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To use DeepSeek through an API, create an API key, send authenticated requests to https://api.deepseek.com with an OpenAI-compatible client, and specify a currently supported model such as deepseek-flash or deepseek-v4-pro. DeepSeek also provides an Anthropic-compatible base URL at https://api.deepseek.com/anthropic. The practical differences from a typical chat integration are model aliases that can change, token-based pricing with cache and time-of-day rates, format-specific reasoning controls, and a stateless Responses API that requires you to resend conversation history.
This guide shows working request patterns, model selection, streaming, multi-turn state management, cost controls, caching, and recovery steps. Model names, served versions, prices, and parameter support are volatile; verify the live DeepSeek model and pricing pages before deploying.
What do I need before making a DeepSeek API call?
You need an account with API access, a secret API key, an HTTP client or SDK, and a model identifier accepted by DeepSeek at the time of the call. Keep the key on your server or in a secret manager; never place it in browser JavaScript, a mobile app, a public repository, or an article example.
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Create and store the key
- Create an API key in the DeepSeek account console and copy it once.
- Store it as an environment variable such as
DEEPSEEK_API_KEY. In production, inject the value through your deployment platform’s secret store. - Give the process only the permissions and network access it needs. Rotate the key if it appears in logs, source control, screenshots, or a client-side bundle.
Choose an API format
Use the OpenAI-compatible format for the broadest SDK support and familiar chat/completions or responses calls. Use the Anthropic-compatible format when your application already depends on Anthropic message conventions. The documented base URLs are:
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- OpenAI-compatible:
https://api.deepseek.com - Anthropic-compatible:
https://api.deepseek.com/anthropic
Check the live model list
Do not hard-code an internal served-version name from an old tutorial. Query the model-list endpoint documented by DeepSeek at deployment time, or inspect the account’s current model page, then use the returned identifier in your request. Aliases are intended to remain stable while the version behind them can be upgraded.
Which DeepSeek model identifier should I use?
As of October 25, 2026, DeepSeek’s model documentation directs API callers to deepseek-flash or deepseek-v4-pro. The pricing page maps those aliases to DeepSeek-V4.1-Flash-1017 and DeepSeek-V4-Pro-0813, respectively. The alias is the safer value to put in application code because DeepSeek can change the served version without requiring a rename.
| API model value | Served version shown on the October 2026 pricing page | Use it when |
|---|---|---|
deepseek-flash |
DeepSeek-V4.1-Flash-1017 | You want the current Flash alias and its lower published token rates. |
deepseek-v4-pro |
DeepSeek-V4-Pro-0813 | You need the current V4 Pro alias and have verified its availability for your account. |
DeepSeek’s October 17, 2026 changelog says V4.1 Flash was upgraded to the 1017 version and that callers should continue using deepseek-flash. Treat both the alias and the mapping as date-sensitive: a 404 or “model not found” response means you should refresh the live model list rather than guessing a new name.
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The Chat Completions format sends a JSON array of role/content messages to /chat/completions with a bearer token. A system message sets durable behavior for that request, while a user message contains the immediate task.
Minimal curl request
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": "Answer clearly and briefly."},
{"role": "user", "content": "Explain API rate limits in two sentences."}
],
"stream": false
}'A successful response contains an assistant message in the returned choices array. Log the request ID, model, token usage, and latency, but redact the authorization header and any sensitive prompt content.
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Python with the OpenAI SDK
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 concise technical assistant."},
{"role": "user", "content": "List three ways to protect an API key."},
],
)
print(response.choices[0].message.content)The SDK shape is OpenAI-compatible, but the base URL and model value are DeepSeek-specific. Pin and test the SDK version used by your application; compatibility does not guarantee that every newest OpenAI-only field is accepted by DeepSeek.
Node.js with the OpenAI SDK
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 concise technical assistant." },
{ role: "user", content: "Give a five-step deployment checklist." }
]
});
console.log(response.choices[0].message.content);How do I use DeepSeek’s Anthropic-compatible endpoint?
Point an Anthropic-compatible client at https://api.deepseek.com/anthropic, provide the same bearer-style API key required by DeepSeek, and use the message schema expected by the Anthropic format. This is useful when migrating an existing application whose abstractions already produce Anthropic requests.
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Keep the format consistent from request construction through response parsing. Do not copy OpenAI fields such as messages arrays, tool definitions, or reasoning controls into an Anthropic request unless the current DeepSeek compatibility guide documents them for that endpoint. Validate one real request in a staging environment, then record the exact model, status code, and response shape your parser receives.
How does the Responses API work?
The Responses API uses an OpenAI-style client configured with DeepSeek’s OpenAI base URL and a responses.create call. Unlike a stateful chat session, DeepSeek documents this endpoint as stateless: the service does not remember earlier turns for you.
Single-turn Responses request
from openai import OpenAI
import os
client = OpenAI(
api_key=os.environ["DEEPSEEK_API_KEY"],
base_url="https://api.deepseek.com",
)
response = client.responses.create(
model="deepseek-flash",
input="Summarize why idempotency matters in payment APIs."
