Technical Specifications of `hunyuan-turbos-20250416`

Item	Details
Model ID	`hunyuan-turbos-20250416`
Provider / family	Tencent Hunyuan TurboS github.com
Model type	Large language model for text generation, reasoning, and instruction following github.com
Architecture	Hybrid Transformer-Mamba Mixture-of-Experts (MoE) model github.com
Parameters	560B total parameters, 56B activated parameters github.com
Layers	128 layers github.com
Context window	Up to 256K tokens github.com
Training data	Pre-trained on 16T high-quality tokens github.com
Post-training	Supervised fine-tuning on 3M instructions, adaptive long-short CoT fusion, multi-round deliberation learning, and two-stage reinforcement learning github.com
Notable benchmark results	LMSYS Chatbot Arena score 1356; reported among the top 7 of 239 models; top 1 in Chinese, French, and Spanish, and top 2 in Korean in the reported results github.com
Efficiency positioning	Designed to balance strong reasoning with lower inference cost; Tencent’s Turbo line was introduced with a focus on improved training efficiency and reduced inference cost github.com

What is `hunyuan-turbos-20250416`?

hunyuan-turbos-20250416 is CometAPI’s platform identifier for Tencent’s Hunyuan-TurboS-20250416 model, a high-performance large language model built for fast responses, strong reasoning, multilingual capability, and long-context tasks. Tencent describes Hunyuan as a family of large models with broad-domain knowledge and natural language understanding, while the TurboS variant specifically emphasizes a hybrid Mamba-Transformer design and adaptive reasoning behavior. hunyuan.tencent.com

Based on Tencent’s published technical materials, this model is intended to handle both lightweight everyday prompts and more difficult problems by dynamically shifting between short and deeper reasoning modes. That makes it suitable for chat, content generation, coding assistance, summarization, analysis of long documents, and complex instruction-following workflows. github.com

Main features of `hunyuan-turbos-20250416`

Hybrid Mamba-Transformer architecture: Combines Mamba’s long-sequence efficiency with Transformer contextual modeling, aiming to improve both scalability and language understanding. github.com
Adaptive reasoning modes: Uses an adaptive long-short chain-of-thought approach so the model can respond quickly on simple tasks and allocate deeper reasoning on harder ones. github.com
Very long context support: Supports up to 256K context length, which is useful for long documents, multi-file workflows, and extended multi-turn conversations. github.com
Large-scale MoE efficiency: With 560B total parameters and 56B activated parameters, the model is designed to deliver strong quality while managing inference efficiency through sparse activation. github.com
Strong benchmark positioning: Tencent reports a 1356 LMSYS Chatbot Arena score for Hunyuan-TurboS-20250416, placing it among the top models in the published comparison set. github.com
Multilingual strength: Tencent reports top-ranked or near-top-ranked results in multiple non-English languages, including Chinese, French, Spanish, and Korean. github.com
Large-scale training and alignment: The model was pre-trained on 16T tokens and further improved with supervised fine-tuning, deliberation learning, and reinforcement learning for reasoning and instruction following. github.com
Cost/performance focus: The broader Hunyuan Turbo line was launched with an emphasis on better training efficiency and lower inference cost, and TurboS is presented as continuing that efficiency-oriented direction. tencentcloud.com

How to access and integrate `hunyuan-turbos-20250416`

To get started, sign up on CometAPI and generate your API key from the dashboard. After you have an API key, store it securely and use it to authenticate every request you send to the API.

Step 2: Send Requests to `hunyuan-turbos-20250416` API

Once you have your API key, you can call the OpenAI-compatible CometAPI endpoint and set the model field to hunyuan-turbos-20250416.

curl https://api.cometapi.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $COMETAPI_API_KEY" \
  -d '{
    "model": "hunyuan-turbos-20250416",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Explain the main strengths of this model."}
    ]
  }'

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-turbos-20250416",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain the main strengths of this model."}
    ]
)

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

Step 3: Retrieve and Verify Results