Qwen3-VL-2B-Thinking

Qwen3-VL-2B-Thinking is a reasoning-oriented version of the base 2B-Instruct model, specifically optimized for tasks requiring detailed analysis. A specialized reinforcement learning process taught the model to employ chains of thought when processing visual information. The model's architecture inherits all three key innovations of the base Qwen3-VL version: Interleaved-MRoPE for enhanced video understanding, DeepStack for multi-level fusion of visual and textual features, and Text-Timestamp Alignment for precise temporal localization in video content.

The model supports a 256K token context window, extendable to 1M tokens, enabling it to process multi-page documents and long videos while maintaining context to build accurate logical connections during reasoning generation. An OCR system supporting 32 languages ensures multilingual text recognition in complex shooting conditions. A key feature is the model's ability to self-check and verify its answers when solving visual tasks, which enhances the reliability of the results.

Qwen3-VL-2B-Thinking is optimal for applications on resource-constrained devices that still require detailed analysis of visual information and explainable answers or agent-based decisions. The model is effective for developing intelligent quality inspection systems with detailed descriptions of detected defects. However, its greatest value lies in research prototypes, where the transparency of the reasoning process helps to accelerate and optimize fine-tuning and debugging workflows.

Announce Date: 22.10.2025
Parameters: 2B
Context: 263K
Layers: 28
Attention Type: Full Attention
Developer: Qwen
Transformers Version: 4.57.0.dev0
License: Apache 2.0

Use our pre-built public endpoints for free to test inference and explore Qwen3-VL-2B-Thinking 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.