Qwen3-VL-4B-Thinking

Qwen3-VL-4B-Thinking is a compact 4-billion-parameter model with enhanced reasoning chain capabilities, optimized for solving multimodal tasks that require analysis. The model represents a unique combination of deployment efficiency and advanced reasoning abilities with minimal hardware requirements.

Architecturally, the model inherits all three key innovations of the Qwen3-VL series: Interleaved-MRoPE ensures precise spatio-temporal understanding of video content, DeepStack enables the extraction of fine-grained details through multi-level fusion of visual features, and Text-Timestamp Alignment provides second-level accuracy for event localization. The context window is 256K tokens, expandable to 1M, and the recommended output sequence length has been increased to 40,960 tokens to provide sufficient space for extensive reasoning chains.

On reasoning and multimodal benchmarks , the model consistently ranks among the top multimodal models in its class, outperforming many solutions of a similar size, including those from Microsoft, OpenAI, Google, and others.Application scenarios for Qwen3-VL-4B-Thinking include: educational applications, scientific research tasks, intelligent processing of complex documents with information extraction from specific fields according to required templates, and visual data verification tools.

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-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.