How to set up the DeepSeek API and make your first model request

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Your first DeepSeek API call requires four things: a DeepSeek platform account, an API key exposed to your application as DEEPSEEK_API_KEY, an OpenAI-compatible client or an HTTP tool, and a request containing a model ID plus a messages array. Use https://api.deepseek.com as the base URL, then call /chat/completions with a Bearer token. The Python SDK and a raw curl request are both shown below, along with streaming, reasoning, JSON output, response handling, and the failure checks that matter when you move beyond a one-line test.

What do I need before making a DeepSeek API request?

You need a DeepSeek account, an API key issued by the DeepSeek platform, and a runtime that can make HTTPS requests. The API follows a chat-completions format compatible with OpenAI and Anthropic-style clients, so you can use the OpenAI Python or Node.js package by changing the base URL and supplying your DeepSeek key.

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• Account and key: create an account on the DeepSeek platform and apply for an API key.
• Secret handling: make the key available to the running process as DEEPSEEK_API_KEY; do not place it in source control.
• Endpoint: use the OpenAI-compatible base URL https://api.deepseek.com and append /chat/completions.
• Request body: include a documented model ID and a messages array.
• Client option: install the openai package for Python or Node.js, or send the same HTTP request with curl.

Nothing in the first request requires a separate DeepSeek-specific SDK. The compatibility layer is the reason an existing OpenAI client can be pointed at DeepSeek by setting its base URL.

How do I get a DeepSeek API key and keep it safe?

Sign in to the DeepSeek platform, create or access your account, and apply for an API key from the platform’s API credentials area. Copy the key once it is issued and provide it to your process through an environment variable rather than embedding it in Python, JavaScript, a shell script committed to a repository, or a browser bundle.

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Set the variable for a Unix-like shell

export DEEPSEEK_API_KEY='your_key_here'

Start the Python or Node.js process from that shell so it inherits the variable. The examples in this guide read the value at runtime; if the variable is missing, the client cannot authenticate.

Set it for a single command

DEEPSEEK_API_KEY='your_key_here' python3 app.py

A one-command assignment limits the key’s lifetime to that process invocation. Use your operating system’s equivalent secret-management facility for longer-lived services, and grant access only to the process that needs to call the API.

What should never happen to the key?

• Do not commit it to Git or paste it into an issue, log, screenshot, or client-side JavaScript bundle.
• Do not print the value while debugging; log whether the variable is present, not its contents.
• Do not send it to a browser. A server-side process should make the authenticated request and return only the application data the browser needs.

If a key is exposed, stop using it and follow the platform’s credential-management process to replace it before continuing development.

What URL, method, headers, and fields does DeepSeek use?

The API uses an HTTPS POST request to https://api.deepseek.com/chat/completions. Authenticate with an HTTP Bearer token and identify the payload as JSON.

The messages value is an array of objects. Each object has a role such as system or user and a text content value. A minimal request can contain one system instruction and one user question.

“The DeepSeek API uses an API format compatible with OpenAI/Anthropic.” — DeepSeek API quick-start guide

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How do I make the first request in Python?

Install the OpenAI Python package, set DEEPSEEK_API_KEY, create an OpenAI client with DeepSeek’s base URL, and call chat.completions.create. This is the smallest complete non-streaming example:

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["DEEPSEEK_API_KEY"],
    base_url="https://api.deepseek.com",
)

response = client.chat.completions.create(
    model="deepseek-flash",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"},
    ],
)
print(response.choices[0].message.content)

Install the dependency

pip3 install openai

Run the script from the environment in which the variable is set. The client sends the key as a Bearer token and serializes the model and messages into the expected JSON request. A successful non-streaming response places generated text in response.choices[0].message.content.

Check the result instead of printing blindly

Before using the text in an application, inspect the response’s first choice and its finish_reason. A value of stop indicates a natural completion; length indicates that the token limit was reached. The response can also include a usage object containing prompt, completion, cached, reasoning, and total token counts.

choice = response.choices[0]
text = choice.message.content
reason = choice.finish_reason
usage = response.usage

print(text)
print("finish_reason:", reason)
print("usage:", usage)

Keep the finish_reason check close to the code that consumes the answer. If the reason is length, treat the output as incomplete rather than silently storing it as a finished response.

