Technical Specifications of `hunyuan-t1-20250321`

Specification	Details
Model ID	`hunyuan-t1-20250321`
Provider	Tencent Hunyuan
Model type	Reasoning large language model focused on deep inference and complex problem solving
Release date	March 21, 2025
Family	Hunyuan-T1 series
Architecture basis	Built on Tencent’s TurboS fast-thinking base and described by Tencent as using a Hybrid-Transformer-Mamba MoE foundation
Strength areas	Mathematical reasoning, logical reasoning, science, code, and long-text understanding
API availability	Available through Tencent Hunyuan cloud APIs and ChatCompletions-style access paths in Tencent’s ecosystem
Streaming support	Supports streaming and non-streaming invocation in Tencent Hunyuan APIs, with SSE used for streaming responses
Pricing reference	Tencent listed Hunyuan-T1 token pricing at input 1 RMB / million tokens and output 4 RMB / million tokens in its pricing overview

What is `hunyuan-t1-20250321`?

hunyuan-t1-20250321 is CometAPI’s platform identifier for Tencent’s Hunyuan-T1 reasoning model release from March 21, 2025. Tencent introduced Hunyuan-T1 as its first deep reasoning model, positioning it for harder inference-heavy tasks rather than simple chat-only generation.

Based on Tencent’s own materials, Hunyuan-T1 was upgraded from the earlier T1-Preview and built on the TurboS base model, with post-training intended to significantly expand reasoning ability and align outputs better with human preferences. Tencent also highlights strong performance in long-context information capture and in solving mathematics, logic, science, and code problems.

In practical terms, this means hunyuan-t1-20250321 is best suited for applications that need deliberate multi-step thinking, structured analysis, and better handling of complex prompts, especially where Chinese-language capability and technical reasoning matter. This last point is an inference based on Tencent’s product positioning and API documentation.

Main features of `hunyuan-t1-20250321`

Deep reasoning focus: Tencent presents Hunyuan-T1 as a dedicated reasoning model designed for more difficult inference tasks, not just general conversational generation.
Strong STEM and logic performance: The model is explicitly described as supporting mathematics, logical reasoning, scientific problem solving, and code-related questions across varying difficulty levels.
Long-text understanding: Tencent highlights strong long-text information capture, making it more suitable for lengthy documents, multi-part instructions, and extended analytical prompts.
Hybrid foundation architecture: Tencent states the official Hunyuan-T1 version is based on the TurboS fast-thinking base and references a Hybrid-Transformer-Mamba MoE large-model foundation.
Improved post-training alignment: Tencent says the official version expands reasoning through large-scale post-training and further aligns model behavior with human preferences.
API-friendly deployment: Tencent Hunyuan APIs support HTTPS-based access, regional endpoints, and streaming or non-streaming calls, which makes integration practical for production workloads.
Ecosystem fit: Tencent materials show Hunyuan models are exposed through ChatCompletions-style workflows and broader Tencent Cloud tooling, which helps teams adopt the model in existing AI application pipelines.

How to access and integrate `hunyuan-t1-20250321`

To get started, sign up on CometAPI and generate your API key from the dashboard. Once you have the key, store it securely and use it in the Authorization header for all requests.

Step 2: Send Requests to `hunyuan-t1-20250321` API

Use the OpenAI-compatible Chat Completions interface and set the model field to hunyuan-t1-20250321.

curl https://api.cometapi.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $COMETAPI_API_KEY" \
  -d '{
    "model": "hunyuan-t1-20250321",
    "messages": [
      {
        "role": "user",
        "content": "Explain how a reasoning model helps with multi-step math and coding tasks."
      }
    ],
    "temperature": 0.6
  }'

from openai import OpenAI

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

response = client.chat.completions.create(
    model="hunyuan-t1-20250321",
    messages=[
        {"role": "user", "content": "Explain how a reasoning model helps with multi-step math and coding tasks."}
    ],
    temperature=0.6
)

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

Step 3: Retrieve and Verify Results