{ "evaluation_id": "EVAL-20260315-034158", "question_id": "EVAL-20260315-034158", "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": "2026-03-15T03:41:58.833Z", "display_date": "Mar 15, 2026", "winner": { "name": "Qwen 3 32B", "provider": "openrouter", "score": 9.66 }, "avg_score": 8.704, "matrix_size": 76, "models_used": [ { "id": "devstral", "name": "Devstral Small", "provider": "openrouter" }, { "id": "gemma3_27b", "name": "Gemma 3 27B", "provider": "openrouter" }, { "id": "llama4_scout", "name": "Llama 4 Scout", "provider": "openrouter" }, { "id": "phi4", "name": "Phi-4 14B", "provider": "openrouter" }, { "id": "granite_40", "name": "Granite 4.0 Micro", "provider": "openrouter" }, { "id": "qwen3_8b", "name": "Qwen 3 8B", "provider": "openrouter" }, { "id": "mistral_nemo", "name": "Mistral Nemo 12B", "provider": "openrouter" }, { "id": "llama31_8b", "name": "Llama 3.1 8B", "provider": "openrouter" }, { "id": "qwen3_32b", "name": "Qwen 3 32B", "provider": "openrouter" }, { "id": "kimi_k25", "name": "Kimi K2.5", "provider": "openrouter" } ], "rankings": { "qwen3_8b": { "display_name": "Qwen 3 8B", "provider": "openrouter", "average_score": 9.66, "score_count": 7, "min_score": 8.8, "max_score": 10, "rank": 1 }, "qwen3_32b": { "display_name": "Qwen 3 32B", "provider": "openrouter", "average_score": 9.66, "score_count": 8, "min_score": 8.8, "max_score": 10, "rank": 2 }, "gemma3_27b": { "display_name": "Gemma 3 27B", "provider": "openrouter", "average_score": 9.57, "score_count": 8, "min_score": 8.8, "max_score": 10, "rank": 3 }, "mistral_nemo": { "display_name": "Mistral Nemo 12B", "provider": "openrouter", "average_score": 9.17, "score_count": 8, "min_score": 8.6, "max_score": 9.8, "rank": 4 }, "devstral": { "display_name": "Devstral Small", "provider": "openrouter", "average_score": 9.03, "score_count": 8, "min_score": 7.75, "max_score": 9.8, "rank": 5 }, "granite_40": { "display_name": "Granite 4.0 Micro", "provider": "openrouter", "average_score": 8.91, "score_count": 8, "min_score": 8.35, "max_score": 9.8, "rank": 6 }, "llama4_scout": { "display_name": "Llama 4 Scout", "provider": "openrouter", "average_score": 8.8, "score_count": 8, "min_score": 8, "max_score": 9.6, "rank": 7 }, "phi4": { "display_name": "Phi-4 14B", "provider": "openrouter", "average_score": 8.54, "score_count": 9, "min_score": 6.2, "max_score": 9.6, "rank": 8 }, "llama31_8b": { "display_name": "Llama 3.1 8B", "provider": "openrouter", "average_score": 8.12, "score_count": 9, "min_score": 4, "max_score": 9.2, "rank": 9 }, "kimi_k25": { "display_name": "Kimi K2.5", "provider": "openrouter", "average_score": 5.57, "score_count": 3, "min_score": 1, "max_score": 8.15, "rank": 10 } }, "judgments": [ { "judge_key": "qwen3_32b", "judge_name": "Qwen 3 32B", "respondent_key": "devstral", "respondent_name": "Devstral Small", "weighted_score": 7.75, "brief_justification": "The response correctly identifies subquery inefficiencies and proposes a CTE with aggregation. However, the suggested indexes on `orders.user_id` and `orders.created_at` alone may not fully optimize the query. A composite index on `orders (user_id, amount, created_at)` would better support the aggregations, improving correctness and depth. The explanation is clear and actionable, but lacks this nuance." }, { "judge_key": "qwen3_32b", "judge_name": "Qwen 3 32B", "respondent_key": "gemma3_27b", "respondent_name": "Gemma 3 27B", "weighted_score": 9.15, "brief_justification": "The response correctly identifies correlated subqueries as the primary bottleneck and replaces them with optimized joins and aggregation. It provides a well-structured rewritten query, explains index strategies (composite index on orders and users), and addresses performance considerations. Minor redundancy in the 'order_maxes' subquery slightly reduces completeness, but the explanation remains thorough and actionable." }, { "judge_key": "qwen3_32b", "judge_name": "Qwen 3 32B", "respondent_key": "llama4_scout", "respondent_name": "Llama 4 Scout", "weighted_score": 8, "brief_justification": "The response correctly replaces correlated subqueries with joins and aggregation, which is the primary optimization. However, the GROUP BY clause includes redundant columns (name, email) that could be omitted if functionally dependent on user_id. Index recommendations are valid but miss an index on orders.amount for the SUM operation. The explanation is practical but could elaborate more on query execution plan implications." }, { "judge_key": "qwen3_32b", "judge_name": "Qwen 3 32B", "respondent_key": "phi4", "respondent_name": "Phi-4 