in the subquery could theoretically return different users than the original, though the final ORDER BY fixes this." }, { "judge_key": "minimax_m2", "judge_name": "MiniMax M2", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 10, "brief_justification": "Excellent response that correctly identifies correlated subqueries as the root cause, provides a properly optimized query using aggregation/CTE, explains the logic clearly, and recommends specific covering indexes with column order explanations. The before/after comparison table enhances clarity. Note: Response appears truncated at the end but core content is complete." }, { "judge_key": "minimax_m2", "judge_name": "MiniMax M2", "respondent_key": "minimax_m21", "respondent_name": "MiniMax M2.1", "weighted_score": 7.85, "brief_justification": "The rewritten query correctly replaces 4 correlated subqueries with a single JOIN to a derived table, which will dramatically improve performance. However, the response is incomplete (the problem analysis is cut off, index recommendations are partial), and misses mentioning specific index definitions beyond generic mentions." }, { "judge_key": "minimax_m2", "judge_name": "MiniMax M2", "respondent_key": "minimax_m1", "respondent_name": "MiniMax M1", "weighted_score": 9.8, "brief_justification": "Excellent response that correctly identifies correlated subquery issues, provides optimized query with proper JOIN and pre-aggregation, recommends appropriate indexes, and explains execution plans with quantified performance improvements." }, { "judge_key": "minimax_m2", "judge_name": "MiniMax M2", "respondent_key": "minimax_01", "respondent_name": "MiniMax-01", "weighted_score": 8.4, "brief_justification": "The response correctly identifies correlated subqueries as the main issue and provides a valid JOIN-based solution. The index recommendations are comprehensive but somewhat excessive. Minor issue: using LEFT JOIN with IS NOT NULL is awkward when INNER JOIN would suffice." }, { "judge_key": "minimax_m2", "judge_name": "MiniMax M2", "respondent_key": "judge_claude_sonnet", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 10, "brief_justification": "Excellent response that correctly transforms correlated subqueries into a CTE with JOIN, proposes appropriate covering indexes, and explains the optimization strategy with execution plan comparisons." }, { "judge_key": "minimax_m2", "judge_name": "MiniMax M2", "respondent_key": "judge_gpt54", "respondent_name": "GPT-5.4", "weighted_score": 9.55, "brief_justification": "Excellent response that correctly identifies the N×4 correlated subquery problem and provides a proper JOIN-based rewrite. Thoroughly covers indexing strategies at multiple levels (basic to advanced), explains WHY each optimization works, and even provides a materialized view approach for guaranteed sub-second performance at scale. Clear structure with actionable code." }, { "judge_key": "minimax_m1", "judge_name": "MiniMax M1", "respondent_key": "minimax_m27", "respondent_name": "MiniMax M2.7", "weighted_score": 9.8, "brief_justification": "Excellent response that correctly identifies the correlated subquery problem and provides a well-optimized JOIN-based solution with appropriate composite indexes. Covers multiple approaches with clear explanations, though minor improvement possible by noting the subquery could benefit from its own ORDER BY for true top-N optimization." }, { "judge_key": "minimax_m1", "judge_name": "MiniMax M1", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 9.55, "brief_justification": "Excellent response that correctly identifies the correlated subquery problem, provides a sound CTE-based optimization, and recommends specific covering indexes. Minor扣分 for not addressing potential edge cases with the HAVING clause placement and not mentioning potential alternative approaches like materialized views." }, { "judge_key": "minimax_m1", "judge_name": "MiniMax M1", "respondent_key": "minimax_m21", "respondent_name": "MiniMax M2.1", "weighted_score": 8.25, "brief_justification": "The rewritten query correctly eliminates correlated subqueries and is functionally equivalent. The optimization strategy is sound, explaining the O(n²) problem and how JOIN with pre-aggregation solves it. However, it lacks specific index recommendations (e.g., covering index on orders(user_id, amount, created_at)) and