LangMart: Qwen: Qwen3 VL 30B A3B Instruct
Model Overview
| Property | Value |
|---|---|
| Model ID | openrouter/qwen/qwen3-vl-30b-a3b-instruct |
| Name | Qwen: Qwen3 VL 30B A3B Instruct |
| Provider | qwen |
| Released | 2025-10-06 |
Description
Qwen3-VL-30B-A3B-Instruct is a multimodal model that unifies strong text generation with visual understanding for images and videos. Its Instruct variant optimizes instruction-following for general multimodal 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 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 | 262,144 tokens |
| Modalities | text+image->text |
| Input Modalities | text, image |
| Output Modalities | text |
Pricing
| Type | Price |
|---|---|
| Input | $0.15 per 1M tokens |
| Output | $0.60 per 1M tokens |
Capabilities
- Frequency penalty
- Logit bias
- Logprobs
- Max tokens
- Min p
- Presence penalty
- Repetition penalty
- Response format
- Seed
- Stop
- Structured outputs
- Temperature
- Tool choice
- Tools
- Top k
- Top logprobs
- Top p
Detailed Analysis
Qwen3-VL-30B-A3B-Instruct is a Mixture-of-Experts (MoE) vision-language model from the Qwen 3 series released October 2025, offering large-scale capabilities with efficient inference. Key characteristics: (1) Architecture: 30B total parameters with ~3B activated per forward pass (A3B = Activated 3B), achieving ~90% compute reduction vs dense 30B model; includes Qwen3-VL improvements (Interleaved-MRoPE, DeepStack, text-timestamp alignment) with MoE expertise specialization; (2) Capabilities: All Qwen3-VL features including 32-language OCR, visual agent functionality, long-video understanding with hour+ support, sophisticated document parsing, precise object detection, and temporal event localization; (3) Performance: Approaches or exceeds dense 30B model quality on most tasks while using fraction of compute; expert specialization improves efficiency on diverse visual tasks; (4) Use Cases: Cost-sensitive production deployments requiring large model capabilities, high-throughput visual processing, multimodal applications at scale, visual agents for automation, multilingual document processing pipelines; (5) Context Window: 256K tokens with dynamic resolution; (6) Trade-offs: MoE complexity may affect latency predictability vs dense models; cutting-edge architecture. Best for production applications needing flagship-level vision-language capabilities with optimized cost per request through sparse activation.