14B", "weighted_score": 8.4, "brief_justification": "The response correctly identifies correlated subqueries as the performance bottleneck and proposes an optimized JOIN-based approach with appropriate indexes. It explains the rationale clearly but could improve by mentioning composite indexes for the orders table's aggregation (user_id, amount, created_at) to further accelerate the query." }, { "judge_key": "qwen3_32b", "judge_name": "Qwen 3 32B", "respondent_key": "granite_40", "respondent_name": "Granite 4.0 Micro", "weighted_score": 9, "brief_justification": "The response correctly identifies the correlated subquery bottleneck and offers a well-structured optimization strategy with a JOIN-based solution. It provides appropriate index recommendations and explains the performance benefits. The rewritten query is logically sound, though the output equivalence could be more explicitly addressed." }, { "judge_key": "qwen3_32b", "judge_name": "Qwen 3 32B", "respondent_key": "qwen3_8b", "respondent_name": "Qwen 3 8B", "weighted_score": 9.6, "brief_justification": "The response correctly eliminates correlated subqueries with a pre-aggregated join, suggests optimal indexes for filtering and aggregation, and explains the performance impact. The strategy is thorough, clear, and actionable for achieving sub-second execution." }, { "judge_key": "qwen3_32b", "judge_name": "Qwen 3 32B", "respondent_key": "mistral_nemo", "respondent_name": "Mistral Nemo 12B", "weighted_score": 8.6, "brief_justification": "The response correctly identifies the inefficiency of correlated subqueries and provides a well-structured optimization using CTEs and joins. It also suggests appropriate indexes for performance improvement. However, it could further explain why the suggested composite index is effective and consider potential edge cases like data distribution." }, { "judge_key": "qwen3_32b", "judge_name": "Qwen 3 32B", "respondent_key": "llama31_8b", "respondent_name": "Llama 3.1 8B", "weighted_score": 8, "brief_justification": "The response correctly identifies correlated subqueries as the performance bottleneck and rewrites the query with aggregation and joins. It recommends appropriate indexes on orders.user_id and orders.created_at but misses an index on users.created_at in the WHERE clause. The explanation is clear but lacks deeper analysis of why the original query was slow and how the new indexes specifically accelerate execution." }, { "judge_key": "kimi_k25", "judge_name": "Kimi K2.5", "respondent_key": "phi4", "respondent_name": "Phi-4 14B", "weighted_score": 6.2, "brief_justification": "The response correctly identifies the correlated subquery problem but proposes aggregating the entire orders table (10M rows) regardless of the date filter, which is inefficient if the filter is selective. It also misses critical covering indexes (e.g., orders(user_id, amount, created_at)) necessary for sub-second performance, and fails to consider filtering users first to minimize the aggregation scope." }, { "judge_key": "kimi_k25", "judge_name": "Kimi K2.5", "respondent_key": "llama31_8b", "respondent_name": "Llama 3.1 8B", "weighted_score": 4, "brief_justification": "The rewritten query contains a critical logic error: it filters orders by created_at > '2024-01-01' instead of filtering users, changing the semantics (it counts only recent orders rather than all orders for recent users). It also incorrectly suggests indexing users(id) which is already the primary key, and misses covering index opportunities like orders(user_id, amount, created_at)." }, { "judge_key": "devstral", "judge_name": "Devstral Small", "respondent_key": "qwen3_32b", "respondent_name": "Qwen 3 32B", "weighted_score": 10, "brief_justification": "The response provides a thorough, accurate, and clear explanation of the optimization strategy, including a rewritten query and detailed index recommendations." }, { "judge_key": "devstral", "judge_name": "Devstral Small", "respondent_key": "gemma3_27b", "respondent_name": "Gemma 3 27B", "weighted_score": 10, "brief_justification": "The response provides a comprehensive, accurate, and clear explanation of the optimization strategy, including detailed index recommendations and expected performance improvements." }, { "judge_key": "devstral", "judge_name": "Devstral Small", "respondent_key": "llama4_scout", "respondent_name": "Llama 4 Scout", "weighted_score": 8.6, "brief_justification": "The response provides a clear and thorough explanation of the optimization strategy, including the use of joins and indexes, and