{"id":992380,"date":"2026-10-29T10:14:00","date_gmt":"2026-10-29T03:14:00","guid":{"rendered":"https:\/\/5id.vn\/small-model-search-boosts-coding-agents-efficiency\/"},"modified":"2026-10-29T10:14:00","modified_gmt":"2026-10-29T03:14:00","slug":"small-model-search-boosts-coding-agents-efficiency","status":"publish","type":"post","link":"https:\/\/5id.vn\/vi\/small-model-search-boosts-coding-agents-efficiency\/","title":{"rendered":"T\u00ecm ki\u1ebfm m\u00f4 h\u00ecnh nh\u1ecf gi\u00fap t\u0103ng hi\u1ec7u qu\u1ea3 c\u1ee7a c\u00e1c t\u00e1c nh\u00e2n l\u1eadp tr\u00ecnh."},"content":{"rendered":"
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Coding agents no longer need to rely on massive language models for locating code snippets. By delegating the search phase to a compact model, the overall workflow becomes faster and more cost-effective.<\/p>\n

small model search<\/h2>\n

The tool jevgrep adopts this strategy: a small model processes a natural-language query, scans the repository without any prior indexing, and returns the relevant file path, code fragment, and line numbers. This pre-search step ensures that the larger model can focus exclusively on generating or completing code.<\/p>\n

In a benchmark of ten SWE-bench tasks, jevgrep correctly solved eight of them and achieved a 25.8% reduction in total execution cost. The division of labor-search handled by the small model and generation by the large model-delivers both higher accuracy and lower resource consumption.<\/p>\n

References<\/h2>\n

These external sources were used to verify the article and provide deeper context.<\/p>\n