LangMart: Qwen: Qwen3 VL 30B A3B Thinking
Model Overview
| Property | Value |
|---|---|
| Model ID | openrouter/qwen/qwen3-vl-30b-a3b-thinking |
| Name | Qwen: Qwen3 VL 30B A3B Thinking |
| Provider | qwen |
| Released | 2025-10-06 |
Description
Qwen3-VL-30B-A3B-Thinking is a multimodal model that unifies strong text generation with visual understanding for images and videos. Its Thinking variant enhances reasoning in STEM, math, and complex tasks. It excels in perception of real-world/synthetic categories, 2D/3D spatial grounding, and long-form visual comprehension, achieving competitive multimodal benchmark results. For agentic use, it handles multi-image multi-turn instructions, video timeline alignments, GUI automation, and visual coding from sketches to debugged UI. Text performance matches flagship Qwen3 models, suiting document AI, OCR, UI assistance, spatial tasks, and agent research.
Description
LangMart: Qwen: Qwen3 VL 30B A3B Thinking 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 | text, image |
| Output Modalities | text |
Pricing
| Type | Price |
|---|---|
| Input | $0.16 per 1M tokens |
| Output | $0.80 per 1M tokens |
Capabilities
- Frequency penalty
- Include reasoning
- Max tokens
- Presence penalty
- Reasoning
- Repetition penalty
- Response format
- Seed
- Stop
- Structured outputs
- Temperature
- Tool choice
- Tools
- Top k
- Top p
Detailed Analysis
Qwen3-VL-30B-A3B-Thinking is the reasoning-enabled variant of the Qwen3-VL MoE model, combining efficient sparse activation with transparent visual reasoning. Key characteristics: (1) Architecture: 30B total parameters with ~3B activated per token plus explicit reasoning mode; MoE structure with Qwen3-VL enhancements (Interleaved-MRoPE, DeepStack) and toggleable /think and /no_think tokens; (2) Capabilities: Full Qwen3-VL-30B capabilities (32-language OCR, visual agents, long videos) with step-by-step visual reasoning transparency; shows how the model analyzes visual inputs, interprets spatial relationships, and reaches conclusions through expert activation patterns; (3) Performance: Enhanced accuracy on complex multimodal reasoning tasks through reasoning exposure; MoE architecture provides computational efficiency while reasoning mode improves quality; (4) Use Cases: Explainable visual AI systems, high-stakes visual decision support requiring audit trails, visual reasoning education, debugging multimodal systems, applications requiring both performance and transparency, cost-sensitive deployments needing reasoning capabilities; (5) Context Window: 256K tokens (reasoning consumes additional context); (6) Trade-offs: MoE latency variability plus reasoning token overhead; provides unique combination of efficiency (sparse activation) and transparency (reasoning mode). Best for production systems requiring both cost optimization and explainability in visual understanding tasks.