Qwen3-VL-4B-Instruct

Qwen3-VL-4B-Instruct is a compact 4-billion-parameter multimodal model designed for efficient deployment on resource-constrained servers while retaining the full functionality of the Qwen3-VL series. Despite being half the size of the 8B version, the model preserves all key architectural innovations: Interleaved-MRoPE for video understanding, DeepStack for multi-level visual feature fusion, and Text-Timestamp Alignment for precise temporal localization. The seamless integration of text and visual modalities provides an understanding of multimodal context at a level comparable to pure-text LLMs.

In terms of performance, Qwen3-VL-4B-Instruct approaches the results of Qwen2.5-VL-7B, demonstrating that the reduction in model size was achieved without significant loss of quality. The model supports a native context of 256K tokens (expandable to 1M), enabling the processing of long documents, multi-hour videos, and complex multimodal dialogues. Advanced OCR capabilities with support for 32 languages and resilience to challenging shooting conditions make the 4B model a full-fledged solution for intelligent document processing tasks, despite its compact size.

Qwen3-VL-4B-Instruct represents an ideal solution for scenarios requiring a balance between performance and efficiency: deployment on consumer devices, the ability to process large volumes of visual content, fast response times for integration into real-time applications, and research projects. Furthermore, the open Apache 2.0 license allows for free commercial use of the model, making it accessible to a wide range of users, from startups to large enterprises.

Announce Date: 15.10.2025
Parameters: 4B
Context: 263K
Layers: 36
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-4B-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.