Qwen3-VL-8B-Instruct

Qwen3-VL-8B-Instruct is a multimodal model with 8 billion parameters, offering an optimal balance between performance and deployment efficiency. Built upon the Qwen3-8B language model with an integrated Vision Transformer-based visual encoder, it provides seamless understanding of text, images, and videos. Thanks to architectural innovations—Interleaved-MRoPE, DeepStack, and Text-Timestamp Alignment—the model demonstrates superior multimodal comprehension, surpassing its predecessor, Qwen2.5-VL-7B, across all key accuracy metrics, while achieving a 15-60% increase in token generation speed and a 20-40% reduction in response latency.

A key feature of the model is its native support for a 256K token context window, expandable up to 1 million tokens. This enables the processing of entire books, multi-hour videos, and complex multi-page documents with full context retention. Enhanced OCR capabilities supporting 32 languages (up from 19 in the previous version) and robustness to challenging capture conditions make Qwen3-VL-8B-Instruct an ideal solution for intelligent document processing. The model accurately recognizes text under low light, blur, and tilt conditions, handles rare and ancient characters, and understands complex long-document structures. On the DocVQA benchmark, it shows a significant advantage due to its improved document structure parsing.

The model was trained on a significantly enriched multimodal corpus, ensuring nearly complete coverage of real-world object categories (faces, natural landscapes, products, and interfaces). In this context, its visual agent capabilities are particularly noteworthy: Qwen3-VL-8B-Instruct can recognize GUI elements (buttons, input fields, menus), understand their functions, and execute complex action sequences on PCs and mobile devices. It generates functional HTML/CSS/JavaScript code and Draw.io diagrams from images, significantly accelerating interface prototyping. Advanced spatial perception with support for 2D and 3D object localization opens up possibilities for applications in robotic vision and embodied AI.

Announce Date: 15.10.2025
Parameters: 9B
Context: 263K
Layers: 36
Attention Type: Full Attention
VRAM requirements: 46.2 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-8B-Instruct 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.