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Documentation Index

Fetch the complete documentation index at: https://docs.gmicloud.ai/llms.txt

Use this file to discover all available pages before exploring further.

Model ID 
Qwen/Qwen3-VL-235B-A22B-Instruct-FP8
This generation delivers comprehensive upgrades across the board: superior text understanding & generation, deeper visual perception & reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities. Available in Dense and MoE architectures that scale from edge to cloud, with Instruct and reasoning‑enhanced Thinking editions for flexible, on‑demand deployment.

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Key Enhancements:

  • Visual Agent: Operates PC/mobile GUIs-recognizes elements, understands functions, invokes tools, completes tasks.
  • Visual Coding Boost: Generates Draw.io/HTML/CSS/JS from images/videos.
  • Advanced Spatial Perception: Judges object positions, viewpoints, and occlusions; provides stronger 2D grounding and enables 3D grounding for spatial reasoning and embodied AI.
  • Long Context & Video Understanding: Native 256K context, expandable to 1M; handles books and hours-long video with full recall and second-level indexing.
  • Enhanced Multimodal Reasoning: Excels in STEM/Math-causal analysis and logical, evidence-based answers.
  • Upgraded Visual Recognition: Broader, higher-quality pretraining is able to “recognize everything”-celebrities, anime, products, landmarks, flora/fauna, etc.
  • Expanded OCR: Supports 32 languages (up from 19); robust in low light, blur, and tilt; better with rare/ancient characters and jargon; improved long-document structure parsing.
  • Text Understanding on par with pure LLMs: Seamless text–vision fusion for lossless, unified comprehension.

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API Usage

You can interact with the Qwen3-VL-235B-A22B-Instruct-FP8 model through various programming languages and methods. Below are examples showing how to use the model’s API.

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API Examples

Generate a model response using the chat endpoint of Qwen3-VL-235B-A22B-Instruct-FP8.

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Shell

curl --request POST \
  --url https://api.gmi-serving.com/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer *************' \
  --data '{
    "model": "Qwen/Qwen3-VL-235B-A22B-Instruct-FP8",
    "messages": [
      {"role": "system", "content": "You are a helpful AI assistant"},
      {"role": "user", "content": "List 3 countries and their capitals."}
    ],
    "temperature": 0,
    "max_tokens": 500
  }'

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Python

import requests
import json

url = "https://api.gmi-serving.com/v1/chat/completions"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer *************"
}

payload = {
    "model": "Qwen/Qwen3-VL-235B-A22B-Instruct-FP8",
    "messages": [
        {"role": "system", "content": "You are a helpful AI assistant"},
        {"role": "user", "content": "List 3 countries and their capitals."}
    ],
    "temperature": 0,
    "max_tokens": 500
}

response = requests.post(url, headers=headers, json=payload)
print(json.dumps(response.json(), indent=2))