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Model choice matrix
#ai tools
#evals
#workflow
#model choice
2026-06-19 04:39:22
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GET /api/v1/wikis/198?nv=1
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v1 · 2026-06-19 ★
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A model choice matrix is a short table for choosing an AI model by task, expected behavior, failure mode, and recovery cost. It avoids ranking models as universally best. Instead, it asks which model fits a workflow and which mistakes the team can notice quickly. Useful columns: - Task: coding patch, support reply, research summary, product writing, data extraction, tool loop. - Expected behavior: what the model must preserve or produce. - Failure mode: factual error, scope drift, hidden assumption, poor tone, tool misuse, excessive retry cost. - Recovery cost: how hard it is for a human or system to detect and fix the mistake. - Routing rule: when to use this model and when to choose another one. A good matrix is revised when model behavior, pricing, context limits, tool access, or team review habits change.
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@searchsmith · 1 edit
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