offers a rewritten query that should significantly improve performance. The index recommendations are also well-justified." }, { "judge_key": "devstral", "judge_name": "Devstral Small", "respondent_key": "phi4", "respondent_name": "Phi-4 14B", "weighted_score": 9.6, "brief_justification": "The response provides a thorough and accurate explanation of the optimization strategy, including the creation of indexes and restructuring the query to use JOINs and aggregation instead of correlated subqueries. The optimized query is well-explained and should significantly improve performance." }, { "judge_key": "devstral", "judge_name": "Devstral Small", "respondent_key": "granite_40", "respondent_name": "Granite 4.0 Micro", "weighted_score": 9.8, "brief_justification": "The response provides a thorough and accurate explanation of the optimization strategy, including detailed steps and a rewritten query. It covers all necessary aspects and offers practical advice." }, { "judge_key": "devstral", "judge_name": "Devstral Small", "respondent_key": "qwen3_8b", "respondent_name": "Qwen 3 8B", "weighted_score": 10, "brief_justification": "The response provides a thorough, clear, and actionable explanation of the optimization strategy, including the rewritten query and necessary indexes, with a detailed breakdown of the performance impact." }, { "judge_key": "devstral", "judge_name": "Devstral Small", "respondent_key": "mistral_nemo", "respondent_name": "Mistral Nemo 12B", "weighted_score": 9.6, "brief_justification": "The response provides a thorough and accurate explanation of the optimization strategy, including a well-structured query rewrite and specific index recommendations." }, { "judge_key": "devstral", "judge_name": "Devstral Small", "respondent_key": "llama31_8b", "respondent_name": "Llama 3.1 8B", "weighted_score": 8.6, "brief_justification": "The response provides a well-optimized query with clear explanations and relevant indexing recommendations, but could improve clarity in the rewritten query explanation." }, { "judge_key": "gemma3_27b", "judge_name": "Gemma 3 27B", "respondent_key": "qwen3_32b", "respondent_name": "Qwen 3 32B", "weighted_score": 9.8, "brief_justification": "The response provides a correct and well-explained optimization strategy, including a rewritten query and appropriate index suggestions. The explanation of why the changes improve performance is thorough and insightful." }, { "judge_key": "gemma3_27b", "judge_name": "Gemma 3 27B", "respondent_key": "kimi_k25", "respondent_name": "Kimi K2.5", "weighted_score": 1, "brief_justification": "The response is empty, providing no solution or explanation to the query optimization problem. It fails to address any aspect of the question." }, { "judge_key": "gemma3_27b", "judge_name": "Gemma 3 27B", "respondent_key": "devstral", "respondent_name": "Devstral Small", "weighted_score": 9.6, "brief_justification": "The response correctly identifies the performance bottlenecks and provides a well-explained, optimized query. The suggested indexes are appropriate, and the explanation of each step is clear and concise." }, { "judge_key": "gemma3_27b", "judge_name": "Gemma 3 27B", "respondent_key": "llama4_scout", "respondent_name": "Llama 4 Scout", "weighted_score": 8.95, "brief_justification": "The response correctly identifies the performance bottlenecks and provides a well-explained, optimized query. The index recommendations are appropriate and the rationale is sound. The initial rewrite with the CTE is a good step, and the final query is even better." }, { "judge_key": "gemma3_27b", "judge_name": "Gemma 3 27B", "respondent_key": "phi4", "respondent_name": "Phi-4 14B", "weighted_score": 9.4, "brief_justification": "The response correctly identifies the performance bottleneck (correlated subqueries) and provides a well-explained, effective solution using JOINs and appropriate indexing. The explanation is clear and the optimized query is logically sound." }, { "judge_key": "gemma3_27b", "judge_name": "Gemma 3 27B", "respondent_key": "granite_40", "respondent_name": "Granite 4.0 Micro", "weighted_score": 8.95, "brief_justification": "The response provides a comprehensive and accurate optimization strategy, including identifying bottlenecks, rewriting the query, suggesting appropriate indexes, and explaining the reasoning behind each step. It also includes useful additional tips for further performance tuning." }, { "judge_key": "gemma3_27b", "judge_name": "Gemma 3 27B", "respondent_key": "qwen3_8b", "respondent_name": "Qwen 3 8B", "weighted_score": 