Qwen3.6 Plus
Open weightVisionToolsQwen · Released April 2026
Qwen 3.6 Plus builds on a hybrid architecture that combines efficient linear attention with sparse mixture-of-experts routing, enabling strong scalability and high-performance inference. Compared to the 3.5 series, it delivers...
It is part of Alibaba's Qwen family, a broad catalog where Max leads on capability, Plus balances cost, and Flash and the open-weight sizes cover high-volume work.
Facts and pricing
- Provider
- Qwen
- Context window
- 1M tokens
- Input price
- 3.6 credits / 1M tokens ($0.33 raw)
- Output price
- 21.4 credits / 1M tokens ($1.95 raw)
- Vision (image input)
- Yes
- Tool calling
- Yes
- Extended reasoning
- No
Credits are what Zeplik bills: 1 credit = $0.10, computed from the raw provider rate with a 1.10x margin. Raw prices shown per 1M tokens.
Try Qwen3.6 Plus now
What Qwen3.6 Plus is best for
- Everyday chat and drafting on an open-weight model with transparent lineage
- Very long documents and codebases: a 1M-token window fits entire books or repositories in one conversation
- Working with images: screenshots, charts, photos and scanned documents alongside text
- Tool use and agents: reliably calls functions, so it can search, run skills and drive workflows
Example prompts
Prompts that suit a open weight model like Qwen3.6 Plus:
- Draft a project update from these rough notes
- Explain how HTTPS works to a curious teenager
- Turn this list of features into a changelog entry
- Brainstorm objections to this proposal and how to answer them
Qwen family
| Model | Context | Input cr/M | Output cr/M | Released |
|---|---|---|---|---|
| Qwen3.7 Plus | 1M | 3.5 | 14.1 | June 2026 |
| Qwen3.7 Max | 1M | 13.8 | 41.3 | May 2026 |
| Qwen3.5 Plus 2026-04-20 | 1M | 3.3 | 19.8 | April 2026 |
| Qwen3.6 Flash | 1M | 2.1 | 12.4 | April 2026 |
| Qwen3.6 35B A3B | 262K | 1.5 | 11.0 | April 2026 |
| Qwen3.6 Max Preview | 262K | 11.4 | 68.6 | April 2026 |
| Qwen3.6 27B | 262K | 3.1 | 26.4 | April 2026 |
| Qwen3.6 Plus | 1M | 3.6 | 21.4 | April 2026 |
| Qwen3.5-9B | 262K | 1.1 | 1.7 | March 2026 |
| Qwen3.5-35B-A3B | 262K | 1.5 | 11.0 | February 2026 |
Frequently asked questions
- What is Qwen3.6 Plus?
- Qwen3.6 Plus is an AI model by Qwen. Qwen 3.6 Plus builds on a hybrid architecture that combines efficient linear attention with sparse mixture-of-experts routing, enabling strong scalability and high-performance inference. Compared to the 3.5 series, it delivers...
- How much does Qwen3.6 Plus cost on Zeplik?
- Input tokens cost 3.6 credits per million and output tokens 21.4 credits per million (1 credit = $0.10; the raw provider rates are $0.33 and $1.95 per million). New accounts start with free credits.
- How long can a conversation with Qwen3.6 Plus be?
- Qwen3.6 Plus has a 1M-token context window (1,000,000 tokens), which covers the conversation plus any documents you attach.
- Does Qwen3.6 Plus support images and tools?
- Qwen3.6 Plus accepts image input and supports tool calling.
Related models
Qwen3.7-Plus is a cost-effective model in Alibaba's Qwen3.7 series. It supports text and image input with text output, building on the series' text capabilities with a comprehensive upgrade to its...
Qwen3.7-Max is the flagship model in Alibaba's Qwen3.7 series. It supports text input and output and is designed for agent-centric workloads, with particular strengths in coding, office and productivity tasks,...
Qwen3.5 Plus (April 2026) is a large-scale multimodal language model from Alibaba. It accepts text, image, and video input and produces text output, with a 1M token context window. This...
Qwen3.6 Flash is a fast, efficient language model from Alibaba's Qwen 3.6 series. It supports text, image, and video input with a 1M token context window. Tiered pricing kicks in...
Qwen3.6-35B-A3B is an open-weight multimodal model from Alibaba Cloud with 35 billion total parameters and 3 billion active parameters per token. It uses a hybrid sparse mixture-of-experts architecture combining Gated...
Qwen3.6-Max-Preview is a proprietary frontier model from Alibaba Cloud built on a sparse mixture-of-experts architecture with approximately 1 trillion total parameters. It is optimized for agentic coding, tool use, and...