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Model details

Qwen3.6 Max Preview

Qwen3.6 Max Preview sits inside the Qwen3.6 model family as a higher-end successor to Qwen3.6-Plus and the open-source Qwen3.6 series. It is positioned as a closed-weight flagship, meaning the model weights are not publicly released and access is delivered through cloud API endpoints rather than local deployment. That positioning places it in the same competitive space as other proprietary frontier-scale language models, where the value comes from raw capability and integration options rather than from self-hosting flexibility.

The model is described as being built around two intertwined strengths: high-end reasoning and agentic coding. Practically, this means it is intended for autonomous and tool-using coding agents that handle complex software engineering work, as well as for general assistant scenarios that require precise instruction following and long-context document or workspace analysis. Its focus on agentic workflows suggests it is best suited to teams building coding assistants, browser-research tools, or multi-step automation pipelines, rather than to users who mainly need lightweight chat or short-form generation.

Ofoxqwen/qwen3.6-max-previewqwen

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Provider
Ofox
Model key
qwen/qwen3.6-max-preview
Release date
Apr 20, 2026
Last updated
Apr 20, 2026
Knowledge cutoff
2025-04
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$2.15
Output token cost
$12.86

Limits

Output tokens
64,000 tokens
Context window
256,000 tokens

Transparent token rates

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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Latest news about Qwen3.6 Max Preview

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CoveragePreview

Alibaba announced Qwen3.6-Max-Preview on April 20, 2026, as an early-access version of its upcoming flagship model. According to the supplied coverage, the model achieved the top ranking for "best domestic model" on the Artificial Analysis third-party evaluation platform, surpassing other Chinese models. The preview is The preview's core improvements center on world knowledge, instruction following, and agent programming. Alibaba reports benchmark gains of +9.9 on SkillsBench, +10.8 on SciCode, and +3.8 on Terminal-Bench 2.0 compared to its predecessor Qwen3.6-Plus. World knowledge improved by +2.3 on SuperGPQA and +5.3 on QwenChines

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