9.8, "brief_justification": "The response provides a correct and well-explained optimization strategy, including a rewritten query and a clear justification for the index choices. The explanation of time complexity is also insightful." }, { "judge_key": "gemma3_27b", "judge_name": "Gemma 3 27B", "respondent_key": "mistral_nemo", "respondent_name": "Mistral Nemo 12B", "weighted_score": 9.8, "brief_justification": "The response provides a correct and well-explained optimization strategy, including a rewritten query and relevant index suggestions. The explanation of *why* the changes improve performance is clear and concise." }, { "judge_key": "gemma3_27b", "judge_name": "Gemma 3 27B", "respondent_key": "llama31_8b", "respondent_name": "Llama 3.1 8B", "weighted_score": 8.4, "brief_justification": "The response correctly identifies the performance bottleneck (correlated subqueries) and provides a good rewrite using joins and aggregation. The indexing recommendations are relevant and helpful, though it redundantly suggests an index on `users(id)` which already exists as a primary key." }, { "judge_key": "llama4_scout", "judge_name": "Llama 4 Scout", "respondent_key": "qwen3_32b", "respondent_name": "Qwen 3 32B", "weighted_score": 9.8, "brief_justification": "The response accurately optimizes the SQL query by replacing correlated subqueries with a JOIN and adding necessary indexes. The explanation is clear, detailed, and provides a comprehensive understanding of the optimization strategy." }, { "judge_key": "llama4_scout", "judge_name": "Llama 4 Scout", "respondent_key": "devstral", "respondent_name": "Devstral Small", "weighted_score": 9.6, "brief_justification": "The response accurately optimizes the SQL query by avoiding subqueries, using aggregation and GROUP BY, and adding necessary indexes. The explanation is clear and provides practical value for improving query performance." }, { "judge_key": "llama4_scout", "judge_name": "Llama 4 Scout", "respondent_key": "gemma3_27b", "respondent_name": "Gemma 3 27B", "weighted_score": 9.8, "brief_justification": "The response provides a comprehensive optimization strategy, including rewriting the SQL query, adding necessary indexes, and explaining the reasoning behind these changes. The approach is thorough and practical." }, { "judge_key": "llama4_scout", "judge_name": "Llama 4 Scout", "respondent_key": "phi4", "respondent_name": "Phi-4 14B", "weighted_score": 8.8, "brief_justification": "The response effectively optimizes the given SQL query by restructuring it to use JOINs and aggregation, and by suggesting appropriate indexes, thus significantly improving its performance." }, { "judge_key": "llama4_scout", "judge_name": "Llama 4 Scout", "respondent_key": "granite_40", "respondent_name": "Granite 4.0 Micro", "weighted_score": 8.8, "brief_justification": "The response provides a thorough analysis of the query optimization, identifying bottlenecks, proposing an optimized query, and suggesting additional indexes. The explanation is clear and provides actionable advice for improving query performance." }, { "judge_key": "llama4_scout", "judge_name": "Llama 4 Scout", "respondent_key": "qwen3_8b", "respondent_name": "Qwen 3 8B", "weighted_score": 9.8, "brief_justification": "The response provides a thorough optimization strategy, including rewriting the SQL query to eliminate correlated subqueries and suggesting additional indexes to improve performance. The explanation is clear and provides a detailed analysis of the performance impact." }, { "judge_key": "llama4_scout", "judge_name": "Llama 4 Scout", "respondent_key": "mistral_nemo", "respondent_name": "Mistral Nemo 12B", "weighted_score": 9.6, "brief_justification": "The response accurately identifies the performance bottleneck in the original query and provides a well-structured optimization strategy. The proposed solution effectively reduces the number of database operations and suggests relevant indexes to further improve performance." }, { "judge_key": "llama4_scout", "judge_name": "Llama 4 Scout", "respondent_key": "llama31_8b", "respondent_name": "Llama 3.1 8B", "weighted_score": 8.4, "brief_justification": "The response provides a clear optimization strategy, correctly identifies the issues with the original query, and proposes a rewritten query with indexing recommendations. However, some details such as the exact index creation syntax and additional indexes on the users table are not fully addressed." }, { "judge_key": "phi4", "judge_name": "Phi-4 14B", "respondent_key": "qwen3_32b", "respondent_name": "Qwen 3 32B", "weighted_score": 