Comparison · OpenAI and Qwen
GPT-6 Luna vs Qwen3.8 Flash
Listed API prices of GPT-6 Luna and Qwen3.8 Flash turned into the cost of the same three standard tasks, with speed from our own test requests in the United States and what each provider’s API documentation says about features that break on a switch. Output quality and answer length are not compared.
Calculated impact cost per task and per month
For an L2 task (deep analysis over a 50,000-token document, 3 steps), GPT-6 Luna costs $0.017 and Qwen3.8 Flash $0.025 at the prices listed in the United States on , so GPT-6 Luna is 31.1 % less than Qwen3.8 Flash, counting visible output only, without cache discounts or hidden reasoning.
| Task | GPT-6 Luna, per task | Qwen3.8 Flash, per task | GPT-6 Luna, per month | Qwen3.8 Flash, per month | GPT-6 Luna vs Qwen3.8 Flash, per task |
|---|---|---|---|---|---|
| L1Micro-task (classification or strict JSON extraction) | $0.0003per L1 task | $0.0003per L1 task | $3.65per month | $4.66per month | −21.6 % |
| L2Deep analysis over a 50,000-token document | $0.017per L2 task | $0.025per L2 task | $254.04per month | $368.80per month | −31.1 % |
| L3Agentic coding loop, 15 steps | $0.136per L3 task | $0.202per L3 task | $1,986.33per month | $2,942.70per month | −32.5 % |
Calculated Per month: 20 tasks an hour around the clock, 730 hours.
Prices USD per 1M tokens
| Price | GPT-6 Luna | Qwen3.8 Flash |
|---|---|---|
| Input, per 1M tokens | $0.100 | $0.150 |
| Output, per 1M tokens | $0.500 | $0.470 |
| Price from | Provider's price list · source page Published | Provider's price list · source page Published |
| Read | · the United States | · the United States |
| Context window | 1,050,000 tokens | 1,000,000 tokens |
What can I use it for? six common tasks, our data checks
Quality on your task: not measured by Measured Lab yet.
| Task | GPT-6 Luna | Qwen3.8 Flash |
|---|---|---|
| Answer customer messages | Fits on price and context | Fits on price and context |
| Pull data from invoices and documents with strict JSON and page images | Fits on price and context | Not confirmed: strict JSON output partly, per Alibaba Cloud’s documentation |
| Summarize documents and meetings | Fits on price and context | Fits on price and context |
| Translate content | Fits on price and context | Fits on price and context |
| Chat assistant on your website | Fits on price and context | Fits on price and context |
| Write and review code with tool calls | Fits on price and context | Fits on price and context |
| Tasks it fits | 6 of 6 | 5 of 6 |
Checks of our data for six common tasks: a published price, a context window and answer length for the task and, where the task needs them, strict JSON output, page images and tool calls; prices, cost a month and terms for each model on its model card. Compare models for your own task in the model picker.
Speed in the US Observed our test requests
In our test requests from the United States, the median time to the first answer token was 2,938 ms for GPT-6 Luna (Direct API · OpenAI) and 1,250 ms for Qwen3.8 Flash (Direct API · Alibaba Cloud).
| Model and route | Time to first answer token, median | Output speed, median | Answers |
|---|---|---|---|
| GPT-6 LunaDirect API · OpenAI | 2,938 ms range 1,981 ms–7,571 ms | 159 tokens/s range 95.1 tokens/s–239 tokens/s | 70 answers on 14 dates |
| Qwen3.8 FlashDirect API · Alibaba Cloud | 1,250 ms range 970 ms–3,449 ms | 60.3 tokens/s range 28.9 tokens/s–137 tokens/s | 70 answers on 14 dates |
The same short request to every model, sent from a measurement point in the United States; medians of successful answers on at least 2 different dates, latest on . How we measure
What breaks when you switch Published provider documentation
Moving from OpenAI to Qwen, 3 of the 5 features may not carry over, by each provider’s documentation:
- Strict JSON schemas — OpenAI: yes; Qwen: partly. Code that parses answers without checks starts failing where a provider promises only valid JSON, not your schema.
- Streamed tool arguments — OpenAI: yes; Qwen: not stated. An interface that shows arguments as they are written sees nothing until the whole call is ready.
- Cache covers tool definitions — OpenAI: yes; Qwen: not stated. Where the cache does not cover tool definitions, a long tool list is paid as fresh input on every request.
Moving from Qwen to OpenAI: every feature that Qwen’s documentation lists as supported is also listed as supported by OpenAI.
| Feature of the API | OpenAI | Qwen |
|---|---|---|
| Strict JSON schemas | Yes | Partly |
| Parallel tool calls | Yes | Yes |
| Streamed tool arguments | Yes | Not stated |
| Reasoning with tools | Yes | Yes |
| Cache covers tool definitions | Yes | Not stated |
What each provider’s own API documentation says, read ; “not stated” is not the same as “no”. Quotes and sources: what breaks when you switch AI APIs.