8 AI Coding Models Ranked by Cost-per-Task

In 2026, the evaluation of AI coding models focuses on cost-per-task, which combines token pricing and each model's ability to resolve coding tasks effectively. DeepSeek V3.2 emerges as the most economical option, costing only $0.028 per resolved task, making it ideal for high-volume, well-defined coding tasks. Claude Opus 4.5, while leading in accuracy at 80.9% SWE-bench Verified, is the most expensive at $0.68 per task, suitable only for critical tasks where accuracy is paramount. Among open models, Kimi K2.6 offers a competitive accuracy of 80.2% at $0.075 per task, presenting the best accuracy-per-dollar ratio. The closed model Gemini 3 Flash also delivers good value at 78% accuracy for $0.078 per task, benefiting teams already using Google Cloud. This analysis emphasizes the importance of aligning the choice of AI coding models with the specific needs and budget constraints of the task at hand, suggesting a hybrid approach that leverages both open and closed models for optimal cost-efficiency.

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