How do I make the same call with curl?

curl removes SDK dependencies and makes the exact HTTP request visible. The following command sends a non-streaming completion with the same model and messages as the Python example:

curl https://api.deepseek.com/chat/completions 
  -H 'Content-Type: application/json' 
  -H 'Authorization: Bearer ${DEEPSEEK_API_KEY}' 
  -d '{
    "model": "deepseek-flash",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Hello!"}
    ],
    "stream": false
  }'

The shell expands ${DEEPSEEK_API_KEY} before sending the header. Keep the outer JSON single-quoted in a Unix-like shell so the embedded JSON double quotes are passed unchanged. The service returns a JSON response; for a non-streaming call, inspect the first choice’s message content and finish reason as you would in the SDK.

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Should I use the Python SDK, Node.js SDK, or raw HTTP?

All three approaches address the same endpoint and payload. Choose based on where your code runs and how much request and response handling you want the library to provide.

The protocol does not change when you switch approaches: point at https://api.deepseek.com, send the Bearer key, and provide model plus messages. A client library changes ergonomics, not the required API concepts.

How do I call DeepSeek from Node.js?

Install the same OpenAI package for Node.js and construct the client with the DeepSeek base URL. Keep this code on a server or other trusted process so the key is not exposed to a browser.

npm install openai

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.DEEPSEEK_API_KEY,
  baseURL: 'https://api.deepseek.com'
});

const response = await client.chat.completions.create({
  model: 'deepseek-flash',
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'Hello!' }
  ]
});

console.log(response.choices[0].message.content);

The JavaScript response has the same logical fields: read the first choice’s message content, examine finish_reason, and record usage when you need token accounting. The exact module-loading syntax can vary with your Node.js project configuration, but the client options and request fields remain the same.

Which DeepSeek model ID should I put in the request?

The current API reference lists deepseek-flash and deepseek-v4-pro as accepted model IDs. The quick-start guide says that deepseek-flash is served by DeepSeek-V4.1-Flash-1017 while the calling method remains unchanged.

Do not hard-code assumptions about speed, context size, or reasoning quality from an alias name. Model serving is volatile, so verify the live model documentation when pinning a production configuration. Changing the model normally means changing the model string while leaving the endpoint and message format intact.

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How do thinking and reasoning controls work?

The request can include a thinking object whose type is enabled or disabled. It can also include reasoning_effort with one of none, low, high, or max; the documented default effort is high.

response = client.chat.completions.create(
    model="deepseek-flash",
    messages=[
        {"role": "user", "content": "Explain this algorithm."}
    ],
    thinking={"type": "enabled"},
    reasoning_effort="high",
)

Use these controls deliberately. An explicit setting makes your intent visible in configuration, while omitting them uses the service’s documented defaults. If you change reasoning settings, measure the resulting latency, output length, and quality for your own workload instead of assuming that a higher setting is always better.

How do I stream a response?

Set stream to true to request incremental output. The service returns server-sent event chunks and ends the stream with data: [DONE].

curl https://api.deepseek.com/chat/completions 
  -H 'Content-Type: application/json' 
  -H 'Authorization: Bearer ${DEEPSEEK_API_KEY}' 
  -d '{
    "model": "deepseek-flash",
    "messages": [{"role": "user", "content": "Write a short greeting."}],
    "stream": true
  }'

Streaming changes delivery, not the authentication or required message structure. Your client must keep the connection open, process each server-sent event in order, append the text fragments it receives, and stop when it sees data: [DONE]. Treat a disconnected stream as potentially incomplete and retain enough state to retry or report that condition.

How do I request JSON output?

Pass response_format: {"type": "json_object"} in the request and also instruct the model in the prompt to produce JSON. The parameter alone does not replace a clear instruction in the messages.

response = client.chat.completions.create(
    model="deepseek-flash",
    messages=[
        {
            "role": "system",
            "content": "Return a JSON object with keys answer and confidence."
        },
        {"role": "user", "content": "Summarize this text."}
    ],
    response_format={"type": "json_object"},
)

Parse the returned message content as JSON in your application and handle parse failures as errors. Keep the prompt’s requested shape unambiguous; a JSON mode request is still a model-generated response, not a guarantee that your application can skip validation.

