OpenAI: GPT-6 Luna vs Qwen: Qwen3.8 Flash
A side-by-side comparison of OpenAI: GPT-6 Luna ($0.35 / 1M tokens) and Qwen: Qwen3.8 Flash ($0.52 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.
Specifications
| OpenAI: GPT-6 Luna | Qwen: Qwen3.8 Flash | |
|---|---|---|
| Provider | OpenAI | Qwen |
| Input | $0.35 / 1M tokens | $0.52 / 1M tokens |
| Output | $1.75 / 1M tokens | $1.65 / 1M tokens |
| Cache read | $0.04 / 1M tokens | $0.06 / 1M tokens |
| Cache write | $0.44 / 1M tokens | - |
| Web search | $35.00 / 1K searches | - |
| Context window | 1,050,000 | 1,000,000 |
| Max output | 128,000 | 131,072 |
| Input | file, image, text | text, image, video |
| Output | text | text |
| Tool calling | ||
| Structured output | ||
| Reasoning control | ||
| Added | Sep 22, 2026 | Aug 26, 2026 |
Key differences
- OpenAI: GPT-6 Luna is 33% cheaper on input tokens.
- Qwen: Qwen3.8 Flash is 6% cheaper on output tokens.
- A request with 1,000 input and 500 output tokens costs $0.00123 with OpenAI: GPT-6 Luna and $0.00135 with Qwen: Qwen3.8 Flash.
In depth
OpenAI: GPT-6 Luna is a text model from OpenAI, released on 22 September 2026. Qwen: Qwen3.8 Flash is a text model from Qwen, released on 26 August 2026. Both are served through api14 under the same OpenAI-compatible surface, so the choice between them is mostly about modality coverage, parameter control and price shape rather than about integration work.
The two models are close on context. GPT-6 Luna takes up to 1,050,000 tokens and Qwen3.8 Flash takes up to 1,000,000 tokens, which is not a meaningful difference for most applications. Where they diverge is input modality. GPT-6 Luna accepts files, images and text, so it can read a PDF or a screenshot alongside your prompt. Qwen3.8 Flash accepts text, images and video, so it can look at moving footage but has no listed file input. Both return text only. The parameter sets also differ in character. GPT-6 Luna exposes reasoning controls such as reasoning_effort, verbosity and max_completion_tokens, which suits tuning how much thinking the model does before it answers. Qwen3.8 Flash exposes the more traditional sampling controls, including temperature, top_p, top_k, frequency_penalty, presence_penalty, stop, logprobs and top_logprobs, which suits workloads where you want to shape or inspect the distribution directly.
For task selection, start with the input you actually have. If your pipeline ingests documents, spreadsheets or scanned pages, GPT-6 Luna's file input removes a conversion step. If you are working with video, Qwen3.8 Flash is the one that accepts it. If you need fine grained control over reasoning depth, GPT-6 Luna's reasoning parameters are the more direct fit. If you need log probabilities, top_k, or classic penalty and stop controls, Qwen3.8 Flash gives you those and GPT-6 Luna does not list them. Both support tools, tool_choice, structured_outputs, response_format, seed and include_reasoning, so function calling and JSON shaped output are available either way and should not decide it.
Price is the other axis, and it splits. GPT-6 Luna is the cheaper of the two on input, while Qwen3.8 Flash is the cheaper on output. That means the better pick depends on your token mix. Long prompts with short answers lean toward GPT-6 Luna, while short prompts with long generations lean toward Qwen3.8 Flash. Check the live price table on this page for the current numbers before you commit, since the gap in either direction may be small enough that other factors dominate.
Calling either model is the same operation. Point your OpenAI client at api14's base URL, send your request to the chat completions endpoint, and set the model field to "openai/gpt-6-luna" or "qwen/qwen3.8-flash". Everything else in the request body keeps the same shape. Switching between them is a one line change to the model id, so you can route per request, compare outputs side by side, or fall back from one to the other without touching your application code. Just remember to only send parameters the target model lists, since the two sets are not identical.
Switch with one string
Both models use the same endpoint and request shape. Change the model value from "openai/gpt-6-luna" to "qwen/qwen3.8-flash" and prompts, streaming and tools keep working.
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api14.com/api/v1",
apiKey: process.env.API14_API_KEY,
});
const result = await client.chat.completions.create({
"model": "qwen/qwen3.8-flash",
"messages": [
{
"role": "user",
"content": "Explain vector databases in two sentences."
}
]
});
console.log(result.choices[0].message.content);
More comparisons
- LiquidAI: LFM3-1.7B vs Ricursive: Ripple 65B Preview
- LiquidAI: LFM3-1.7B vs Crouton Alpha
- LiquidAI: LFM3-1.7B vs Tencent: Hy4
- LiquidAI: LFM3-1.7B vs Z.ai: GLM 5.3 FlashX Legal
- Ricursive: Ripple 65B Preview vs Crouton Alpha
- Ricursive: Ripple 65B Preview vs Tencent: Hy4
- Ricursive: Ripple 65B Preview vs Z.ai: GLM 5.3 FlashX Legal
- Crouton Alpha vs Tencent: Hy4