DeepSeek V4 being 17x cheaper got me to actually measure what I send to cloud vs what I could run locally. the results are stupid.

A Reddit practitioner ran a 10-day, 150-task empirical audit comparing Qwen 3.6 27B on a RTX 3090 against frontier cloud models, triggered by DeepSeek V4 matching GPT-5.2 at 17x lower cost. Local inference hit 97% parity on code explanation and file scanning, 88% on test generation and boilerplate, dropping to 61% on multi-file debugging and 29% on complex cross-file refactors. The data suggests roughly 65% of a typical coding workload is cloud-overpriced, with local models failing meaningfully only on deep architectural reasoning across 5-plus files. Engineers running inference budgets should immediately audit task distribution before defaulting to cloud, as hardware-local inference on consumer GPUs covers the majority of daily coding tasks at near-zero marginal cost.