xiaohongshu
xiaohongshu
deepseek
DeepSeek AI large language model API via curl. Use this skill for chat completions, reasoning, and code generation with OpenAI-compatible endpoints.
Full skill instructions
Use the DeepSeek API via direct curl calls to access powerful AI language models for chat, reasoning, and code generation.
Official docs:
https://api-docs.deepseek.com/
Use this skill when you need to:
export DEEPSEEK_API_KEY="your-api-key"
| Type | Price |
|---|---|
| Input (cache hit) | $0.028 |
| Input (cache miss) | $0.28 |
| Output | $0.42 |
DeepSeek does not enforce strict rate limits. They will try to serve every request. During high traffic, connections are maintained with keep-alive signals.
Important: When using
$VARin a command that pipes to another command, wrap the command containing$VARinbash -c '...'. Due to a Claude Code bug, environment variables are silently cleared when pipes are used directly.bash -c 'curl -s "https://api.example.com" -H "Authorization: Bearer $API_KEY"'
All examples below assume you have DEEPSEEK_API_KEY set.
The base URL for the DeepSeek API is:
https://api.deepseek.com (recommended)https://api.deepseek.com/v1 (OpenAI-compatible)Send a simple chat message:
Write to /tmp/deepseek_request.json:
{
"model": "deepseek-chat",
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "Hello, who are you?"
}
]
}
Then run:
bash -c 'curl -s "https://api.deepseek.com/chat/completions" -X POST -H "Content-Type: application/json" -H "Authorization: Bearer ${DEEPSEEK_API_KEY}" -d @/tmp/deepseek_request.json'
Available models:
deepseek-chat: DeepSeek-V3.2 non-thinking mode (128K context, 8K max output)deepseek-reasoner: DeepSeek-V3.2 thinking mode (128K context, 64K max output)Adjust creativity/randomness with temperature:
Write to /tmp/deepseek_request.json:
{
"model": "deepseek-chat",
"messages": [
{
"role": "user",
"content": "Write a short poem about coding."
}
],
"temperature": 0.7,
"max_tokens": 200
}
Then run:
bash -c 'curl -s "https://api.deepseek.com/chat/completions" -X POST -H "Content-Type: application/json" -H "Authorization: Bearer ${DEEPSEEK_API_KEY}" -d @/tmp/deepseek_request.json' | jq -r '.choices[0].message.content'
Parameters:
temperature (0-2, default 1): Higher = more creative, lower = more deterministictop_p (0-1, default 1): Nucleus sampling thresholdmax_tokens: Maximum tokens to generateGet real-time token-by-token output:
Write to /tmp/deepseek_request.json:
{
"model": "deepseek-chat",
"messages": [
{
"role": "user",
"content": "Explain quantum computing in simple terms."
}
],
"stream": true
}
Then run:
bash -c 'curl -s "https://api.deepseek.com/chat/completions" -X POST -H "Content-Type: application/json" -H "Authorization: Bearer ${DEEPSEEK_API_KEY}" -d @/tmp/deepseek_request.json'
Streaming returns Server-Sent Events (SSE) with delta chunks, ending with data: [DONE].
Use the reasoner model for complex reasoning tasks:
Write to /tmp/deepseek_request.json:
{
"model": "deepseek-reasoner",
"messages": [
{
"role": "user",
"content": "What is 15 * 17? Show your work."
}
]
}
Then run:
bash -c 'curl -s "https://api.deepseek.com/chat/completions" -X POST -H "Content-Type: application/json" -H "Authorization: Bearer ${DEEPSEEK_API_KEY}" -d @/tmp/deepseek_request.json' | jq -r '.choices[0].message.content'
The reasoner model excels at math, logic, and multi-step problems.
Force the model to return valid JSON:
Write to /tmp/deepseek_request.json:
{
"model": "deepseek-chat",
"messages": [
{
"role": "system",
"content": "You are a JSON generator. Always respond with valid JSON."