deeper analysis of trade-offs." }, { "judge_key": "minimax_m1", "judge_name": "MiniMax M1", "respondent_key": "minimax_m2", "respondent_name": "MiniMax M2", "weighted_score": 4.3, "brief_justification": "Response correctly identifies correlated subqueries as the problem but is truncated mid-explanation and fails to provide the actual optimized query or index recommendations requested." }, { "judge_key": "minimax_m1", "judge_name": "MiniMax M1", "respondent_key": "minimax_01", "respondent_name": "MiniMax-01", "weighted_score": 7.35, "brief_justification": "The rewritten query has logical issues - the WHERE clause with 'o.id IS NOT NULL' before GROUP BY can cause incorrect results. The indexing suggestions are reasonable but could be more targeted (e.g., covering indexes). While the explanation is clear and addresses the core N+1 problem, the optimized query may not work correctly in all cases." }, { "judge_key": "minimax_m1", "judge_name": "MiniMax M1", "respondent_key": "judge_claude_sonnet", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 10, "brief_justification": "Excellent solution that correctly transforms 4 correlated subqueries into a single CTE aggregation with proper JOIN semantics. Provides comprehensive index recommendations with covering index strategy, execution plan analysis, and clear explanations of why each optimization works. The response is thorough, well-structured, and highly actionable." }, { "judge_key": "minimax_m1", "judge_name": "MiniMax M1", "respondent_key": "judge_gpt54", "respondent_name": "GPT-5.4", "weighted_score": 10, "brief_justification": "Excellent response that correctly identifies the N+1 query problem, provides a logically equivalent rewrite using aggregation, suggests appropriate indexes including covering indexes, and offers multiple optimization strategies from basic to production-grade materialized views." }, { "judge_key": "minimax_01", "judge_name": "MiniMax-01", "respondent_key": "minimax_m27", "respondent_name": "MiniMax M2.7", "weighted_score": 9.8, "brief_justification": "The response provides a comprehensive and accurate optimization strategy, including a rewritten query, index recommendations, and clear explanations of the performance improvements. However, it could include slightly more depth on the trade-offs of alternative approaches." }, { "judge_key": "minimax_01", "judge_name": "MiniMax-01", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 9.6, "brief_justification": "The response provides a comprehensive and accurate explanation of the performance issues in the original query, proposes a highly optimized solution, and recommends specific indexes with clear justifications. Minor clarity improvements could be made in the indexing section." }, { "judge_key": "minimax_01", "judge_name": "MiniMax-01", "respondent_key": "minimax_m21", "respondent_name": "MiniMax M2.1", "weighted_score": 8.6, "brief_justification": "The rewritten query is accurate and optimized by eliminating correlated subqueries, but it lacks a detailed explanation of indexing strategies and performance implications." }, { "judge_key": "minimax_01", "judge_name": "MiniMax-01", "respondent_key": "minimax_m2", "respondent_name": "MiniMax M2", "weighted_score": 7.6, "brief_justification": "The response correctly identifies the performance issue with correlated sub-queries and explains the high computational cost. However, it does not provide the full rewritten query or specify the exact indexes to add for optimization." }, { "judge_key": "minimax_01", "judge_name": "MiniMax-01", "respondent_key": "minimax_m1", "respondent_name": "MiniMax M1", "weighted_score": 9.8, "brief_justification": "The response provides a comprehensive and accurate solution, including a clear explanation of the optimization strategy, a well-structured optimized query, and a detailed index strategy. However, it could have further elaborated on the trade-offs or edge cases related to the optimization." }, { "judge_key": "minimax_01", "judge_name": "MiniMax-01", "respondent_key": "judge_claude_sonnet", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 10, "brief_justification": "The response provides a comprehensive and accurate optimization of the SQL query, including a detailed explanation of the strategy, appropriate index recommendations, and a clear comparison of execution plans before and after