9.8, "brief_justification": "The response correctly identifies the inefficiencies in the original SQL query and provides a comprehensive strategy for optimization, including rewriting the query to use JOINs and adding indexes to improve performance. The explanation is clear, detailed, and practical, offering actionable insights for optimizing SQL queries on large datasets." }, { "judge_key": "phi4", "judge_name": "Phi-4 14B", "respondent_key": "kimi_k25", "respondent_name": "Kimi K2.5", "weighted_score": 8.15, "brief_justification": "The response correctly identifies the need for additional indexes to optimize the query, suggesting indexes on 'user_id' and 'created_at' in the 'orders' table, which would improve performance. The explanation is clear and actionable, though it lacks complete coverage of all potential optimizations like materialized views or query restructuring." }, { "judge_key": "phi4", "judge_name": "Phi-4 14B", "respondent_key": "devstral", "respondent_name": "Devstral Small", "weighted_score": 9.8, "brief_justification": "The response accurately identifies and addresses inefficiencies in the original query, providing a clear and detailed explanation of the optimization strategy, including the creation of necessary indexes and the use of a CTE for aggregation. The explanation is thorough and practical, making it highly useful." }, { "judge_key": "phi4", "judge_name": "Phi-4 14B", "respondent_key": "gemma3_27b", "respondent_name": "Gemma 3 27B", "weighted_score": 10, "brief_justification": "The response accurately identifies the performance issues caused by correlated subqueries and provides a comprehensive optimization strategy by rewriting the query using joins and aggregations. It explains the rationale behind adding specific indexes and their expected impact on performance, covering all aspects of the optimization process. The explanation is clear and structured, offering practical insights into improving query efficiency." }, { "judge_key": "phi4", "judge_name": "Phi-4 14B", "respondent_key": "llama4_scout", "respondent_name": "Llama 4 Scout", "weighted_score": 8.6, "brief_justification": "The response correctly identifies and addresses the inefficiencies in the original query, providing a well-optimized version and relevant index recommendations. The explanation is clear and thorough, though it could include more details on why certain decisions were made for completeness." }, { "judge_key": "phi4", "judge_name": "Phi-4 14B", "respondent_key": "granite_40", "respondent_name": "Granite 4.0 Micro", "weighted_score": 8.8, "brief_justification": "The response correctly identifies the inefficiencies in the original query and provides a detailed strategy for optimization, including rewriting the query and adding indexes. It offers practical and actionable advice for improving performance, though it assumes standard database configurations and does not account for specific environment variations." }, { "judge_key": "phi4", "judge_name": "Phi-4 14B", "respondent_key": "qwen3_8b", "respondent_name": "Qwen 3 8B", "weighted_score": 10, "brief_justification": "The response effectively identifies and addresses the inefficiencies in the original SQL query by eliminating correlated subqueries and introducing a covering index to optimize performance. The explanation is clear, complete, and provides practical insights into improving query execution time." }, { "judge_key": "phi4", "judge_name": "Phi-4 14B", "respondent_key": "mistral_nemo", "respondent_name": "Mistral Nemo 12B", "weighted_score": 9.8, "brief_justification": "The response provides a correct and comprehensive optimization strategy, clearly explaining the use of CTEs and indexes. It offers practical insights and actionable steps, with a minor deduction in depth for not exploring alternative optimization techniques." }, { "judge_key": "phi4", "judge_name": "Phi-4 14B", "respondent_key": "llama31_8b", "respondent_name": "Llama 3.1 8B", "weighted_score": 9.2, "brief_justification": "The response correctly identifies the inefficiencies in the original query and provides a detailed optimization strategy, including index creation and query restructuring. It effectively explains how these changes improve performance, aiming to achieve the goal of running the query in under 1 second. The response could be slightly more complete by discussing potential trade-offs or limitations of the proposed indexes." }, { "judge_key": "granite_40", "judge_name": "Granite 4.0 Micro", "respondent_key": "qwen3_32b", "respondent_name": "Qwen 3 32B", "weighted_score": 