Overview

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Qwen3-Coder is the code iteration of the Qwen3 large language model series developed by the Qwen team. This model is particularly designed for agentic coding tasks and comes in various sizes, offering performance in both coding and agentive functions.

The Qwen3-Coder-Next model has been specially trained on large-scale executable task synthesis, environment interaction and reinforcement learning. This advanced training has provided it with robust coding and agentive functions while keeping inference costs significantly lower.

A significant feature of Qwen3-Coder is its efficiency-performance trade-off, making it highly effective in foundational coding tasks and agentic browser use.

It can support most platforms including Qwen Code, CLINE, and Claude Code, and features a function call format designed specifically for it. Qwen3-Coder can handle long-context capabilites with inherent support for 256K tokens, extendable up to 1M tokens using Yarn.

This allows it to optimize for repository-scale understanding. The model can understand and generate in a long context length of 256K tokens and supports 358 coding languages.

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Initial release

July 22, 2025

Initial release of Qwen3-Coder.

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Pros and Cons

Pros

Various model sizes

Large-scale task synthesis training

Robust agentive functions

Low inference costs

Efficiency-performance trade-off

Agentic browser use

Support for Qwen Code

Support for CLINE

Support for Claude Code

Specialized function call format

Long-context capabilities

Support for 256K tokens

Extendable to 1M tokens

Optimized for repository scale

Support for 358 languages

GitHub integration

Specialized for developers

Environment interaction training

Supports reinforcement learning

Advanced training

Support for Web Development

Support for software development

View 17 more pros

Cons

Supports 358 not all languages

Inefficiency in long-context abilities

Inconsistent performance across languages

Heavy inference cost

Special formatting requirements

Potentially complex to implement

Limited to non-thinking mode

Function calling depends on parser

Not all models support 'Fill-in-the-Middle'

Requires updated tokenizer for consistency

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Q&A

What is Qwen3-Coder?

Qwen3-Coder is the code iteration of the Qwen3 large language model series developed by the Qwen team. It's specifically engineered for agentic coding tasks of various size, offering both coding and agentive functions.

What key features does Qwen3-Coder offer?

Qwen3-Coder offers key features such as a high efficiency-performance trade-off and strong performance in foundational coding tasks and agentic browser usage. It supports most platforms including Qwen Code, CLINE, and Claude Code, and is designed with a unique function call format. One of its notable features is its long-context capabilities, handling up to 256K tokens, and it can be extended up to 1M tokens using Yarn which optimizes repository-scale understanding.

What is the Qwen3-Coder-Next model?

Qwen3-Coder-Next is a model that has been specially trained on large-scale executable task synthesis, environment interaction, and reinforcement learning. This advanced training has provided it with robust coding and agentive functions reducing inference costs significantly. It is built on top of Qwen3-Next-80B-A3B-Base and boasts a novel architecture with hybrid attention and MoE.

What coding languages does Qwen3-Coder support?

Qwen3-Coder supports 358 coding languages, facilitating a wide range of coding and development tasks across various platforms.

Can Qwen3-Coder handle long-context capabilities?

Yes, Qwen3-Coder is capable of handling long-context tasks. It inherently supports 256K tokens, enabling it to understand and generate in a long context length of 256K tokens.

How many tokens can Qwen3-Coder handle?

Qwen3-Coder can handle long-context capabilities with inherent support for 256K tokens.

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Can Qwen3-Coder support 1M tokens using Yarn?

Yes, Qwen3-Coder can extend its token handling capacity up to 1M tokens using the Yarn system. This allows it to even more effectively optimize for repository-scale understanding.

What is the efficiency-performance trade-off in Qwen3-Coder?

The efficiency-performance trade-off is a unique feature of Qwen3-Coder which ensures its high effectiveness. Despite its robust coding and agentive functions, it has been designed to keep inference costs significantly low, making it a highly efficient tool for various coding tasks and agentic browser use.

Does Qwen3-Coder support Qwen Code, CLINE, and Claude Code platforms?

Yes, Qwen3-Coder supports most platforms including Qwen Code, CLINE, and Claude Code.

What is the function call format designed for Qwen3-Coder?

Qwen3-Coder features a function call format that has been designed specifically for it, supporting its robust coding and agentive capacities.

Is there a GitHub repository for Qwen3-Coder?

Yes, there is a GitHub repository specifically for Qwen3-Coder. It can be accessed under the QwenLM username with the repository name Qwen3-Coder.

What training techniques make Qwen3-Coder unique?

What makes Qwen3-Coder unique is the advanced training it has undergone for tasks such as large-scale executable task synthesis, environment interaction and reinforcement learning. This has equipped it with robust coding and agentive functions while keeping the inference costs significantly lower. Its architecture, built on top of Qwen3-Next-80B-A3B-Base, is another factor that enhances its performance.

What type of tasks is Qwen3-Coder particularly good at handling?

Qwen3-Coder is particularly effective at handling foundational coding tasks and agentic browser use, owing to its specialist design and advanced training in large-scale executable task synthesis, environment interaction, and reinforcement learning.

What are 'agentic coding tasks'?

Agentic coding tasks refer to coding activities that involve autonomous functions. These tasks often require the coding model to interact with its environment and make decisions to accomplish the task, much like an agent would.

How does Qwen3-Coder optimize for repository-scale understanding?

Qwen3-Coder optimizes for repository-scale understanding through its support for long-context capabilities. It inherently supports 256K tokens and can be extended up to 1M tokens using Yarn. This allows it to handle large coding models and tasks, enhancing its efficacy and performance.

What makes Qwen3-Coder highly effective in foundational coding tasks?

Qwen3-Coder's high level of efficacy in foundational coding tasks stems from its design and training. It has been subject to advanced training on large-scale executable task synthesis, environment interaction, and reinforcement learning, which has resulted in robust coding capabilities while maintaining a low inference cost.

How does Qwen3-Coder handle agentic browser use?

As part of its agentive functions, Qwen3-Coder handles agentic browser use. This refers to its ability to perform actions in a web environment armed with its training in environment interaction.

How does reinforcement learning and environment interaction enhance Qwen3-Coder?

Both reinforcement learning and environment interaction enhance the capabilities of Qwen3-Coder. Reinforcement learning allows the model to learn from its environment and improve its performance over time, while environment interaction equips the model to better interact with and function within the parameters of the environment it operates in.

What is the inference cost of Qwen3-Coder?

Qwen3-Coder is designed to maintain a significantly lower inference cost despite its robust capabilities and performance. This is achieved through its unique training and design, which enables it to provide high efficiency while carrying out various coding tasks.

What is the role of the Qwen3-Next-80B-A3B-Base in the Qwen3-Coder-Next model?

The Qwen3-Next-80B-A3B-Base plays a crucial role in the Qwen3-Coder-Next model as it forms the architectural foundation of the model. With a hybrid attention and MoE, the Qwen3-Next-80B-A3B-Base provides the framework for the Qwen3-Coder-Next model to function effectively in its agentic coding tasks, environment interaction, and reinforcement learning.

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