optimization." }, { "judge_key": "minimax_01", "judge_name": "MiniMax-01", "respondent_key": "judge_gpt54", "respondent_name": "GPT-5.4", "weighted_score": 9.8, "brief_justification": "The response provides a comprehensive and technically accurate optimization strategy, including query rewrites, index recommendations, and advanced solutions like summary tables. It is well-structured and detailed, with only minor improvements needed in flow for perfect clarity." }, { "judge_key": "judge_claude_sonnet", "judge_name": "Claude Sonnet 4.6", "respondent_key": "minimax_m27", "respondent_name": "MiniMax M2.7", "weighted_score": 8.55, "brief_justification": "The response correctly identifies the core problem (correlated subqueries causing O(n×m) complexity) and provides a solid optimized query using a derived table with GROUP BY. The index recommendations are practical and well-explained. Minor issues: the window function alternative has a bug (ROW_NUMBER with SUM in OVER clause is invalid SQL), and the claim of 'constant time' scalability is inaccurate—it's still O(n+m). The main optimization strategy is sound and actionable." }, { "judge_key": "judge_claude_sonnet", "judge_name": "Claude Sonnet 4.6", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 9.2, "brief_justification": "The response correctly identifies all performance issues (correlated subqueries, missing indexes, redundant scans), provides a well-structured optimized query using a CTE with proper HAVING clause, and recommends appropriate covering indexes with clear explanations. Minor gap: the response is slightly cut off at the end, and doesn't mention potential trade-offs like the CTE scanning all orders vs. filtering users first, or database-specific optimizations." }, { "judge_key": "judge_claude_sonnet", "judge_name": "Claude Sonnet 4.6", "respondent_key": "minimax_m21", "respondent_name": "MiniMax M2.1", "weighted_score": 5.7, "brief_justification": "The rewritten query is correct and addresses the core correlated subquery problem by converting to a JOIN with aggregation, which is the right approach. However, the response appears truncated - it cuts off mid-explanation and never addresses the critical question about which indexes to add, making it significantly incomplete." }, { "judge_key": "judge_claude_sonnet", "judge_name": "Claude Sonnet 4.6", "respondent_key": "minimax_m2", "respondent_name": "MiniMax M2", "weighted_score": 5, "brief_justification": "The response correctly identifies the correlated subquery problem but appears to be cut off mid-explanation, missing the rewritten query, index recommendations, and optimization strategy that were explicitly requested. The table format is a good start but the answer is fundamentally incomplete." }, { "judge_key": "judge_claude_sonnet", "judge_name": "Claude Sonnet 4.6", "respondent_key": "minimax_m1", "respondent_name": "MiniMax M1", "weighted_score": 9.35, "brief_justification": "The response correctly identifies all performance issues, provides a well-optimized rewrite using a single aggregation JOIN instead of correlated subqueries, and recommends appropriate covering indexes. The execution plan comparison and performance metrics table add excellent practical context, with only minor nitpicks like the subquery aggregating all orders rather than pre-filtering by users.created_at first for potential further optimization." }, { "judge_key": "judge_claude_sonnet", "judge_name": "Claude Sonnet 4.6", "respondent_key": "minimax_01", "respondent_name": "MiniMax-01", "weighted_score": 8, "brief_justification": "The response correctly identifies the core problem (correlated subqueries) and provides a solid rewrite using JOIN with GROUP BY/HAVING. The indexing recommendations are reasonable, though slightly over-specified with redundant composite indexes. One minor issue: using LEFT JOIN with o.id IS NOT NULL is functionally equivalent to INNER JOIN but less clear; an INNER JOIN would be more direct. The response could go deeper on covering indexes (e.g., a single composite index on orders(user_id, amoun" }, { "judge_key": "judge_claude_sonnet", "judge_name": "Claude Sonnet 4.6", "respondent_key": "judge_gpt54", "respondent_name": "GPT-5.4", "weighted_score": 9.35, "brief_justification": "The response correctly identifies the core problem (4 correlated subqueries causing repeated table scans), provides