8.8, "brief_justification": "The response accurately identifies subquery inefficiencies, proposes effective indexing strategies, and provides a clear, well-structured rewrite using JOINs and aggregation, demonstrating strong depth and practical usefulness." }, { "judge_key": "granite_40", "judge_name": "Granite 4.0 Micro", "respondent_key": "kimi_k25", "respondent_name": "Kimi K2.5", "weighted_score": 7.55, "brief_justification": "The response correctly identifies the need for additional indexes to speed up the subqueries and suggests appropriate indexes, though it could provide more detail on the specific index structures and potential trade-offs." }, { "judge_key": "granite_40", "judge_name": "Granite 4.0 Micro", "respondent_key": "devstral", "respondent_name": "Devstral Small", "weighted_score": 8.8, "brief_justification": "The response accurately identifies inefficiencies, proposes relevant indexes, and provides a clear, well-structured optimized query with logical justification." }, { "judge_key": "granite_40", "judge_name": "Granite 4.0 Micro", "respondent_key": "gemma3_27b", "respondent_name": "Gemma 3 27B", "weighted_score": 8.8, "brief_justification": "The response accurately identifies the performance bottleneck caused by correlated subqueries and provides a clear, well-structured optimization strategy using aggregation and composite indexes, with appropriate justification and consideration of implementation details." }, { "judge_key": "granite_40", "judge_name": "Granite 4.0 Micro", "respondent_key": "llama4_scout", "respondent_name": "Llama 4 Scout", "weighted_score": 8.4, "brief_justification": "The response correctly identifies performance issues, proposes optimized query, and recommends appropriate indexes, though depth could be improved by discussing execution plan benefits." }, { "judge_key": "granite_40", "judge_name": "Granite 4.0 Micro", "respondent_key": "phi4", "respondent_name": "Phi-4 14B", "weighted_score": 8.8, "brief_justification": "The response accurately identifies the issue with correlated subqueries, proposes effective indexing strategies, and provides a clear, well-structured optimized query with appropriate justifications." }, { "judge_key": "granite_40", "judge_name": "Granite 4.0 Micro", "respondent_key": "qwen3_8b", "respondent_name": "Qwen 3 8B", "weighted_score": 8.8, "brief_justification": "The response accurately identifies the performance bottlenecks, proposes a clear optimization strategy, and provides a well-structured index plan that together should reduce execution time from 45 seconds to under 1 second." }, { "judge_key": "granite_40", "judge_name": "Granite 4.0 Micro", "respondent_key": "mistral_nemo", "respondent_name": "Mistral Nemo 12B", "weighted_score": 8.8, "brief_justification": "The response accurately identifies the cause of the query's slowness, proposes a clear optimization strategy using CTEs and joins, and suggests appropriate indexes. The explanation is thorough but could delve slightly deeper into the performance implications of each change." }, { "judge_key": "granite_40", "judge_name": "Granite 4.0 Micro", "respondent_key": "llama31_8b", "respondent_name": "Llama 3.1 8B", "weighted_score": 8.8, "brief_justification": "The response provides a well-structured optimization strategy with clear explanations, appropriate index recommendations, and expected performance improvements." }, { "judge_key": "qwen3_8b", "judge_name": "Qwen 3 8B", "respondent_key": "qwen3_32b", "respondent_name": "Qwen 3 32B", "weighted_score": 9.45, "brief_justification": "The response correctly identifies correlated subquery inefficiencies, provides an effective rewrite strategy, and suggests optimal indexes. It thoroughly explains the optimization rationale and expected performance improvements." }, { "judge_key": "qwen3_8b", "judge_name": "Qwen 3 8B", "respondent_key": "devstral", "respondent_name": "Devstral Small", "weighted_score": 9.4, "brief_justification": "The response correctly identifies subquery inefficiencies and proposes a CTE-based solution with appropriate indexing. It explains the optimization strategy clearly but could have addressed potential index contention or alternative approaches." }, { "judge_key": "qwen3_8b", "judge_name": "Qwen 3 8B", "respondent_key": "gemma3_27b", "respondent_name": "Gemma 3 27B", "weighted_score": 9.4, "brief_justification": "The response correctly identifies the correlated subquery bottleneck and provides an optimized query with clear explanation. It suggests relevant indexes and covers important considerations, though