> ## Documentation Index
> Fetch the complete documentation index at: https://documentation.deepmask.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Qwen

> Qwen (DeepMask) and Qwen3 (StackIT) on DeepMask. Alibaba's flagship models with dual-mode reasoning, 1M token context, and EU-hosted infrastructure via StackIT.

Alibaba's Qwen3 models bring frontier-class reasoning and repository-scale coding to DeepMask in two deployment configurations. Qwen (DeepMask) is the 235B flagship with dual-mode inference, spatial-visual logic, and a 1M-token context window. Qwen3 (StackIT) is a StackIT-tuned variant co-developed for European cloud environments, with native infrastructure awareness and the same powerful reasoning core. Both models excel at multilingual tasks, document analysis, and agentic workflows.

<Tabs>
  <Tab title="Qwen (DeepMask)">
    ## About

    Qwen (DeepMask) is Alibaba's 235B Qwen3 flagship model. It features Dual-Mode Inference, allowing you to toggle between "Instant" mode for fast chat and "Thinking" mode for deep, PhD-level problem solving. It leads on repository-scale coding — able to reason across tens of thousands of lines of code without context drift — and supports a 1M-token context window with efficient hardware use via a tiered KV cache.

    <Note>
      Qwen (DeepMask) is hosted on DeepMask infrastructure with a 1M-token context window. Your data remains within DeepMask's EU-compliant environment.
    </Note>

    ## Key Capabilities

    <CardGroup cols={2}>
      <Card title="Dual-Mode Reasoning" icon="toggle-on">
        Toggles between fast chat and deep Thinking mode for complex, multi-step problem solving.
      </Card>

      <Card title="Spatial-Visual Logic" icon="image">
        Excels at understanding complex diagrams, maps, technical blueprints, and spatial relationships.
      </Card>

      <Card title="1M Token Context" icon="arrows-left-right">
        Handles up to 1 million tokens, enabling analysis of very large codebases and document sets.
      </Card>

      <Card title="Repository-Scale Coding" icon="code">
        Understands the architectural intent behind a codebase, enabling whole-repo reasoning and refactoring.
      </Card>
    </CardGroup>

    ## Use Cases

    * **Enterprise software architecture** — Plan and refactor multi-repository backend systems with full structural awareness.
    * **Global fintech analytics** — Process large volumes of financial data for predictive market analysis.
    * **Creative design suite** — Leverage native support for high-fidelity image understanding and natural speech tasks.
    * **Multilingual RAG** — Build retrieval-augmented generation pipelines across multiple languages with strong reasoning.

    <Tip>
      Qwen (DeepMask) is your best choice for repository-scale coding and complex reasoning tasks on DeepMask. Use Thinking mode for difficult problems and Instant mode for interactive chat at 0.22s TTFT.
    </Tip>

    ## Specifications

    | Specification    | Value                           |
    | ---------------- | ------------------------------- |
    | Model Provider   | Alibaba                         |
    | Main Use Cases   | Agents Coding, Multilingual RAG |
    | Reasoning Effort | High (Instant & Thinking)       |
    | GPQA Diamond     | 89.3%                           |
    | Max Context      | 1M Tokens                       |
    | Latency (TTFT)   | 0.22s (Non-Thinking Mode)       |
    | Throughput       | 145 Tokens/sec                  |
  </Tab>

  <Tab title="Qwen3 (StackIT)">
    ## About

    Qwen3 (StackIT) is a specialized variant of Alibaba's Qwen3 series, co-developed with StackIT for European enterprise cloud environments. It features Hybrid Thinking Modes, allowing it to alternate between a high-compute "Deep Logic" mode and a lightweight "Fast Action" mode via API toggle. This model is specifically tuned for infrastructure-as-code, cloud-native application management, and complex technical project workflows.

    <Note>
      Qwen3 (StackIT) is EU-hosted via StackIT, making it a strong choice for teams with European data residency requirements. It provides the same Qwen3 reasoning core with infrastructure-aware tuning and a 1M+ token context window.
    </Note>

    ## Key Capabilities

    <CardGroup cols={2}>
      <Card title="Dual-Mode Inference" icon="toggle-on">
        A single model that can think step-by-step or respond instantly — no separate model needed.
      </Card>

      <Card title="Infrastructure Awareness" icon="server">
        Strong at understanding cloud topologies and technical system configurations.
      </Card>

      <Card title="Long-Context Memory" icon="arrows-left-right">
        Maintains long-term project memory efficiently, suitable for large-scale document and code tasks.
      </Card>

      <Card title="Vision Capabilities" icon="image">
        Built-in vision to recognize and reason about architecture diagrams and visual inputs.
      </Card>
    </CardGroup>

    ## Use Cases

    * **Cloud infrastructure management** — Generate and debug complex multi-cloud deployment scripts with infrastructure-native understanding.
    * **Repository-scale refactoring** — Analyze 10,000+ line codebases and propose structural architectural changes.
    * **Technical project management** — Convert visual whiteboard sketches into technical PRDs and Jira tickets.
    * **Agentic workflows** — Run complex reasoning-heavy agent pipelines with EU data residency guarantees.

    <Tip>
      Choose Qwen3 (StackIT) when you need EU-hosted Qwen3 reasoning for cloud infrastructure, DevOps, or enterprise workflows. It shares the same reasoning core as Qwen (DeepMask) with added StackIT infrastructure awareness.
    </Tip>

    ## Specifications

    | Specification    | Value                                |
    | ---------------- | ------------------------------------ |
    | Model Provider   | Alibaba                              |
    | Hosting          | EU-hosted via StackIT                |
    | Main Use Cases   | Agentic Workflows, Complex Reasoning |
    | Reasoning Effort | High                                 |
    | GPQA Diamond     | 87.4%                                |
    | Max Context      | 1.01M Tokens                         |
    | Latency (TTFT)   | 0.35s                                |
    | Throughput       | 95 Tokens/sec                        |
  </Tab>
</Tabs>