deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
DeepSeekPricing
- Input Tokens: $0.01 /M tokens
- Output Tokens: $0.01 /M tokens
Input Modalities
Try this model
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["AIHUBMIX_API_KEY"],
base_url="https://shkq.org/v1",
)
response = client.chat.completions.create(
model="deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
messages=[
{
"role": "user",
"content": "Hello, how are you?"
}
],
max_tokens=1024,
stream=False,
)
print(response.choices[0].message.content)Frequently asked questions
What is deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B?
How much does deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B cost?
How do I call deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B via API?
Who created deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B?
More models from DeepSeek
See all DeepSeek models →DeepSeek-V3.2-Exp
DeepSeek-V3.2-Exp
DeepSeek-V3.2-Exp
The model DeepSeek-V3.2-Exp is officially named deepseek-chat on the website. It is an experimental version. As an intermediate step towards the next-generation architecture, V3.2-Exp introduces DeepSeek Sparse Attention (a sparse attention mechanism) based on V3.1-Terminus, exploring and validating
DeepSeek V3.2 is DeepSeek's text large language model, designed for reasoning tasks and agentic tool use. It harmonizes high computational efficiency with strong reasoning performance through DeepSeek Sparse Attention (DSA), while providing native support for tools, function calling, and structured outputs.
The model DeepSeek-V3.2-Exp is officially named deepseek-chat on the website. It is an experimental version. As an intermediate step towards the next-generation architecture, V3.2-Exp introduces DeepSeek Sparse Attention (a sparse attention mechanism) based on V3.1-Terminus, exploring and validating
© 2023 - 2026 AIHubMix, LLC