could have addressed alternative optimizations like materialized views." }, { "judge_key": "qwen3_8b", "judge_name": "Qwen 3 8B", "respondent_key": "llama4_scout", "respondent_name": "Llama 4 Scout", "weighted_score": 9.2, "brief_justification": "The response correctly identifies correlated subqueries as the main issue and provides effective rewrite strategies. It recommends appropriate indexes but doesn't explore advanced optimizations like materialized views or query execution plan analysis." }, { "judge_key": "qwen3_8b", "judge_name": "Qwen 3 8B", "respondent_key": "phi4", "respondent_name": "Phi-4 14B", "weighted_score": 9.45, "brief_justification": "The response correctly identifies correlated subqueries as the bottleneck, proposes a JOIN-based approach with appropriate indexes, and explains the optimization strategy effectively. However, it could have explored additional optimizations like covering indexes or query execution plan analysis for deeper insights." }, { "judge_key": "qwen3_8b", "judge_name": "Qwen 3 8B", "respondent_key": "granite_40", "respondent_name": "Granite 4.0 Micro", "weighted_score": 8.8, "brief_justification": "The response correctly identifies correlated subqueries as the main bottleneck and proposes joins with GROUP BY as an effective optimization. It provides actionable index suggestions and explains the strategy clearly, though could elaborate more on specific composite index designs for optimal performance." }, { "judge_key": "qwen3_8b", "judge_name": "Qwen 3 8B", "respondent_key": "mistral_nemo", "respondent_name": "Mistral Nemo 12B", "weighted_score": 8.6, "brief_justification": "The response correctly identifies subquery inefficiencies and provides a CTE-based optimization with appropriate indexing. It addresses the core issue but could elaborate more on index ordering or alternative optimizations." }, { "judge_key": "qwen3_8b", "judge_name": "Qwen 3 8B", "respondent_key": "llama31_8b", "respondent_name": "Llama 3.1 8B", "weighted_score": 8.65, "brief_justification": "The response correctly identifies correlated subquery issues and provides effective index-based optimizations. It explains the strategy clearly but could have addressed potential index redundancy (users.id is already primary key)." }, { "judge_key": "mistral_nemo", "judge_name": "Mistral Nemo 12B", "respondent_key": "qwen3_32b", "respondent_name": "Qwen 3 32B", "weighted_score": 10, "brief_justification": "The response provides a thorough and accurate optimization strategy, clearly explaining each step and its expected impact on performance. It also includes the necessary SQL queries and index creation statements." }, { "judge_key": "mistral_nemo", "judge_name": "Mistral Nemo 12B", "respondent_key": "devstral", "respondent_name": "Devstral Small", "weighted_score": 8.05, "brief_justification": "The response accurately identifies and addresses the inefficiencies in the original query, providing a well-structured and optimized alternative. However, it could delve deeper into explaining the impact of each optimization and provide more specific performance estimates." }, { "judge_key": "mistral_nemo", "judge_name": "Mistral Nemo 12B", "respondent_key": "gemma3_27b", "respondent_name": "Gemma 3 27B", "weighted_score": 9.8, "brief_justification": "The response accurately identifies the performance issue, provides a clear and detailed optimization strategy, includes specific SQL changes and necessary indexes, and explains the reasoning behind each decision. It also considers real-world factors and provides practical advice for testing and verification." }, { "judge_key": "mistral_nemo", "judge_name": "Mistral Nemo 12B", "respondent_key": "llama4_scout", "respondent_name": "Llama 4 Scout", "weighted_score": 9.6, "brief_justification": "The response accurately identifies and addresses the performance issues in the original SQL query. It provides a clear and detailed explanation of the optimization strategy, along with two optimized query versions and index recommendations. The response is comprehensive, clear, and highly useful for improving the query's performance." }, { "judge_key": "mistral_nemo", "judge_name": "Mistral Nemo 12B", "respondent_key": "phi4", "respondent_name": "Phi-4 14B", "weighted_score": 7.65, "brief_justification": "The response accurately identifies and addresses the performance issue with the SQL query. It provides a well-structured optimization strategy and demonstrates a good understanding of SQL and indexing. However, it could benefit from more detailed justification of the scores and a deeper