H

hunyuan-turbos-20250515

Entrada:$0.08912/M
Saída:$0.2228/M
Uso comercial

Technical Specifications of hunyuan-turbos-20250515

AttributeDetails
Model IDhunyuan-turbos-20250515
Provider / model familyTencent Hunyuan TurboS
Model typeLarge language model (LLM)
ArchitectureHybrid Transformer-Mamba Mixture-of-Experts (MoE)
Parameter scale560B total parameters, 56B activated parameters
Context windowUp to 256K tokens
Training dataPre-trained on 16T high-quality tokens
Reasoning styleAdaptive long-short chain-of-thought, designed to switch between fast responses and deeper reasoning depending on task complexity
Notable training / alignment methodsSupervised fine-tuning, adaptive long-short CoT fusion, multi-round deliberation learning, and large-scale reinforcement learning
Strength areasLong-context handling, multilingual capability, reasoning, coding, instruction following, and efficient inference
Publicly reported benchmark notesTencent reports a 1356 LMSYS Chatbot Arena score for Hunyuan-TurboS-20250416 and an average 77.9% across 23 automated benchmarks

What is hunyuan-turbos-20250515?

hunyuan-turbos-20250515 is CometAPI’s platform identifier for Tencent’s Hunyuan TurboS model line, a high-performance foundation LLM built for both speed and strong reasoning. Public technical materials describe Hunyuan-TurboS as a large hybrid Transformer-Mamba MoE model that combines the long-sequence efficiency of Mamba with the contextual understanding of Transformer attention.

Tencent’s published description emphasizes that TurboS is designed to respond quickly on simpler prompts while allocating deeper reasoning effort to more complex tasks through an adaptive long-short chain-of-thought mechanism. This makes the model relevant for interactive chat, enterprise assistants, coding help, knowledge tasks, and long-context workflows where latency and quality both matter.

Based on Tencent’s official GitHub repository and the corresponding arXiv paper, Hunyuan-TurboS uses 128 layers, supports a 256K context window, and was pre-trained on 16 trillion tokens. Tencent also positions it as an industry-deployed large-scale Mamba-based model and reports strong multilingual and benchmark performance for the TurboS family.

Main features of hunyuan-turbos-20250515

  • Hybrid Transformer-Mamba design: Hunyuan-TurboS combines Transformer attention with Mamba sequence modeling to balance contextual understanding with efficient long-sequence processing.
  • Mixture-of-Experts efficiency: The model is reported as 560B total parameters with 56B activated, which helps deliver strong capability while keeping active compute lower than a dense model of the same total size.
  • Adaptive reasoning behavior: Tencent describes an adaptive long-short chain-of-thought mechanism that shifts between fast answers for simple requests and deeper reasoning for harder ones.
  • Long-context support: With support for up to 256K tokens, the TurboS family is suited for large documents, long chats, repository-scale code context, and retrieval-heavy enterprise use cases.
  • Large-scale training and post-training: Public technical details mention 16T pretraining tokens plus supervised fine-tuning, deliberation learning, and reinforcement learning for stronger instruction following and reasoning.
  • Multilingual performance: Tencent reports especially strong multilingual Arena performance, including leading placements in Chinese, French, and Spanish for the evaluated TurboS release.
  • Strong benchmark positioning: Tencent reports top-tier Chatbot Arena placement and solid average performance across 23 automated benchmarks, positioning TurboS as a competitive general-purpose model family.
  • Built for low-latency interactive use: External coverage of the launch highlighted TurboS as being optimized for immediate responses, reinforcing its role as a speed-oriented base model for conversational and production deployments.

How to access and integrate hunyuan-turbos-20250515

Step 1: Sign Up for API Key

First, sign up for an API key on the CometAPI platform. After registration, create and copy your API key from the dashboard. You will use this key to authenticate requests to the hunyuan-turbos-20250515 API.

Step 2: Send Requests to hunyuan-turbos-20250515 API

Use the standard OpenAI-compatible chat completions interface provided by CometAPI, and set model to hunyuan-turbos-20250515.

curl https://api.cometapi.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_COMETAPI_KEY" \
  -d '{
    "model": "hunyuan-turbos-20250515",
    "messages": [
      {
        "role": "user",
        "content": "Explain the main advantages of hybrid Transformer-Mamba language models."
      }
    ]
  }'

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-turbos-20250515",
    messages=[
        {"role": "user", "content": "Explain the main advantages of hybrid Transformer-Mamba language models."}
    ]
)

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

Step 3: Retrieve and Verify Results

After receiving the response, parse the generated content from the API result and validate it for your application’s needs. For production use, you should verify factual outputs, test prompt behavior on your own workloads, and monitor latency, token usage, and output quality when integrating hunyuan-turbos-20250515 into your system.