Qwen/Qwen3-VL-30B-A3B-Instruct

Qwen3-VL-30B-A3-Instruct is a large-scale, high-capacity vision-language instruction model designed for advanced multimodal reasoning. It delivers significantly stronger visual understanding, OCR accuracy, document reasoning, long-context comprehension, and agent-style interactions compared to smaller Qwen-VL variants.

Apache license 2.0 Vision 128K Tokens
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api_example.sh

curl -X POST "https://platform.qubrid.com/v1/chat/completions" \
  -H "Authorization: Bearer QUBRID_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "Qwen/Qwen3-VL-30B-A3B-Instruct",
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "What is in this image? Describe the main elements."
        },
        {
          "type": "image_url",
          "image_url": {
            "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
          }
        }
      ]
    }
  ],
  "max_tokens": 4096,
  "temperature": 0.7,
  "stream": true,
  "top_p": 0.9,
  "presence_penalty": 0
}'

Technical Specifications

Model Architecture & Performance

Variant Instruct
Model Size 30B params
Context Length 128K Tokens
Quantization fp16 / bf16
Tokens/Second 110
Architecture Transformer decoder-only with ViT-based visual encoder (Qwen3-VL)
Precision fp16 / bf16
License Apache 2.0
Release Date 2024
Developers Alibaba Cloud (QwenLM)
Layers 80
Hidden Size 7168
Attention Heads 56

Pricing

Pay-per-use, no commitments

Input Tokens $1.15/1M Tokens
Output Tokens $1.17/1M Tokens

API Reference

Complete parameter documentation

Parameter Type Default Description
stream boolean true Enable streaming responses for real-time output.
temperature number 0.7 Controls randomness in output
max_tokens number 4096 Maximum tokens to generate
top_p number 0.9 Controls nucleus sampling
top_k number 50 Limits sampling to top-k tokens
presence_penalty number 0 Discourages repeated tokens

Explore the full request and response schema in our external API documentation

Performance

Strengths & considerations

Strengths Considerations
State-of-the-art vision-language reasoning
Excellent multilingual OCR & document parsing
Very long context support
Strong instruction following & agent workflows
Streaming-friendly inference
High GPU memory requirements
Lower throughput compared to smaller models
No image generation (vision understanding only)

Use cases

Recommended applications for this model

Advanced multimodal chat
High-accuracy OCR & document understanding
Visual reasoning & VQA
Chart, diagram & layout interpretation
Long-context multimodal analysis

Enterprise
Platform Integration

Docker

Docker Support

Official Docker images for containerized deployments

Kubernetes

Kubernetes Ready

Production-grade KBS manifests and Helm charts

SDK

SDK Libraries

Official SDKs for Python, Javascript, Go, and Java

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