Using DeepSeek 67B Chat on Together AI
Implementation guide · DeepSeek · DeepSeek
Quick Start
- 1
- 2Use the Together AI SDK or REST API to call
deepseek-67b-chat— see the documentation for request format. - 3
Code Examples
pip install togetherTOGETHER_API_KEYdeepseek-67b-chatTogether uses "organization/model-name" format, e.g. "meta-llama/Llama-4-Scout-17B-16E-Instruct" or "Qwen/QwQ-32B". See the Together model catalog for the exact ID.
from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="deepseek-67b-chat",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)About Together AI
Platform for running open-source and proprietary LLMs
Together AI is a platform for running open-source and proprietary LLMs with fast serverless and dedicated endpoints at competitive inference pricing.
Pricing on Together AI
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.90 |
| Output tokens | $0.90 |
Capabilities
About DeepSeek 67B Chat
DeepSeek LLM 67B Chat is a sophisticated language model with 67 billion parameters, leveraging the LLaMA architecture with enhancements such as Grouped-Query Attention across 95 layers. Trained on a vast corpus of 2 trillion tokens in English and Chinese, it excels in tasks like text generation, question answering, and fluent conversation, demonstrating superior performance in reasoning, coding, and mathematics compared to some larger models. Despite its advanced capabilities, the model can exhibit biases from its training data, experience hallucinations, and produce repetitive outputs. Due to its size, substantial computational resources are needed for inference, although quantization methods can reduce its size with potential trade-offs in quality.