MODEL PROFILE

Qwopus3.5-9B-Coder-GGUF

Jackrong · Qwopus3.5-9B-Coder-GGUF

As the base model of this model, **Qwopus3.5-9B-v3.5** is already a model with powerful capabilities. On this foundation, **Qwopus3.5-9B-coder** is specially optimized and fine-tuned for high-performance **🤖 Agentic Coding, complex Tool Calling, and logical reasoning.** > 💡 **Why the 9B Dense Model?** > We believe that the 9B dense architecture represents the perfect **"sweet spot"** for large language models. It runs seamlessly at 8-bit precision on entry-level 16GB RAM devices—such as standard laptops and the Mac mini—making it exceptionally lightweight yet highly versatile. Without requiring expensive hardware, it allows you to achieve excellent performance paired with impressive inference speeds. Simply put, **Qwen3.5-9B is currently the best open-source model in its class.** > [!TIP] >**Vision & Tool Calling Support**: This model supports visual capabilities and tool calling. To enable vision, please place the `mmproj.gguf` file from the GGUF repository into the same directory as the main `.gguf` file.

Visual QAmultimodalreasoningcoderlicense:apache-2.0
Context windowNot disclosed
Input → OutputText、Image → Text
Model weightsNot verified
Profile updated2026/10/30 07:57:37
OVERVIEW

Model overview

As the base model of this model, **Qwopus3.5-9B-v3.5** is already a model with powerful capabilities. On this foundation, **Qwopus3.5-9B-coder** is specially optimized and fine-tuned for high-performance **🤖 Agentic Coding, complex Tool Calling, and logical reasoning.** > 💡 **Why the 9B Dense Model?** > We believe that the 9B dense architecture represents the perfect **"sweet spot"** for large language models. It runs seamlessly at 8-bit precision on entry-level 16GB RAM devices—such as standard laptops and the Mac mini—making it exceptionally lightweight yet highly versatile. Without requiring expensive hardware, it allows you to achieve excellent performance paired with impressive inference speeds. Simply put, **Qwen3.5-9B is currently the best open-source model in its class.** > [!TIP] >**Vision & Tool Calling Support**: This model supports visual capabilities and tool calling. To enable vision, please place the `mmproj.gguf` file from the GGUF repository into the same directory as the main `.gguf` file.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

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Estimate monthly base token costs

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

Do open weights permit free commercial use?

Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.

Are these real-time billing prices?

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。