| Specification | Details |
|---|---|
| Model Name | Qwen3-VL-30B-A3B |
| Developer / Team | Alibaba Qwen AI Team |
| Architecture | Transformer with Mixture-of-Experts (MoE) |
| Total Parameters | 30.5 B |
| Activated Parameters | ~3.3 B |
| Attention Heads | Grouped (32 Q / 4 KV) |
| Layers | ~48 |
| Native Context Length | 256,000 tokens (text + vision) |
| Extended Context | Up to ~1,000,000 tokens (via extension techniques) |
| Modalities | Text, Image, Video, OCR |
| Input Types | Text, Images, Video streams |
| Output Types | Text |
| License | Apache 2.0 (Open Source) |
What Is Qwen3-VL-30B-A3B?
Qwen3-VL-30B-A3B is one of the Mixture-of-Experts variants in the Qwen3-VL series — built specifically as a vision-language foundation model. This means it can ingest long sequences of text plus visual content (images, video frames, document scans) and generate sophisticated responses grounded in both modalities.
Unlike earlier vision models, this version is engineered for real-world, extended context comprehension, enabling capabilities like:
- Two-hour video scanning and indexing, matching visual inputs with text descriptions.
- OCR across multiple languages and challenging inputs (low light, tilted text).
- Complex multimodal reasoning and chart/document analysis with best-in-class benchmarks.
Main Features
1) Multimodal Integration
The model fuses text, images, and video into a single context, enabling complex understanding such as chart interpretation, object recognition, and spatial reasoning.
2) Extended Context Support
Supports 256K tokens natively and can be expanded up to ~1M tokens — one of the largest context windows among open models.
3) Efficient Mixture-of-Experts (MoE)
Activates only ~3 B of the 30 B total parameters during inference, striking a balance between performance and efficiency.
4) Strong Benchmark Performance
Delivers leading results on multimodal tests (OCR, vision-QA, video comprehension, design-to-code).
5) Multilingual and OCR Support
Built-in support for 32+ OCR languages and strong performance across multilingual text, enabling broad global usability.
Limitations
Despite strong capabilities, the model has known challenges:
- Inference Complexity: MoE models can be slower or more resource-intensive than smaller dense models in some scenarios, depending on hardware and execution engine.
- Inconsistency Reports: Some users report variable output quality in reasoning modes and occasional hallucinations compared with dense models.
- Deployment Requirements: Large context and multimodal functionality demand high memory and optimized stack (e.g., vLLM, GPU support).
Comparison to Other Models
| Model | Strengths | Trade-offs |
|---|---|---|
| Qwen3-VL-30B-A3B | Efficient MoE multimodal reasoning, long contexts, open-source | Complexity, mixed performance reports |
| Qwen3-VL-235B-A22B | Highest unimodal/multimodal performance | Higher compute / cost |
| Dense Models (e.g., Qwen3-32B) | Simpler inference, consistent behavior | Homogeneous scaling, lower efficiency |
| Closed Models (GPT-5 / Gemini) | Established benchmarks, ecosystem integration | Closed weight access, cost & privacy concerns |
Alibaba’s open approach for Qwen models aims to rival proprietary models with transparent performance and community adoption.
How to access Qwen3 VL-30B-A3B API
Step 1: Sign Up for API Key
Log in to cometapi.com. If you are not our user yet, please register first. Sign into your CometAPI console. Get the access credential API key of the interface. Click “Add Token” at the API token in the personal center, get the token key: sk-xxxxx and submit.
Step 2: Send Requests to Qwen3 VL-30B-A3B API
Select the “Qwen3-VL-30B-A3B” endpoint to send the API request and set the request body. The request method and request body are obtained from our website API doc. Our website also provides Apifox test for your convenience. Replace <YOUR_API_KEY> with your actual CometAPI key from your account. base url is Chat
Insert your question or request into the content field—this is what the model will respond to . Process the API response to get the generated answer.
Step 3: Retrieve and Verify Results
Process the API response to get the generated answer. After processing, the API responds with the task status and output data.