},
{
"role": "user",
"content": "List 3 programming languages with their main use cases."
}
],
"response_format": {
"type": "json_object"
}
}
Then run:
bash -c 'curl -s "https://api.deepseek.com/chat/completions" -X POST -H "Content-Type: application/json" -H "Authorization: Bearer ${DEEPSEEK_API_KEY}" -d @/tmp/deepseek_request.json' | jq -r '.choices[0].message.content'
Continue a conversation with message history:
Write to /tmp/deepseek_request.json:
{
"model": "deepseek-chat",
"messages": [
{
"role": "user",
"content": "My name is Alice."
},
{
"role": "assistant",
"content": "Nice to meet you, Alice."
},
{
"role": "user",
"content": "What is my name?"
}
]
}
Then run:
bash -c 'curl -s "https://api.deepseek.com/chat/completions" -X POST -H "Content-Type: application/json" -H "Authorization: Bearer ${DEEPSEEK_API_KEY}" -d @/tmp/deepseek_request.json' | jq -r '.choices[0].message.content'
Use Fill-in-the-Middle for code completion (beta endpoint):
Write to /tmp/deepseek_request.json:
{
"model": "deepseek-chat",
"prompt": "def add(a, b):\n ",
"max_tokens": 20
}
Then run:
bash -c 'curl -s "https://api.deepseek.com/beta/completions" -X POST -H "Content-Type: application/json" -H "Authorization: Bearer ${DEEPSEEK_API_KEY}" -d @/tmp/deepseek_request.json' | jq -r '.choices[0].text'
FIM is useful for:
Define functions the model can call:
Write to /tmp/deepseek_request.json:
{
"model": "deepseek-chat",
"messages": [
{
"role": "user",
"content": "What is the weather in Tokyo?"
}
],
"tools": [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather for a location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city name"
}
},
"required": ["location"]
}
}
}
]
}
Then run:
bash -c 'curl -s "https://api.deepseek.com/chat/completions" -X POST -H "Content-Type: application/json" -H "Authorization: Bearer ${DEEPSEEK_API_KEY}" -d @/tmp/deepseek_request.json'
The model will return a tool_calls array when it wants to use a function.
Extract usage information from response:
Write to /tmp/deepseek_request.json:
{
"model": "deepseek-chat",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}
Then run:
bash -c 'curl -s "https://api.deepseek.com/chat/completions" -X POST -H "Content-Type: application/json" -H "Authorization: Bearer ${DEEPSEEK_API_KEY}" -d @/tmp/deepseek_request.json' | jq '.usage'
Response includes:
prompt_tokens: Input token countcompletion_tokens: Output token counttotal_tokens: Sum of bothDeepSeek is fully compatible with OpenAI SDKs. Just change the base URL:
Python:
from openai import OpenAI
client = OpenAI(api_key="your-deepseek-key", base_url="https://api.deepseek.com")
Node.js:
import OpenAI from 'openai';
const client = new OpenAI({ apiKey: 'your-deepseek-key', baseURL: 'https://api.deepseek.com' });
For complex requests with nested JSON (like function calling), use a temp file to avoid shell escaping issues:
Write to /tmp/deepseek_request.json:
{
"model": "deepseek-chat",
"messages": [{"role": "user", "content": "What is the weather in Tokyo?"}],
"tools": [{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get current weather",
"parameters": {
"type": "object",
"properties": {"location": {"type": "string"}},
"required": ["location"]
}
}
}]
}
Then run:
bash -c 'curl -s "https://api.deepseek.com/chat/completions" -X POST -H "Content-Type: application/json" -H "Authorization: Bearer ${DEEPSEEK_API_KEY}" -d @/tmp/deepseek_request.json'
deepseek-chat for general tasks, deepseek-reasoner for complex reasoningresponse_format, include JSON instructions in system messageapi.deepseek.com/beta-d @filename to avoid shell quoting issuesxiaohongshu
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