GPT-5.4 nano versus 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-5.4 nano and Qwen3.6 Flash, one from OpenAI, the other from Alibaba Cloud (Qwen). The first dates from March 17, 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.

What the benchmarks say

On our capability index, GPT-5.4 nano scores 71.7 out of 100 and Qwen3.6 Flash 64.5. GPT-5.4 nano leads by 7.2 points.

On the 3 benchmarks reported for both, GPT-5.4 nano wins 2 times and Qwen3.6 Flash 1:

• GPQA Diamond (PhD-level science questions): GPT-5.4 nano 78.5%, Qwen3.6 Flash 83.3%
• OTIS Mock AIME (competition mathematics problems): GPT-5.4 nano 87.8%, Qwen3.6 Flash 84.4%
• FrontierMath (very hard research-level mathematics): GPT-5.4 nano 44.9%, Qwen3.6 Flash 22.5%

Pricing

GPT-5.4 nano costs $0.20 per million input tokens and $1.25 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 $0.007 with GPT-5.4 nano versus $0.006 with Qwen3.6 Flash. The cost is almost the same.

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

Under the hood

Qwen3.6 Flash reads more in one go: 1,000,000 tokens of context (roughly 1,500 pages), versus 400,000 for GPT-5.4 nano.

Reasoning mode: GPT-5.4 nano yes, Qwen3.6 Flash yes. Tool calling: GPT-5.4 nano yes, Qwen3.6 Flash yes. Image input: GPT-5.4 nano yes, Qwen3.6 Flash yes.

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

In short

Our reading of the data:

• Choose GPT-5.4 nano for a higher capability index.
• Choose Qwen3.6 Flash for a larger context window.

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