a well-structured rewrite using a single aggregation JOIN, recommends appropriate indexes including a covering index, and goes beyond the question with advanced strategies like materialized views and user-first filtering. Minor note: the 'filter users first' variant adds a redundant join inside the subquery that some optimizers may not handle well, but overall the advice is sound a" }, { "judge_key": "judge_gpt54", "judge_name": "GPT-5.4", "respondent_key": "minimax_m27", "respondent_name": "MiniMax M2.7", "weighted_score": 6.95, "brief_justification": "The rewrite correctly removes correlated subqueries and suggests sensible indexes, but it overstates guarantees like '< 1 second', mislabels the issue as a cartesian product, and the window-function alternative is flawed/inefficient. It is clear and practical overall, though it misses stronger optimizations such as pre-filtering users before aggregating orders or using a materialized summary when strict sub-second latency is required." }, { "judge_key": "judge_gpt54", "judge_name": "GPT-5.4", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 7, "brief_justification": "The rewrite correctly removes correlated subqueries and adds the key missing indexes, but it overstates that the sort can use an index and that this alone guarantees sub-second runtime; grouping all 10M orders may still be expensive. The answer is clear and practical, though slightly incomplete/truncated and missing discussion of alternatives like filtering users first before aggregating relevant orders." }, { "judge_key": "judge_gpt54", "judge_name": "GPT-5.4", "respondent_key": "minimax_m21", "respondent_name": "MiniMax M2.1", "weighted_score": 5.2, "brief_justification": "The rewrite correctly removes repeated correlated subqueries by pre-aggregating orders and joining, which is a sound optimization. However, the response is incomplete: it cuts off mid-explanation, does not address the required indexes, and does not justify how the query would realistically reach under 1 second." }, { "judge_key": "judge_gpt54", "judge_name": "GPT-5.4", "respondent_key": "minimax_m2", "respondent_name": "MiniMax M2", "weighted_score": 2.5, "brief_justification": "The response is truncated and does not actually provide a rewritten query, indexing recommendations, or a full optimization strategy. What is present is broadly correct about correlated subqueries being expensive, but it is incomplete and not actionable." }, { "judge_key": "judge_gpt54", "judge_name": "GPT-5.4", "respondent_key": "minimax_m1", "respondent_name": "MiniMax M1", "weighted_score": 7.2, "brief_justification": "The rewrite correctly removes correlated subqueries and proposes helpful indexes, but it overstates some claims: the original is not necessarily 4 full table scans per user in all engines, and the rewritten query may still aggregate all orders before filtering recent users, so the <1s guarantee is not justified. Clear structure and practical suggestions make it useful, though a better plan would discuss filtering users first and more precise indexing/tradeoffs." }, { "judge_key": "judge_gpt54", "judge_name": "GPT-5.4", "respondent_key": "minimax_01", "respondent_name": "MiniMax-01", "weighted_score": 6.6, "brief_justification": "The response correctly identifies correlated subqueries as the main issue and rewrites the query using aggregation, but it misses stronger optimization patterns like pre-aggregating orders only once and joining to filtered users first. The indexing advice is partially redundant/suboptimal, and the LEFT JOIN plus o.id IS NOT NULL is effectively an INNER JOIN without discussing whether the plan can realistically achieve sub-second performance." }, { "judge_key": "judge_gpt54", "judge_name": "GPT-5.4", "respondent_key": "judge_claude_sonnet", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 7.8, "brief_justification": "The rewrite correctly removes correlated subqueries via pre-aggregation and join, and the explanation is clear and well-structured. However, some claims are overstated or engine-specific: a CTE may not always help, the proposed covering index order is questionable for SUM/MAX efficiency, the partial index on a fixed date is impractical, and guaranteeing sub-1-second performance is not justified from the information given." } ], "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)" } }