Local Small Models for Coding Can Slash Token Costs by 90%
bendee983 · x · 2026-07-04
The author argues that small models under 70B are severely underestimated. Using Gemma 4 26B (MoE) and 31B (Dense) as examples, they run on local hardware with high accuracy. By adopting a workflow where a large model plans and writes detailed specs while a small model writes code step-by-step, they achieved over 90% savings in token costs while keeping sensitive data off the cloud. The potential of local AI is largely underestimated.
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