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
VRAM requirements: 32.7 GB using 4 bits quantization
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.

Rent your own physically dedicated instance with hourly or long-term monthly billing.

We recommend deploying private instances in the following scenarios:

• maximize endpoint performance,
• enable full context for long sequences,
• ensure top-tier security for data processing in an isolated, dedicated environment,
• use custom weights, such as fine-tuned models or LoRA adapters.

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