GPT-6 Luna vs Qwen3.6 Flash

What are we comparing?

This comparison pits two budget models, built for high volumes at a low price against each other: GPT-6 Luna and Qwen3.6 Flash, one from OpenAI, the other from Alibaba Cloud (Qwen). The first dates from September 22, 2026, the second from April 27, 2026.

All figures below come from public sources checked on October 22, 2026: models.dev and OpenRouter for prices and capabilities, Epoch AI for scores.

Which one is more capable?

On our capability index, GPT-6 Luna scores 80.3 out of 100 and Qwen3.6 Flash 64.5. GPT-6 Luna leads by 15.8 points.

On the 3 benchmarks reported for both, GPT-6 Luna wins 3 times and Qwen3.6 Flash 0:

• GPQA Diamond (PhD-level science questions): GPT-6 Luna 90.5%, Qwen3.6 Flash 83.3%
• OTIS Mock AIME (competition mathematics problems): GPT-6 Luna 98.9%, Qwen3.6 Flash 84.4%
• FrontierMath (very hard research-level mathematics): GPT-6 Luna 78.9%, Qwen3.6 Flash 22.5%

Budget

GPT-6 Luna costs $0.10 per million input tokens and $0.50 per million output tokens; Qwen3.6 Flash costs $0.19 and $1.13.

For a typical agent task (read 20,000 tokens, write 2,000), expect $0.003 with GPT-6 Luna versus $0.006 with Qwen3.6 Flash. GPT-6 Luna is 2 times cheaper, a gap that adds up quickly over 1,000 tasks: $3.06 difference.

Run the numbers with your own volumes in the agent cost calculator.

Features and limits

GPT-6 Luna reads more in one go: 1,050,000 tokens of context (roughly 1,575 pages), versus 1,000,000 for Qwen3.6 Flash.

Reasoning mode: GPT-6 Luna yes, Qwen3.6 Flash yes. Tool calling: GPT-6 Luna yes, Qwen3.6 Flash yes. Image input: GPT-6 Luna yes, Qwen3.6 Flash yes.

Neither publishes its weights: both are used through an API.

In short

Our reading of the data:

• Choose GPT-6 Luna for a higher capability index, a lower cost and a more recent release.

This verdict relies on public figures; it does not replace a trial on your own tasks.