Ever wonder how much cost you can save when coding with local LLM?

A developer's viral post demonstrates the stark economics of running large language models locally versus using paid API services. By utilizing Alibaba's Qwen3.5 35B model in quantized formats (Q2_K_XL and Q4_K_M) within the Claude Code environment, they successfully built a usable pet project. The model, while showing some 'intelligence issues,' effectively leveraged tools and spawned subagents to write and verify code. In a key session, it processed 2 million tokens in just 2 minutes. The developer's `ccusage` tool estimated that performing the same work using Anthropic's Claude Sonnet 4.6 via API would have cost $10.85. In contrast, the local run incurred no direct model cost, only the negligible electricity for a 400W PC.

The experiment underscores a major shift in AI accessibility, where quantized open-source models like Qwen3.5 are becoming powerful enough for complex, multi-step tasks like software development. The 35-billion-parameter model, running in lower-precision formats to fit on consumer hardware, delivered results comparable to a leading closed model for a specific use case. However, the post also notes the uncertainty surrounding the Qwen team's future, questioning whether such open-source alternatives will continue to emerge or if development will consolidate under giants like Meta. For developers and companies, this represents a tangible cost-benefit analysis: trading potentially higher inference latency and setup complexity for near-zero marginal cost per query, which could redefine budgeting for AI-assisted coding and other iterative tasks.