Comparison page
GPT-5 nano vs Qwen3 8B
Compare OpenAI’s cheapest practical tier with a third-party-hosted Qwen3 8B deployment option.
Side-by-side pricing
The table uses the same example request on both models so you can compare third-party hosted and ultra-low-cost routing without changing the prompt shape.
Input 1,000 / Output 250
| Model | Input / 1M | Cached input / 1M | Output / 1M | Example request | Source |
|---|---|---|---|---|---|
GPT-5 nano Lowest-cost OpenAI model for routing, classification, and ultra-high-volume requests. | $0.0500 | No public rate | $0.400 | $0.0002 | OpenAI pricing |
Qwen3 8B Third-party-hosted Qwen3 8B pricing from hosted inference providers. This model is third-party-hosted rather than first-party hosted. | $0.200 | No public rate | $0.200 | $0.0003 | Third-party hosted pricing |
When to choose each model
GPT-5 nano
Choose GPT-5 nano when you want the economics or capability profile of OpenAI and you can justify the published rate.
Qwen3 8B
Choose Qwen3 8B when you need the second option in this comparison and want to test whether it lowers spend without hurting the workflow.
Use cases
- • Deciding whether a hosted open model is enough for routine work.
- • Comparing a first-party API against a third-party-hosted alternative.
- • Routing cheap tasks to the least expensive acceptable option.
If the prompt is noisy or repetitive, run it through the prompt optimizer first.
If the brief is too loose, use the context engineer to tighten the instructions before you compare models again.
For the broader pricing strategy, read the LLM cost optimization pillar and the cost-per-million-tokens cheat sheet.
FAQ
Why mark Qwen as third-party hosted?
Because this page is comparing a hosted deployment option rather than a first-party Qwen API offering.
Which model is cheaper?
GPT-5 nano is the cheaper first-party input-rate option in the current comparison, while Qwen3 8B is shown as a hosted alternative.
What should I test before switching?
Run the exact prompt in the calculator and confirm the output quality before moving workload to a different host.