LangMart: Qwen: Qwen3 VL 8B Instruct
Model Overview
| Property | Value |
|---|---|
| Model ID | openrouter/qwen/qwen3-vl-8b-instruct |
| Name | Qwen: Qwen3 VL 8B Instruct |
| Provider | qwen |
| Released | 2025-10-14 |
Description
Qwen3-VL-8B-Instruct is a multimodal vision-language model from the Qwen3-VL series, built for high-fidelity understanding and reasoning across text, images, and video. It features improved multimodal fusion with Interleaved-MRoPE for long-horizon temporal reasoning, DeepStack for fine-grained visual-text alignment, and text-timestamp alignment for precise event localization.
The model supports a native 256K-token context window, extensible to 1M tokens, and handles both static and dynamic media inputs for tasks like document parsing, visual question answering, spatial reasoning, and GUI control. It achieves text understanding comparable to leading LLMs while expanding OCR coverage to 32 languages and enhancing robustness under varied visual conditions.
Description
LangMart: Qwen: Qwen3 VL 8B Instruct is a language model provided by qwen. This model offers advanced capabilities for natural language processing tasks.
Provider
qwen
Specifications
| Spec | Value |
|---|---|
| Context Window | 131,072 tokens |
| Modalities | text+image->text |
| Input Modalities | image, text |
| Output Modalities | text |
Pricing
| Type | Price |
|---|---|
| Input | $0.06 per 1M tokens |
| Output | $0.40 per 1M tokens |
Capabilities
- Frequency penalty
- Logit bias
- Max tokens
- Min p
- Presence penalty
- Repetition penalty
- Response format
- Seed
- Stop
- Structured outputs
- Temperature
- Tool choice
- Tools
- Top k
- Top p
Detailed Analysis
Qwen3-VL-8B-Instruct is a compact yet powerful vision-language model from the Qwen 3 series released October 2025, featuring significant architectural improvements over Qwen2.5-VL. Key characteristics: (1) Architecture: 8B parameters with Interleaved-MRoPE for enhanced long-horizon video reasoning, DeepStack multi-level feature fusion, and text-timestamp alignment for precise event localization; supports native 256K-token context, extensible to 1M tokens; (2) Capabilities: Expanded OCR supporting 32 languages (up from 10 in Qwen2.5-VL) with improved robustness to low-light/blur/tilt, text understanding on par with pure LLMs, advanced visual agent functionality for operating GUIs, hour-long video analysis with second-level event extraction; (3) Performance: Despite smaller size than Qwen2.5-VL-32B/72B, achieves competitive or superior performance on many benchmarks due to architectural improvements and training on 36T tokens; (4) Use Cases: Multilingual document processing, visual agents for computer/mobile interfaces, long-form video understanding, multimodal chatbots, autonomous systems requiring visual perception; (5) Context Window: 256K tokens native, 1M token extension available; (6) Trade-offs: Cutting-edge model with latest capabilities but less battle-tested than 2.5-VL series. Best for applications needing latest VL features, multilingual OCR, or visual agent capabilities in a compact package.