Cutting your OpenAI bill with Qwen: cost vs quality
An outline of the playbook on switching from OpenAI to open-source Qwen models. The full version with example data, working code and a real decision table will be published soon.
This page outlines the upcoming full playbook: which decisions you'll have to make and which questions we'll answer. The full version will include real data, runnable code and an actual decision table.
Picking a real workload
Pick one real workload that already runs on OpenAI: support routing, summarization, extraction or tool-use. Avoid free-form chat; prefer tasks with measurable output schemas.
100-example evaluation
100 examples with the same prompt, expected output and an error-impact note. The goal isn't to declare "Qwen is cheaper", it's to measure whether the quality drop is acceptable.
Qwen shortlist
A fast, a strong and (if needed) an open-self-host candidate from the Qwen family. Run them with Parel Compare against the existing OpenAI model on the same set.
Cost vs latency table
A decision table reads not only price but also accuracy, p95 latency, retry rate and error impact together. A price drop matters only if the quality threshold still passes.
Go / no-go decision
If the threshold passes, recommend a staged migration. If it doesn't, iterate the prompt/schema first; switching models alone is rarely the answer.
You can start today
While we finish the full playbook, you can run the same flow on Parel yourself. Click below to start: