Dropping the API for a local Qwen-3.8-27B: one setup, one cost estimate, no benchmark

On September 24 a post on r/LocalLLaMA titled "Qwen-3.8-27B is good enough that I stopped using API" drew roughly 600 upvotes and about 250 comments. Its author, u/Training-Respect8066, did the unusual thing for the genre: he published the configuration and his own cost arithmetic instead of a screenshot. He runs the official Qwen weights on his own machine under the Pi agent and says he has stopped paying for API access, which he lays out in the thread. It is a workflow report rather than a measurement, and it should be read as one.

The setup: Q4 weights, a Q8_0 cache, four tools

His quant is Q4_K_S and his context cache is quantized to Q8_0, which he reports seems fine quality-wise. He runs the official release rather than a derivative, having briefly tried Swift-Qwen, a family marketed on spending fewer thinking tokens. It was faster, he says, but kept getting trapped in loops, which he calls very rare in vanilla Qwen. On the agent side, Qwen-3.8 drives the Pi agent with no MCP and the minimum toolset: bash, then read, write and edit. He names the edit tool as the weakest link, because the model often has to retry after messing up its indentation. Docker is the sandbox, and the agent itself runs on a Raspberry Pi, which he notes is fitting.

His cost math, on his hardware

The one number the post invites is a price. On his hardware and where he lives, he says 1M tokens cost him:

• Input: 2.4 cents
• Output: 70 cents
• Ratio: output at roughly 29 times input

That is an amortized figure covering his own hardware and electricity, not a hosted price list, and he offers it as comparable to the cheapest providers on nano-gpt.com, a comparison that is his reading rather than a market measurement. The asymmetry matters most for agents, which are output-heavy by construction, and it is the number most likely to move with the workload. Prompt latency, concurrency and the tokens his retried edits burn all sit outside the arithmetic. Comparisons like this live inside the local crowd, where a 27B holding 100K context is already routine.

What one setup cannot carry

It shows one user clearing day-to-day coding work, unsupervised, on a quantized local model. It cannot speak to quality at scale, latency on long prompts, or total cost once hardware, electricity and retries are counted, and the same weights are being sold in two directions: one derivative markets the thinking tokens as waste while this user accepts the verbosity and works around it with minimal tools and a cheaper cache. Reproducible throughput and cost from several users on stated hardware, plus a head-to-head against the API he dropped, would settle it.