{ "evaluation_id": "EVAL-20260402-131651", "question_id": "CODE-013", "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": "2026-04-02T13:16:51.002Z", "display_date": "Apr 02, 2026", "winner": { "name": "Claude Opus 4.6", "provider": "openrouter", "score": 9.29 }, "avg_score": 7.6825, "matrix_size": 54, "models_used": [ { "id": "gemini_31_pro", "name": "Gemini 3.1 Pro", "provider": "openrouter" }, { "id": "mimo_v2_flash", "name": "MiMo-V2-Flash", "provider": "Xiaomi" }, { "id": "claude_opus_46", "name": "Claude Opus 4.6", "provider": "openrouter" }, { "id": "claude_sonnet_46", "name": "Claude Sonnet 4.6", "provider": "openrouter" }, { "id": "deepseek_v4", "name": "DeepSeek V4", "provider": "openrouter" }, { "id": "gpt_oss_120b", "name": "GPT-OSS-120B", "provider": "OpenAI" }, { "id": "gemini_3_flash", "name": "Gemini 3 Flash Preview", "provider": "Google" }, { "id": "gpt_5_4", "name": "GPT-5.4", "provider": "openrouter" } ], "rankings": { "claude_opus_46": { "display_name": "Claude Opus 4.6", "provider": "openrouter", "average_score": 9.29, "score_count": 7, "min_score": 8.55, "max_score": 10, "rank": 1 }, "claude_sonnet_46": { "display_name": "Claude Sonnet 4.6", "provider": "openrouter", "average_score": 9.16, "score_count": 6, "min_score": 8.4, "max_score": 10, "rank": 2 }, "gpt_5_4": { "display_name": "GPT-5.4", "provider": "openrouter", "average_score": 9.06, "score_count": 7, "min_score": 7.2, "max_score": 9.8, "rank": 3 }, "gpt_oss_120b": { "display_name": "GPT-OSS-120B", "provider": "OpenAI", "average_score": 8.82, "score_count": 6, "min_score": 7.75, "max_score": 9.8, "rank": 4 }, "gemini_3_flash": { "display_name": "Gemini 3 Flash Preview", "provider": "Google", "average_score": 8.51, "score_count": 7, "min_score": 7.75, "max_score": 9.35, "rank": 5 }, "mimo_v2_flash": { "display_name": "MiMo-V2-Flash", "provider": "Xiaomi", "average_score": 8.45, "score_count": 7, "min_score": 6.25, "max_score": 9.55, "rank": 6 }, "deepseek_v4": { "display_name": "DeepSeek V4", "provider": "openrouter", "average_score": 7.09, "score_count": 7, "min_score": 4.65, "max_score": 8, "rank": 7 }, "gemini_31_pro": { "display_name": "Gemini 3.1 Pro", "provider": "openrouter", "average_score": 1.08, "score_count": 7, "min_score": 0.2, "max_score": 2.45, "rank": 8 } }, "judgments": [ { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 0.45, "brief_justification": "The response is truncated and does not provide a rewritten query, indexing plan, or coherent optimization strategy. It contains only a fragment with minimal evaluable content." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 9.55, "brief_justification": "The response provides a highly effective optimization strategy. It correctly identifies the N+1 subquery problem, provides a cleaner JOIN-based query, and suggests appropriate composite indexes. The explanation of Index-Only Scans and the impact of the WHERE vs HAVING clause is technically sound and very helpful." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 8.55, "brief_justification": "Strong rewrite and indexing strategy: consolidates repeated correlated subqueries into one aggregate join and recommends useful indexes. Clear and practical. Minor issues: claims like '40M+ sequential scans' and guaranteed '<1s' / '50–200ms' are overstated, INCLUDE is not standard across all SQL engines, and the users index may be less critical than an orders-focused index or engine-specific top-N strategy." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 8.4, "brief_justification": "Strong rewrite and indexing advice with clear explanation of correlated-subquery issues and set-based aggregation. Minor overclaims: a covering index on (user_id, amount, created_at) does not guarantee sub-second performance across engines, index-only scans depend on DB support/visibility, and scanning all orders may still be expensive. Overall very solid, practical answer." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 4.65, "brief_justification": "The rewrite correctly removes correlated subqueries and suggests a key index on orders(user_id), but the answer is incomplete and truncated, missing full index recommendations and enough detail to justify the under-1-second claim." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 7.75, "brief_justification": "Clear rewrite and solid indexing advice, correctly replacing correlated subqueries with one aggregation. However, it overclaims guaranteed sub-second performance, suggests the planner can use an index on the aggregate/sort in ways that are not generally true, and the CTE may not always push filtering as effectively as pre-filtering users before aggregating orders." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 7.75, "brief_justification": "The rewrite correctly removes correlated subqueries and suggests sensible indexes, but it overclaims sub-second performance and index-only behavior across databases. It misses a stronger strategy of filtering users first before aggregating orders for only relevant users, and the complexity discussion is oversimplified. Still, it is clear, practical, and substantially improves the original query." