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ease
features
design
support

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Description

CodeQwen serves as the coding counterpart to Qwen, which is a series of large language models created by the Qwen team at Alibaba Cloud. Built on a transformer architecture that functions solely as a decoder, this model has undergone extensive pre-training using a vast dataset of code. It showcases robust code generation abilities and demonstrates impressive results across various benchmarking tests. With the capacity to comprehend and generate long contexts of up to 64,000 tokens, CodeQwen accommodates 92 programming languages and excels in tasks such as text-to-SQL queries and debugging. Engaging with CodeQwen is straightforward—you can initiate a conversation with just a few lines of code utilizing transformers. The foundation of this interaction relies on constructing the tokenizer and model using pre-existing methods, employing the generate function to facilitate dialogue guided by the chat template provided by the tokenizer. In alignment with our established practices, we implement the ChatML template tailored for chat models. This model adeptly completes code snippets based on the prompts it receives, delivering responses without the need for any further formatting adjustments, thereby enhancing the user experience. The seamless integration of these elements underscores the efficiency and versatility of CodeQwen in handling diverse coding tasks.

Description

Qwen 4 is the upcoming fourth-generation foundation model in Alibaba’s Qwen AI family. Alibaba publicly confirmed at its 2026 Apsara Conference that the model is currently being trained. The company has not yet released technical specifications, model weights, API access, pricing, benchmark results, or a launch date for Qwen 4. Its development forms part of Alibaba’s broader effort to advance foundation models capable of increasingly complex and long-horizon work. The Qwen team is also researching recursive self-improvement, in which models use empirical feedback to identify weaknesses, design experiments, evaluate results, and iteratively improve training processes. Alibaba demonstrated this approach with Qwen3.8-Max, which completed 33 automated optimization cycles during a month-long experiment and improved its Artificial Analysis score from 40 to 45. These experiments provide context for Alibaba’s model-development direction but do not establish specific Qwen 4 capabilities. Alibaba has additionally outlined Qwen 4.5 and Qwen 5 models that could eventually scale to between 5 trillion and 10 trillion parameters. Until Qwen 4 is released, its exact architecture, modalities, performance, deployment options, and licensing remain to be announced.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

No images available

Integrations

Alibaba Cloud
Hugging Face
ModelScope
Ollama
Python
Qwen Studio
Alibaba Cloud Model Studio
Cline
Codeforces
DeepSeek Coder
GPT-4
Hermes Agent
LangChain
LlamaIndex
Odysseus
OfoxAI
PyTorch
Qwen
Qwen Code
StarCoder

Integrations

Alibaba Cloud
Hugging Face
ModelScope
Ollama
Python
Qwen Studio
Alibaba Cloud Model Studio
Cline
Codeforces
DeepSeek Coder
GPT-4
Hermes Agent
LangChain
LlamaIndex
Odysseus
OfoxAI
PyTorch
Qwen
Qwen Code
StarCoder

Pricing Details

Free
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Alibaba

Founded

1999

Country

China

Website

github.com/QwenLM/CodeQwen1.5

Vendor Details

Company Name

Alibaba

Founded

1999

Country

China

Website

qwen.ai

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