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What is Qwen 4?

Qwen 4 is Alibaba’s forthcoming next-generation foundation model and the planned successor to the company’s Qwen3.x model family. Alibaba announced Qwen 4 at the 2026 Apsara Conference on September 22 and confirmed that the model is currently in training. The company has not yet disclosed Qwen 4’s architecture, parameter count, context length, training-compute requirements, benchmark scores, pricing, licensing terms, or release schedule. Qwen 4 is being developed as Alibaba expands its broader AI stack across foundation models, multimodal systems, AI infrastructure, and agent-oriented cloud services. A major research direction surrounding Alibaba’s next generation of models is recursive self-improvement based on real-world tasks and empirical feedback. The company has already experimented with this approach using Qwen3.8-Max, allowing the model to participate in automated pipeline design, data validation, experimentation, error diagnosis, and post-training optimization. Alibaba reported that Qwen3.8-Max completed 33 iterative cycles during one such experiment and increased its Artificial Analysis score from 40 to 45. In a separate chip-design experiment, a Qwen model performed more than 10,000 EDA tool calls during over 60 hours of automated improvement work, illustrating Alibaba’s interest in long-horizon agentic tasks. These demonstrations describe the research program surrounding future Qwen development rather than confirmed features of Qwen 4 itself. Alibaba has also announced a longer-term roadmap in which Qwen 4.5 and Qwen 5 models are projected to reach between 5 trillion and 10 trillion parameters. Qwen 4 therefore remains a pre-release model, with detailed capabilities and access information expected to become clearer when Alibaba publishes its formal launch materials.

What is Qwen3.8-Flash-Next?

Qwen3.8-Flash-Next is a pioneering open-weight multimodal Mixture-of-Experts architecture that offers an initial look at the design meant for its successor, Qwen4. This model has been expertly crafted to enhance various aspects such as attention mechanisms, residual pathways, embeddings, and optimization strategies, thereby increasing its overall functionality, enhancing computational efficiency, expanding its model capacity, and ensuring stability during training. Its unique hybrid structure combines Gated DeltaNet, which effectively condenses historical information, with Qwen Sparse Attention, facilitating the selection of meaningful context on a micro-block scale to reduce both attention and indexing expenses for lengthy sequences. The Gated Residual feature enhances the residual pathway by incorporating four streams, which helps in dynamically regulating the information flow across different layers. Moreover, the N-gram Embedding cleverly merges large-scale local-pattern memory with minimal computational overhead for each token, with the capability to transfer to host memory for added efficiency. The entire model is built around a main network comprising 125 billion parameters, supplemented by an additional 51 billion parameters specifically for N-gram embeddings, activating only 6 billion parameters for each token processed. This advanced framework underscores the continuous evolution in machine learning architectures, laying the groundwork for exciting future innovations, and it exemplifies the increasing sophistication and potential of multimodal models in various applications.

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Integrations Supported

Alibaba Cloud
Alibaba Cloud Model Studio
Cherry Studio
Cline
ClinePass
Happy Shrimp 1.0
Hugging Face
Model Context Protocol (MCP)
ModelScope
Novita AI
Odysseus
OfoxAI
Ollama
OpenClaw
Python
Qwen
Qwen Code
Qwen Studio
QwenCloud
QwenWork

Integrations Supported

Alibaba Cloud
Alibaba Cloud Model Studio
Cherry Studio
Cline
ClinePass
Happy Shrimp 1.0
Hugging Face
Model Context Protocol (MCP)
ModelScope
Novita AI
Odysseus
OfoxAI
Ollama
OpenClaw
Python
Qwen
Qwen Code
Qwen Studio
QwenCloud
QwenWork

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

$2 per 1M (input)

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Alibaba

Date Founded

1999

Company Location

China

Company Website

qwen.ai

Company Facts

Organization Name

Alibaba

Date Founded

1999

Company Location

China

Company Website

qwen.ai/blog

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

AI Reasoning Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

Multimodal Models

Not specified

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

AI Reasoning Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

Multimodal Models

Not specified

Open-Weight AI Models

Not specified

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