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 7.4, "brief_justification": "Clear rewrite and correctly identifies correlated subqueries as the main issue, with reasonable index suggestions. However, claiming under 1 second is not justified, the LEFT JOIN is unnecessary given HAVING COUNT>5, and the proposed orders index column order is debatable for maximizing MAX(created_at) and covering behavior across databases." }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 9.55, "brief_justification": "The response is highly accurate, correctly identifying the core problem (4 correlated subqueries replaced by a single aggregation pass), providing proper index recommendations including covering indexes, and offering progressive optimization strategies. The inclusion of the selectivity-aware variant (pushing user filter into the subquery), the materialized view suggestion for guaranteed sub-second performance, and the INCLUDE column recommendations show excellent depth. The explanation is except" }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 1, "brief_justification": "The response is clearly truncated/corrupted and does not provide a rewritten query, optimization strategy, or index recommendations. It contains only a fragment of what appears to be internal reasoning about COUNT(*) vs COUNT(o.id), which is tangential to the main question. No meaningful content is delivered." }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 9.2, "brief_justification": "The response is excellent across all dimensions. The optimization strategy is correct: replacing correlated subqueries with a CTE/JOIN, adding appropriate indexes including a covering index on orders, and explaining the complexity reduction from O(n²) to O(n). The covering index choice is well-justified. Minor nitpick: the CTE approach aggregates ALL orders first before filtering by user date, which could be suboptimal if most users were created before 2024-01-01 (a JOIN-first approach or pushin" }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 6.45, "brief_justification": "The core optimization of replacing correlated subqueries with a CTE/JOIN is correct and well-explained. However, the response appears truncated - the second index definition is cut off, and likely important indexes are missing (e.g., a covering index on orders(user_id, amount, created_at) would be far more impactful than a simple index on user_id). The CTE approach aggregates ALL users in the orders table before filtering by created_at, which is suboptimal for 10M rows - pushing the user filter " }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 9.2, "brief_justification": "The response is highly accurate in diagnosing the performance problems (correlated subqueries causing repeated scans, missing indexes) and provides a correct rewrite using a CTE with GROUP BY/HAVING. The index recommendations are well-reasoned, especially the composite covering index on orders(user_id, amount, created_at). The explanation is exceptionally well-structured with clear tables, numbered steps, and before/after comparisons. Minor nitpick: the CTE approach aggregates ALL orders first b" }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 8.55, "brief_justification": "The rewrite correctly eliminates correlated subqueries by using a derived table with GROUP BY/HAVING, which is the core optimization needed. The index recommendations are sound—covering index on orders(user_id, amount, created_at) and index on users(created_at) are appropriate. Minor issues: the rewrite aggregates ALL orders globally before joining, which on a 10M-row orders table could be suboptimal compared to pushing the user filter down first (e.g., joining first then aggregating, or using a" }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 8.95, "brief_justification": "The optimized query correctly replaces correlated subqueries with a single JOIN + GROUP BY, which is the primary optimization. The index recommendations are sound—the composite covering index on orders(user_id, amount, created_at) enabling index-only scans is excellent. Minor issues: the LEFT JOIN is unnecessary given the HAVING COUNT > 5 (INNER JOIN would be slightly more efficient), and the claim about 'N+1' is slightly imprecise (it's more like N*3 correlated subqueries, not the ORM N+1 patte" }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 7.2, "brief_justification": "" }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 9.55, "brief_justification": "" }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 9.55, "brief_justification": "" }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 7.65, "brief_justification": "The query rewrite and optimization strategy are correct and highly effective. However, the response is cut off at the end, leaving the index recommendations incomplete." }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 7.8, "brief_justification": "" }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 8, "brief_justification": "" }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 6.25, "brief_justification": "" }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 9.35, "brief_justification": "Excellent response that correctly identifies the core problem (4 correlated subqueries causing repeated table scans), provides a well-optimized rewrite using a single aggregation pass, recommends appropriate indexes with clear justification, and goes beyond the basics with covering indexes, the early-filter optimization variant, and materialized view suggestions. The explanation is well-structured and actionable. Minor deduction: the 'best practical version' with a join inside the subquery intro" }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 9.2, "brief_justification": "Excellent response with correct query rewrite replacing 4 correlated subqueries with single aggregation. Thoroughly explains optimization strategy, provides specific index recommendations with covering index options, and suggests materialized view as ultimate solution. Well-structured with clear explanations." }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 9.55, "brief_justification": "Excellent response that correctly identifies the core problem (correlated subqueries causing N×M scans), provides a well-optimized rewrite using a single