Qwen3-235B-A22B

Qwen3-235B-A22B is the flagship model of the Qwen3 series and one of the largest open language models in the world. It has a total parameter count of 235 billion, with 22 billion parameters activated for each token. This was made possible by an efficient Mixture of Experts (MoE) architecture that includes 128 experts, of which only 8 are engaged at each computational step. Innovative improvements to the attention mechanism ensure high accuracy in context processing and support sequence lengths of up to 40K tokens.

One of the key features of Qwen3-235B-A22B is its support for two operational modes: thinking and no-thinking . In thinking mode , the model applies extended logical reasoning and additional computational resources to deeply analyze tasks, achieving the highest level of precision and depth in its responses. The no-thinking mode, on the other hand, is optimized for quickly performing simple tasks such as text formatting, translation, or short-answer queries, without unnecessary use of computing power. This functionality gives users flexibility in balancing speed and output quality.

Qwen3-235B-A22B can be applied in scientific research, software development, test automation, technical documentation processing, and AI agent creation. It is suitable for academic environments, as well as government and corporate projects where high accuracy, scalability, and flexible task customization are essential. Its support for 119 languages also makes it convenient for international use.

Announce Date: 29.04.2025
Parameters: 235B
Experts: 128
Activated at inference: 22B
Context: 41K
Layers: 94
Attention Type: Full or Sliding Window Attention
Developer: Qwen
Transformers Version: 4.51.0
vLLM Version: 0.8.5
License: Apache 2.0

Use our pre-built public endpoints for free to test inference and explore Qwen3-235B-A22B capabilities. You can obtain an API access token on the token management page after registration and verification.

An endpoint is a server with pre-installed software that automatically downloads neural network weights and launches an OpenAI-compatible API to handle incoming requests.

A private endpoint is created within the user's project, belongs exclusively to the user, and is billed per server configuration in accordance with the infrastructure pricing.

We recommend creating private endpoints when you need to:

• achieve maximum endpoint performance,
• access the full context window for processing large requests and conversations,
• ensure maximum security by processing data in a dedicated, isolated environment.

Contact our dedicated neural networks support team at support@immers.cloud or send your request to the sales department at sale@immers.cloud.