Qwen 3.6 - 35B Vision Language model

VL-Qwen3.6-35B-A3B is a 35-billion-parameter Multimodal Mixture-of-Experts (MoE) model with approximately 3 billion active parameters, deployed in 8-bit precision. It is designed for combined text and image understanding, and offers advanced reasoning, Agentic coding, and thinking-preservation capabilities.

The notation 35B-A3B means the model has 35 billion total parameters, but only ~3 billion are activated per token during inference. It therefore computes like a 3B model per token, keeping latency and cost low despite its large total capacity.

Optimized for Multimodal understanding, Advanced reasoning capabilities, Thinking preservation and Agentic coding workflows. It delivers performance comparable to larger dense models at a fraction of the compute cost, making it practical for cost-sensitive and small-scale deployments.

When should you use Qwen 3.6 - 35B Vision Language?

Qwen 3.6 - 35B Vision Language is best suited for:

• Multimodal tasks combining text and images, such as document understanding, chart and diagram interpretation, and image-based question answering.
• Small-scale or cost-efficient inference where a full 35B dense model would be impractical.
• Cost-sensitive deployments requiring low per-token compute.

Supported input and output formats

Input type: Text and Image

Output formats: Text

Model Key: VL-Qwen3.6-35B-A3B

Capabilities

• Natively Multimodal — meaning it is multimodal by design, instead of just an add-on layer.
• Advanced perception capability that far exceeds the relative performance of other models of similar size with only 3 billion activated parameters.
• Multimodal reasoning across modalities like documents, images, and charts with the instruction prompt combined.
• Efficient multi-step reasoning ability across complex reasoning tasks.
• Retains thinking context across conversation history.

Model details

To view the model details, go to LLM Serving, select the Models tab, and choose the Qwen 3.6 - 35B Vision Language model.

• Model Size: 35 billion (35B) total parameters, providing large capacity for multimodal understanding and generation.
• Active Parameters: Although the model holds 35 billion parameters in total, it only activates about 3 billion of them to process each word (token). This keeps inference fast and efficient without sacrificing quality.
• Precision: Deployed in 8-bit precision to reduce memory footprint and serving cost.
• Architecture: The Mixture-of-Experts design routes each token to a sparse subset of expert layers, enabling large total capacity while keeping per-token compute low.
• Input Context Window: Supports inputs up to three images (6k tokens total) plus text (3k tokens), for approximately 9k tokens overall.

In a Mixture-of-Experts model, the parameters are split into groups called “experts.” For each token, only a small number of these expert groups are selected and used. This reduces the compute per token while keeping the total model capacity large.

Model parameters

QuickML provides a robust and flexible LLM (Large Language Model) serving environment, enabling developers and businesses to fine-tune AI model behavior through a variety of customizable parameters. Unlike other platforms that restrict user control, QuickML prioritizes transparency and adaptability—allowing you to balance creativity with precision, determine the ideal response length, and guide the model to behave in ways that best serve your domain-specific needs.

The Qwen 3.6 - 35B Vision Language model exposes a set of advanced generation parameters in the LLM Serving playground. These parameters give you fine-grained control over the model’s instructions and output behavior — including randomness, token selection, and response length. Whether you’re extracting insights from documents, using a visual content analyzer, or answering Q&A about images, QuickML’s parameter configuration options ensure your application delivers responses that are not only relevant, but also contextually tailored to your multi-modal inputs.

Here’s a breakdown of the available parameters:

Access the model

The Qwen 3.6 - 35B Vision Language model can be accessed by creating an LLM Serving .

Refer to the below help documentation to understand the process of creating endpoints for LLM :

• LLM Serving — LLM Endpoints

Once the endpoint is created, API details and the relevant SDK information will be provided for integration in downstream applications.

1. API Details

The following details apply to the endpoint created for this model.

• Endpoint URL: The Model API URL used to send prompts to the model.
• OAuth Scope: The permission level required to access the model.
• Authentication: Specifies OAuth as the method for verifying user identity.
• HTTP Method: Indicates that API requests must be made using the POST method.
• Headers: Requires metadata, including organization ID and OAuth token, for authorization.
• Sample Request: A sample input JSON showing how to send a prompt to the Qwen 3.6 - 35B Vision Language model along with parameters such as top_p, temperature, and max_tokens.

{
  "prompt": "Fields to extract: Contact details, Skills, education and project details and give the results in the json format with keys as the above fields to extract and values as the original value in the document image",
  "images": [
    "<base64-encoded-image-1>",
    "<base64-encoded-image-2>"
  ]
}

• Sample Response: The output JSON format containing the model’s generated response based on the prompt.

{
  "request_id": "a9df919e72da4ef1a1a9360fdb025641",
  "model": "VL-Qwen3.6-35B-A3B",
  "response": "```json\n{\n  \"Contact\": {\n    \"Email\": \"person.one@email.com\",\n    \"Phone\": \"+91 98 7654 3210\",\n    \"Address\": \"H#1, Area - Chennai 600001\"\n  },\n  \"Skills\": [\"Windows\", \"HTML5\", \"UI/UX DESIGN\", \"Communication\", \"MS Office\", \"CSS3\", \"WordPress\", \"Teamwork\", \"Internet\", \"JAVASCRIPT\", \"Python\", \"Project Mgmt.\"],\n  \"Education\": {\n    \"Degree\": \"Bachelor of Engineering in Computer Science\",\n    \"Institute\": \"National Institute of Technology, Chennai\",\n    \"Year\": \"Aug, 2023\"\n  },\n  \"Projects\": [\n    \"Built a responsive e-commerce website for a local startup, resulting in a 15% increase in online sales.\",\n    \"Contributed bug fixes and feature enhancements to the Bootstrap CSS framework.\",\n    \"Personal portfolio website showcasing coding skills and past projects.\",\n    \"Volunteered for Hack for Education, creating an e-learning platform for underprivileged children.\"\n  ]\n}\n```",
  "metrics": {
    "input_text_token_length": 57,
    "input_image_token_length": 1750,
    "input_guided_prompt_length": 0,
    "output_text_token_length": 256,
    "queue_wait_time": 2.4174728393554688,
    "processing_time": 6.480634450912476,
    "total_time_taken": 8.898107290267944
  }
}

2. SDK Integration

In addition to calling the endpoint directly over REST, you can invoke it programmatically using the Catalyst SDKs, which handles authentication, trigger requests, and response parsing for you.

QuickML endpoints are executed through a two-step pattern: create a QuickML component instance, then call the analyzeImage() method, passing the endpoint key (the unique ID of the published endpoint, copied from the Catalyst console) and your input data as key-value pairs. The method returns the model’s output as a structured response.

The SDK is available across languages, refer to the relevant guide for setup, authentication configuration, and method syntax:

• JavaScript — Overview · Create QuickML Instance · Execute QuickML Endpoint
• Java — Execute QuickML Endpoints
• Python — Python SDK documentation

For JavaScript, install the @zcatalyst/quickml package and import the QuickML module before using the SDK methods. Executing an endpoint requires the Admin scope.