aggregation JOIN, recommends appropriate covering indexes, and explains the execution plan differences clearly. The visual diagrams and performance comparison table add significant clarity. Minor note: the 'Option B' partial index example acknowledges its own limitation. The response goes beyond the question with materialized views and advanced" }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 1, "brief_justification": "The response appears to be a fragment or truncated output with no meaningful content. It does not rewrite the SQL query, explain optimization strategy, or recommend indexes. It is essentially unusable." }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 7.2, "brief_justification": "The optimization strategy is correct and well-explained - replacing correlated subqueries with a CTE/JOIN is the right approach. The response correctly identifies the core problem and provides a working solution. However, it's cut off mid-sentence in the indexes section, missing the full index recommendations (e.g., composite indexes like idx_users_created_at on users(created_at) or covering indexes). The response also lacks discussion of composite indexes that could further optimize the query, " }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 9.55, "brief_justification": "Excellent response that correctly identifies all performance issues (correlated subqueries, missing indexes, repeated scans), provides a well-optimized rewrite using CTE with single aggregation pass, recommends appropriate covering indexes with clear reasoning, and includes a performance comparison table. The explanation is thorough, well-structured, and highly actionable. Minor deduction for correctness as the response slightly overstates that LIMIT reduces sort work (the DB still needs to sort" }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 8.8, "brief_justification": "Excellent response that correctly identifies the N+1 correlated subquery problem and provides a clean, correct rewrite using a derived table with GROUP BY/HAVING. The index recommendations are well-reasoned, particularly the covering index on orders(user_id, amount, created_at). The complexity analysis (O(N×M) vs O(N+M)) adds good depth. Minor note: the HAVING filter happens before the join to users, which is correctly noted as an optimization. The explanation is clear and actionable throughout." }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 9.2, "brief_justification": "Excellent response that correctly identifies the N+1 correlated subquery problem and rewrites it as a single JOIN with GROUP BY/HAVING. The index recommendations are well-reasoned, including the composite covering index for index-only scans. The explanation is thorough, well-structured, and actionable. Minor note: the HAVING clause with LEFT JOIN technically changes semantics slightly (users with 0 orders get filtered, which matches original intent), and the claim about 'scanning orders index on" }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 9, "brief_justification": "The response accurately rewrites the query using a single aggregation, explains the performance issues clearly, provides a solid optimization strategy with multiple variants, recommends appropriate indexes, and suggests further scaling options like materialized views. It is comprehensive, well-structured, and highly practical." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 9, "brief_justification": "The response accurately rewrites the query using a single GROUP BY join to eliminate correlated subqueries, provides correct and practical indexes (including covering ones), and delivers a thorough, well-structured explanation with visuals, comparisons, and advanced options for high value." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 0.2, "brief_justification": "The provided response is incomplete, nonsensical, and does not address the query at all. It appears to be a truncated fragment from an unrelated explanation about COUNT(*)." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 8.8, "brief_justification": "The response correctly identifies the main issues with correlated subqueries and provides an effective rewrite using CTE for aggregation. The index suggestions are mostly accurate, though the covering index on orders could be improved by including only necessary columns for the aggregate (e.g., avoiding unnecessary inclusion of amount in the index for COUNT/MAX). Execution plan comparison and performance table add good insight. The materialized view suggestion is a strong additional optimization" }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 8.6, "brief_justification": "Accurate rewrite and index recommendations, clear explanation, good depth, highly actionable." }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 8.6, "brief_justification": "The answer correctly rewrites the query using a join and appropriate aggregation, explains the N+1 issue, and suggests useful indexes. It covers the main optimization steps clearly and with good insight, though it could mention a few additional tuning options." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 9.8, "brief_justification": "The response provides a highly efficient SQL rewrite, correctly identifies the performance bottlenecks of correlated subqueries, and suggests optimal indexing strategies including covering indexes. It also goes above and beyond by suggesting architectural improvements like materialized views/summary tables." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 10, "brief_justification": "The response is an exceptional masterclass in SQL optimization. It correctly identifies the N+1 problem caused by correlated subqueries, provides a high-performance JOIN-based alternative, suggests modern indexing strategies (covering indexes with INCLUDE), and explains the underlying execution plan changes. The inclusion of advanced options like Materialized Views adds extra value." