Qwen3-VL-32B-Instruct

Qwen3-VL-32B-Instruct is the flagship dense model of the Qwen3-VL family with 32 billion parameters, demonstrating top-tier results on a wide range of multimodal benchmarks. The model achieves performance comparable to leading proprietary systems like GPT-5 and Claude 4 Sonnet, while surpassing other open-source models. The model's architectural foundation is built upon three key innovations. Interleaved-MRoPE implements full-spectrum positional information distribution across the temporal axis and spatial dimensions (width/height), providing robust positional encoding for efficient video analysis. DeepStack integrates Vision Transformer and text features, significantly enhancing their alignment. Text-Timestamp Alignment ensures precise timestamp anchoring of events for temporal video modeling. This architecture enables the model to process visual and textual information simultaneously.

The model natively supports a 256K token context window with the capability to extend up to 1M tokens, allowing it to process extensive documents, entire books, or long videos with full memory recall and accurate information extraction. Visual perception is enhanced through large-scale, high-quality training—the model can recognize celebrities, anime characters, products, locations, flowers, animals, and other objects. The OCR system supports 32 languages (compared to 19 in previous versions), including Greek, Hebrew, Hindi, Romanian, Thai, and many others, with improved robustness under challenging conditions like poor lighting, blur, and skew. Enhanced accuracy in recognizing rare symbols, ancient scripts, and technical terminology expands the model's applicability in specialized domains. The model can function as a visual agent, recognizing user interface elements on computers and mobile devices, understanding their functions, capable of invoking tools, and confidently executing multi-step tasks.

Qwen3-VL-32B-Instruct is optimal for enterprise-grade visual analysis applications with high accuracy requirements. Automation of document, contract, and financial report processing. Advanced visual agents for UI testing automation, RPA scenarios, and managing complex GUI systems. Large-scale video analytics for security, retail, and media with temporal event localization, indexing, and search. Multimodal big data analytics, processing visual and textual data in a unified pipeline. Code generation from designs to accelerate front-end application development.

Announce Date: 22.10.2025
Parameters: 33B
Context: 263K
Layers: 64
Attention Type: Full Attention
VRAM requirements: 86.9 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-32B-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.