hunyuan

Technical Specifications of hunyuan

What is hunyuan?

hunyuan is CometAPI’s model identifier for Tencent Hunyuan, Tencent’s self-developed general-purpose and multimodal large model family. Official Tencent Cloud materials describe Hunyuan as covering text, image, 3D, and other modalities, and positioning it for enterprise-grade scenarios such as content production and business automation.

Tencent’s documentation also highlights strong performance in high-quality content creation, mathematical logic, code generation, and multi-turn dialogue. In addition, the Hunyuan API supports AI search with online plug-ins that connect to Tencent’s content ecosystem for real-time retrieval and question answering.

In practice, this means hunyuan is best understood as a broad multimodal foundation-model endpoint family rather than a narrowly specialized single-purpose model. Based on Tencent’s current public product pages, the Hunyuan ecosystem spans text APIs, image generation, video generation experiences, and 3D model generation services.

Main features of hunyuan

• General-purpose language intelligence: Hunyuan is designed for broad text and reasoning workloads, including content generation, dialogue, math-oriented tasks, and code generation.
• Multimodal model family: Tencent positions Hunyuan as a multimodal family covering text, image, 3D, and related visual-generation capabilities.
• Enterprise-grade service orientation: Official product pages emphasize business automation and production use cases, indicating a platform aimed at commercial and operational deployments rather than only research demos.
• AI search and retrieval augmentation: Tencent states that the Hunyuan API supports online plug-ins for real-time, in-depth content retrieval and AI FAQ workflows.
• Image generation and editing ecosystem: Public Hunyuan image pages show support for instruction-following image generation and image-editing style workflows, suggesting strong visual creation support around the model family.
• Video generation branch: Tencent’s HunyuanVideo materials describe bilingual instruction understanding and text-to-video/image-to-video generation, showing that video is part of the broader Hunyuan capability stack.
• 3D content generation: Tencent offers Hunyuan 3D services that can generate 3D assets from multimodal inputs such as text, images, or sketches, reducing asset-production time significantly.
• Developer and API availability: Tencent Cloud documentation exposes Hunyuan through documented API categories and text API references, which indicates a structured developer integration path.

How to access and integrate hunyuan

Step 1: Sign Up for API Key

To get started, sign up on CometAPI and create an API key from your dashboard. Once you have your API key, store it securely and use it to authenticate requests to the hunyuan model. CometAPI provides an OpenAI-compatible API format, so you can usually integrate it with minimal code changes in existing projects.

Step 2: Send Requests to hunyuan API

After getting your API key, send chat completion requests to the CometAPI endpoint using hunyuan as the model name.

curl https://api.cometapi.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $COMETAPI_API_KEY" \
  -d '{
    "model": "hunyuan",
    "messages": [
      {
        "role": "user",
        "content": "Write a short introduction to multimodal AI."
      }
    ]
  }'

You can also use the OpenAI SDK configuration style by pointing your client to CometAPI’s base URL and setting the model to hunyuan.

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_COMETAPI_KEY",
    base_url="https://api.cometapi.com/v1"
)

response = client.chat.completions.create(
    model="hunyuan",
    messages=[
        {"role": "user", "content": "Write a short introduction to multimodal AI."}
    ]
)

print(response.choices[0].message.content)

Step 3: Retrieve and Verify Results

Parse the JSON response and extract the generated output from the first choice. As with any LLM integration, you should verify factual answers, test prompts for your use case, and add application-level safeguards such as retries, logging, moderation, and schema validation where needed.