Qwen3.6 27B, the Alibaba Cloud (Qwen) model in numbers

Qwen3.6 27B at a glance

Qwen3.6 27B is a language model from Alibaba Cloud (Qwen), released on April 22, 2026. That makes it 6 months old as of October 22, 2026. Its weights are published: you can download it and run it on your own servers, or use one of the 28 API providers that list it on models.dev. It has a reasoning mode: it can "think" before answering, which helps with multi-step problems but uses more tokens, and therefore more money.

We file it under open-weight models (68 models in this category on the site).

What do independent benchmarks say?

According to results published by Epoch AI (checked on October 22, 2026), Qwen3.6 27B has a capability index of 72.2 out of 100, computed on 3 benchmarks from our basket.

With only 3 benchmarks reported, it does not enter the public ranking, which requires at least 4 results, so its index should be read as partial.

• GPQA Diamond (PhD-level science questions): 85.9% — the best tracked model, Gemini 4 Argon, reaches 96.4%
• OTIS Mock AIME (competition mathematics problems): 91.1% — the best tracked model, GPT-5.5 Pro, reaches 100%
• FrontierMath (very hard research-level mathematics): 35.1% — the best tracked model, GPT-6 Astra, reaches 93.7%

What it costs

Qwen3.6 27B costs $0.33 per million input tokens and $2.7 per million output tokens (median of 28 providers, checked on October 22, 2026).

To put it in perspective: reading 20,000 tokens (about thirty pages) and writing 2,000 costs roughly $0.012; a thousand of those tasks, $11.9.

That is 2.7 times the median output price of open-weight models ($1).

To estimate your own budget, use the agent cost calculator.

Technical specifications

Its context window reaches 262,144 tokens, roughly 393 pages of text at 500 words per page: enough for a long report or several contracts at once. A single answer can run to 65,536 tokens.

It accepts as input: text, image and video.

It can call tools (functions, APIs) and return structured JSON output, which makes it a possible foundation for an AI agent.

The weights are published on Hugging Face (Qwen/Qwen3.6-27B). See also our list of open-source models.

What is it good for?

Given its category, Qwen3.6 27B is mainly aimed at projects where you want control over your data or hosting, or want to adapt the model to your field.

Before adopting it, test it on your own examples: benchmarks measure standard tasks, not your job.

Alternatives

In the same category, the models closest to Qwen3.6 27B are:

• DeepSeek V4 Pro 0813 (DeepSeek, index 72.2) — see the comparison
• GLM-5.3-Flash (Zhipu AI (Z.ai), index 71.9) — see the comparison
• GLM-5.1 (Zhipu AI (Z.ai), index 70.9) — see the comparison
• Kimi K2 Thinking (Moonshot AI, index 69.6)