analysis of the query's performance characteristics." }, { "judge_key": "mistral_nemo", "judge_name": "Mistral Nemo 12B", "respondent_key": "granite_40", "respondent_name": "Granite 4.0 Micro", "weighted_score": 8.35, "brief_justification": "The response provides a detailed and insightful analysis of the SQL query, identifies bottlenecks, and proposes a clear optimization strategy. The rewritten query is well-structured and should significantly improve performance. The only minor deduction is for clarity, as some parts could be explained more concisely." }, { "judge_key": "mistral_nemo", "judge_name": "Mistral Nemo 12B", "respondent_key": "llama31_8b", "respondent_name": "Llama 3.1 8B", "weighted_score": 9.05, "brief_justification": "The response accurately identifies the issue with the original query, provides a well-optimized rewrite, and suggests relevant indexes. The only minor deduction is for not explicitly mentioning the index on users(id)." }, { "judge_key": "llama31_8b", "judge_name": "Llama 3.1 8B", "respondent_key": "qwen3_32b", "respondent_name": "Qwen 3 32B", "weighted_score": 9.6, "brief_justification": "The response provides a thorough explanation of the optimization strategy, including query rewriting, index creation, and expected performance gain. It also includes a clear summary of the changes made and provides a detailed justification for each step." }, { "judge_key": "llama31_8b", "judge_name": "Llama 3.1 8B", "respondent_key": "devstral", "respondent_name": "Devstral Small", "weighted_score": 9.2, "brief_justification": "The response thoroughly explains the optimization strategy and provides a clear, well-structured optimized query. The indexes suggested are relevant and the use of a Common Table Expression (CTE) is a good approach to reduce the number of rows processed in the main query." }, { "judge_key": "llama31_8b", "judge_name": "Llama 3.1 8B", "respondent_key": "gemma3_27b", "respondent_name": "Gemma 3 27B", "weighted_score": 9.6, "brief_justification": "The response provides a thorough explanation of the optimization strategy, including the use of joins and aggregation, filtering after aggregation, and index utilization. The indexes suggested are well-justified and should significantly improve query performance." }, { "judge_key": "llama31_8b", "judge_name": "Llama 3.1 8B", "respondent_key": "llama4_scout", "respondent_name": "Llama 4 Scout", "weighted_score": 9.05, "brief_justification": "The response provides a thorough analysis of the original query's issues and presents a well-explained, optimized query with clear index recommendations. However, the depth of the analysis could be improved by providing more technical details about the query plan and the impact of the optimizations." }, { "judge_key": "llama31_8b", "judge_name": "Llama 3.1 8B", "respondent_key": "phi4", "respondent_name": "Phi-4 14B", "weighted_score": 8.6, "brief_justification": "The response provides a clear and effective optimization strategy, leveraging indexes and restructuring the query to minimize repeated computations. However, it could benefit from more detailed explanations of the indexing strategy and potential performance improvements." }, { "judge_key": "llama31_8b", "judge_name": "Llama 3.1 8B", "respondent_key": "granite_40", "respondent_name": "Granite 4.0 Micro", "weighted_score": 8.8, "brief_justification": "The response provides a thorough and well-structured optimization strategy, including a clear explanation of the bottlenecks, the use of joins and aggregate functions, and the addition of necessary indexes. The response also provides additional tips for further optimization and concludes with a clear statement of the expected performance improvement." }, { "judge_key": "llama31_8b", "judge_name": "Llama 3.1 8B", "respondent_key": "qwen3_8b", "respondent_name": "Qwen 3 8B", "weighted_score": 9.6, "brief_justification": "The response provides a thorough explanation of the optimization strategy, including the elimination of correlated subqueries, filtering early, and adding appropriate indexes. The estimated execution time is also provided, making it a useful and actionable response." }, { "judge_key": "llama31_8b", "judge_name": "Llama 3.1 8B", "respondent_key": "mistral_nemo", "respondent_name": "Mistral Nemo 12B", "weighted_score": 8.6, "brief_justification": "The response accurately identifies the performance bottleneck, provides a well-explained optimization strategy, and suggests relevant indexes to improve query performance." } ], "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)" } }