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 1.45, "brief_justification": "The response is incomplete and appears to be a fragment of a thought process rather than a finished answer. It fails to provide the rewritten SQL, the required indexes, or a complete optimization strategy." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 10, "brief_justification": "The response is exceptional. It correctly identifies all four performance bottlenecks, provides a highly optimized query using a CTE and JOIN, suggests the exact covering indexes needed, and explains the complexity shift from O(n^2) to O(n). The inclusion of an execution plan comparison and a materialized view alternative adds significant value." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 8, "brief_justification": "The optimization strategy is sound and the SQL rewrite is much more efficient. However, the index recommendation section is cut off at the end, and the CTE approach might still scan the entire orders table before joining unless the database engine optimizes the predicate pushdown." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 9.8, "brief_justification": "The response provides a perfect optimization strategy, correctly identifying the N+1 subquery problem and providing a much more efficient CTE/Join approach. The index recommendations are spot-on, specifically the use of a composite covering index on the orders table to allow for an index-only scan." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 9, "brief_justification": "Excellent response that correctly identifies correlated subqueries as the root cause, provides a properly optimized query using JOIN with pre-aggregation, suggests appropriate covering indexes, and includes detailed execution plan analysis. Thorough coverage includes advanced options like materialized views. Well-structured with clear explanations and actionable solutions." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 1, "brief_justification": "Response is incomplete and fragmented. It appears to be cut off mid-sentence and does not provide the requested rewritten SQL query, optimization strategy explanation, or index recommendations." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 9, "brief_justification": "Excellent response that correctly identifies the core problem (correlated subqueries), provides a working optimized solution using CTEs, recommends appropriate covering indexes with clear rationale, and even suggests materialized views for further optimization. Well-structured with execution plan comparison and performance breakdown." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 7.85, "brief_justification": "Correctly optimizes the query using CTE to eliminate correlated subqueries. Strategy is sound and will significantly improve performance. Index recommendations are helpful but incomplete (second index is truncated). Good clarity with practical, actionable solution." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 8.8, "brief_justification": "Excellent response that correctly identifies all performance issues (correlated subqueries, repeated scans, missing indexes) and provides a valid solution using CTE aggregation. Thoroughly explains optimization strategy, provides specific index recommendations with reasoning about covering indexes and index-only scans, and includes expected performance impact. Slightly loses points for incomplete ending sentence, but otherwise highly comprehensive and actionable." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 8.55, "brief_justification": "Well-structured response with correct optimized query, comprehensive strategy explanation, and actionable index recommendations. Minor deduction for semantic difference in result set (optimized version excludes users with zero orders)." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 9.2, "brief_justification": "Excellent optimization. Correctly identifies N+1 problem, provides proper JOIN/GROUP BY solution, and suggests appropriate composite indexes with sound reasoning (user_id for join, amount/created_at for index-only scans). Thorough explanation with execution plan details. Minor deduction: total_spent alias in ORDER BY may not work in all SQL dialects." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 9.35, "brief_justification": "Accurate rewrite, thorough index recommendations, clear structure, deep analysis including materialized view option, and highly actionable." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 9.35, "brief_justification": "Accurate and thorough optimization with clear strategy, indexes, and performance analysis." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 2.45, "brief_justification": "Response is incomplete, fragmented, and fails to address the core question. It does not rewrite the query, explain optimization strategy, or suggest indexes. The partial text about aggregation is irrelevant and does not provide a solution." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 7.8, "brief_justification": "Optimization strategy is sound and addresses core inefficiencies, but the response is incomplete (missing final index creation statement) and could explain index usage more deeply." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 9.35, "brief_justification": "Accurate rewrite, clear index recommendations, and thorough explanation of the N+1 problem and covering indexes." } ], "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)" } }