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What should I inspect when a request fails or looks incomplete?

Debug the request in layers so you can distinguish credentials, transport, payload, and generation problems.

1. Verify the process environment. Confirm that DEEPSEEK_API_KEY exists without printing its value. A missing variable produces a client-side authentication setup failure before a useful completion can be returned.
2. Verify the URL. The base URL must be https://api.deepseek.com; the chat-completions path is /chat/completions. An accidental duplicate path or a different host changes the request destination.
3. Verify headers. Send Content-Type: application/json and Authorization: Bearer .... In curl, quote the JSON and check that the shell expanded the variable rather than sending the literal characters.
4. Verify the body. Include a currently accepted model ID and a non-empty messages array with roles and content. If you changed model aliases, confirm them against the current API reference.
5. Read the complete response. For SDK calls, inspect choices[0].message.content, finish_reason, and usage. For raw HTTP, preserve the status and response body in a secure diagnostic log while redacting the key.
6. Handle truncation. A finish_reason of length means the token limit was reached. Do not treat that text as a natural, complete answer; shorten the request, adjust the applicable limit, or continue deliberately.
7. Handle streams separately. A stream must be assembled from its server-sent event chunks and should end at data: [DONE]. A connection that ends first needs retry or partial-result handling.

These checks are intentionally independent: a valid key cannot compensate for a wrong endpoint, and a valid HTTP response can still contain an incomplete generation.

What does a production-ready first integration look like?

After the smoke test prints a completion, turn the example into a controlled service boundary rather than copying it into user-facing code unchanged.

• Configuration: keep the base URL, model ID, reasoning settings, and streaming choice in configuration so a model alias can be changed without editing business logic.
• Secret isolation: inject DEEPSEEK_API_KEY at deployment time and keep it out of repositories, browser code, and application logs.
• Response validation: check that a choice and message content exist, branch on finish_reason, and validate JSON before using it as structured data.
• Usage accounting: record the returned usage fields when you need prompt, completion, cached, reasoning, or total token counts.
• Streaming policy: decide whether users can see partial output and define what happens when a stream disconnects before data: [DONE].
• Model maintenance: verify aliases, serving versions, availability, capability, latency, and context/token limits before production changes.
• Request safety: avoid logging secrets and review prompts before sending confidential data to an external API.

Tool calls are an optional request feature, not a prerequisite for the first completion. Start with a plain message exchange, add one control at a time, and keep the raw request shape available for troubleshooting.

What is the shortest end-to-end smoke test?

For a quick verification, set the key, install the client, run the Python snippet, and confirm that the response contains a choice with message content. If you prefer not to install anything, run the curl command and check that the service returns JSON rather than a shell or authentication error.

1. Set DEEPSEEK_API_KEY in the same environment that will run the test.
2. Install the Python package with pip3 install openai, or prepare the curl command.
3. Use base URL https://api.deepseek.com, model deepseek-flash, and a two-message conversation.
4. Read choices[0].message.content and inspect finish_reason.
5. Only after the basic call works, enable streaming, reasoning, JSON output, or tool calls as separate configuration changes.

Frequently Asked Questions

Can I change the DeepSeek model without rewriting my integration?

Usually, the endpoint, Bearer authentication, and messages structure stay the same; you change the model value. Because aliases and serving versions can change, verify the replacement ID and its current limits before deploying the change.

Where can I see how many tokens a DeepSeek call used?

Inspect the response’s usage object. Depending on the response, it can contain prompt, completion, cached, reasoning, and total token counts, which you can record alongside your application request ID.

What does a DeepSeek stream look like on the wire?

A streaming request returns server-sent event chunks rather than one complete JSON result. Append the incremental content in order and treat data: [DONE] as the terminator; a disconnect before that marker may leave a partial answer.

Why did my JSON-mode response fail to parse?

JSON mode is requested with response_format, but the prompt must also tell the model to produce a JSON object. Your application should still parse and validate the returned content and handle malformed or incomplete output explicitly.

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