GLM-5.1 vs Qwen3.6 27B

The match-up at a glance

This comparison pits two open-weight models that you can install on your own servers against each other: GLM-5.1 and Qwen3.6 27B, one from Zhipu AI (Z.ai), the other from Alibaba Cloud (Qwen). The first dates from April 7, 2026, the second from April 22, 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.

Performance

On our capability index, GLM-5.1 scores 70.9 out of 100 and Qwen3.6 27B 72.2. Qwen3.6 27B leads by 1.3 points.

On the 3 benchmarks reported for both, GLM-5.1 wins 3 times and Qwen3.6 27B 0:

• GPQA Diamond (PhD-level science questions): GLM-5.1 89.9%, Qwen3.6 27B 85.9%
• OTIS Mock AIME (competition mathematics problems): GLM-5.1 93.3%, Qwen3.6 27B 91.1%
• FrontierMath (very hard research-level mathematics): GLM-5.1 36.8%, Qwen3.6 27B 35.1%

Which one costs less?

GLM-5.1 costs $0.90 per million input tokens and $3.59 per million output tokens; Qwen3.6 27B costs $0.33 and $2.7.

For a typical agent task (read 20,000 tokens, write 2,000), expect $0.025 with GLM-5.1 versus $0.012 with Qwen3.6 27B. Qwen3.6 27B is 2.1 times cheaper, a gap that adds up quickly over 1,000 tasks: $13.2 difference.

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

Under the hood

Qwen3.6 27B reads more in one go: 262,144 tokens of context (roughly 393 pages), versus 200,000 for GLM-5.1.

Reasoning mode: GLM-5.1 yes, Qwen3.6 27B yes. Tool calling: GLM-5.1 yes, Qwen3.6 27B yes. Image input: GLM-5.1 no, Qwen3.6 27B yes.

Both publish their weights: you can host them yourself.

In short

Our reading of the data:

• Choose Qwen3.6 27B for a higher capability index, a lower cost, a larger context window and image input.

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