| Field | Value / Notes |
|---|---|
| Model name | Qwen3-VL-32B (Instruct / Thinking variants available). |
| Model family / architecture | Qwen3-VL — vision-language transformer; multimodal backbone with ViT-style visual encoder + LLM fusion layers. |
| Parameter count | Named “32B” class (public sources list ~32–33B parameter scale for the dense 32B variant). |
| Variants | Dense: 2B / 4B / 8B / 32B; MoE: 30B-A3B, 235B-A22B (larger MoE variants also released). |
| Native context length | 256K tokens (native interleaved multimodal context), with engineered extension modes/techniques enabling up to ~1M tokens in some deployments. |
| Input modalities | Text + images (high-resolution) + long video (temporal modeling/timestamps) + OCR (multilingual). |
| Output modalities | Text (natural language), structured extraction (OCR/table/chart extraction), timestamps/segment summaries for video; supports tool use / agent calls. |
What Qwen3-VL-32B is
Qwen3-VL-32B is the 32-billion-parameter dense variant in Alibaba’s Qwen3 vision-language model family. It is a multimodal (vision + language + video) transformer designed for unified perception, long-context reasoning, robust OCR and visual grounding, and agentic/toolified workflows.
Main features
- Large multimodal context — Native support for 256K interleaved tokens (text + image references) and architectural hooks / tooling to extend effective context to ~1M tokens for long documents and long videos; enables cross-document cross-media retrieval and reasoning.
- Unified visual + language pretraining — Joint training from early stages improving language grounding to visual inputs, leading to stronger cross-modal representations (beneficial for VQA, OCR, and diagram reasoning).
- Video comprehension & temporal alignment — Native video handling with timestamped text alignment and the ability to summarize or index long video streams at fine temporal granularity.
- Multilingual OCR and document parsing — High-quality OCR across many languages and robust document/layout understanding for table and chart extraction use cases.
- Instruct vs Thinking variants — Separate builds optimized for instruction compliance (Instruct) vs. deep internal chain-of-thought / reasoning throughput (Thinking) to suit application needs (safety/conciseness vs. stepwise reasoning).
- MoE options for scaling — For extreme capacity/coverage there are MoE variants (30B-A3B, 235B-A22B) that increase representational capacity while attempting to control inference compute via expert routing.
Where Qwen3-VL-32B is well-suited
- Document and form extraction at scale — robust OCR across languages, table and chart extraction, and semantic summarization of long reports.
- Visual question answering for complex images — medical/engineering diagrams, annotated photos, or visual troubleshooting that require integrating visual evidence with stepwise textual reasoning.
- Long-video indexing and summarization — generating searchable transcripts, second-level indexing and summaries for hours-long recordings or surveillance/video archives.
- Multimodal agents / tool chains — orchestrating tool calls that require extracting visual payloads (e.g., OCR→search→action), suitable for agent frameworks that combine perception and action.
- STEM visual reasoning & tutoring tools — diagrammatic math and stepwise solutions that incorporate images/graphs and textual explanation (noting that outputs should be verified for correctness in educational settings).
How to access Qwen3 VL-32B 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-32B API
Select the “Qwen3-VL-32B” 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.