)
print(response.output_text)Use the exact input and output properties supported by the current DeepSeek Responses guide. SDKs can expose convenience properties such as output_text, while lower-level integrations may need to walk the returned output items.
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Preserve history for multi-turn conversations
For a second turn, send the prior user and assistant content again, together with the new user request. A server-side conversation store should keep the canonical history, apply a token budget, and remove or summarize old turns before constructing the next request.
history = [
{"role": "user", "content": "What is a webhook?"},
{"role": "assistant", "content": "A webhook is an HTTP callback sent when an event occurs."},
{"role": "user", "content": "How should I authenticate one?"}
]
response = client.responses.create(
model="deepseek-flash",
input=history
)Because the endpoint is stateless, losing this history in a worker restart, queue retry, or browser refresh produces a context-free answer rather than a continuation. Persist history with an identifier and make retries reuse the same logical input.
How do I enable or tune thinking?
Thinking is enabled by default in DeepSeek’s documented mode. The control names depend on the API format, so using the wrong nesting or spelling can cause a validation error or silently leave the default in place.
| Interface | Thinking switch | Reasoning effort |
|---|---|---|
| Chat Completions | thinking |
reasoning_effort |
| Responses API | Use the Responses reasoning configuration | reasoning.effort |
Consult the current format-specific guide for accepted values. A representative Chat Completions payload is:
{
"model": "deepseek-flash",
"messages": [{"role": "user", "content": "Compare these two algorithms."}],
"thinking": {"type": "enabled"},
"reasoning_effort": "medium"
}The exact accepted switch values and effort levels can change. If the API rejects a request, remove optional reasoning fields, confirm the interface you are calling, and add back only parameters listed for that interface and model.
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How much does the DeepSeek API cost?
DeepSeek bills by tokens rather than by request count alone. Its pricing table separates input cache hits, input cache misses, and output tokens, and it publishes different peak and off-peak rates. The figures below are vendor rates checked in October 2026, not independent measurements or a promise of future pricing.
deepseek-flash component |
Peak rate | Off-peak rate | Qualification |
|---|---|---|---|
| Input, cache miss | $0.30 per 1 million tokens | $0.15 per 1 million tokens | DeepSeek service rate shown for October 2026. |
| Output | $1.20 per 1 million tokens | $0.60 per 1 million tokens | DeepSeek service rate shown for October 2026. |
| Input, cache hit | Separate rate on the live pricing table | Separate rate on the live pricing table | The available data does not establish a numeric cache-hit figure; verify it before budgeting. |
DeepSeek defines peak hours as 01:00–04:00 and 06:00–10:00 UTC, Monday through Friday, excluding Chinese public holidays. Off-peak rates are stated as half of peak rates. Convert your own workload into input and output token totals, then apply the rate for the time window and cache status; do not multiply a single blended price by total tokens.
Control spend in application code
- Set an output-token ceiling appropriate to the task.
- Trim redundant history and large tool results before each turn.
- Track input, output, cache status, model, and UTC timestamp per request.
- Use lower reasoning effort for routine extraction and reserve higher effort for tasks that benefit from it.
- Alert on sudden token or latency increases instead of waiting for the monthly invoice.
Can context caching reduce input cost?
Context caching is enabled by default according to DeepSeek’s documentation. A later request can receive a cache hit when its beginning exactly matches a persisted prefix unit. Cache matching is prefix-based and best effort, so it can lower input cost and latency without being a guarantee.
Design prompts for possible cache hits
- Put stable instructions, policy text, and fixed reference material at the beginning of the prompt.
- Append changing user data, timestamps, random IDs, and the newest conversation turn after that stable prefix.
- Avoid inserting a changing value in the middle of an otherwise reusable prefix.
- Measure actual cache-hit rates and billed tokens from usage data; never assume every repeated prompt is cached.
Because the service uses persisted prefix units, even a small edit near the start can prevent a match for the rest of the request. Treat caching as an optimization, not a correctness dependency: the request must remain affordable and valid when no cache hit occurs.
How do I stream a response?
Set streaming on the endpoint and consume the server-sent event stream until a terminal event. DeepSeek’s Responses streaming guide describes semantic events and terminal states including response.completed, response.incomplete, and response.failed.
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const stream = await client.responses.create({
model: "deepseek-flash",
input: "Explain database indexes with one example.",
stream: true
});
for await (const event of stream) {
if (event.type === "response.output_text.delta") {
process.stdout.write(event.delta);
} else if (["response.completed", "response.incomplete", "response.failed"].includes(event.type)) {
console.error(`Terminal event: ${event.type}`);
}
}Buffer partial text before rendering it in a user interface, handle a dropped connection as an incomplete generation, and retain the final event or usage record when available. Do not treat the first text chunk as proof that the request completed successfully.
How should I build reliable production calls?
Timeouts and retries
Set a finite connection and read timeout. Retry transient network failures and server-side 5xx responses with exponential backoff and jitter, but do not blindly retry every 4xx response. For requests that trigger an external side effect, include your own idempotency strategy so a retry cannot perform the action twice.
Validation and observability
- Validate that the configured model is present in the current live model list during deployment.
- Record status code, latency, model alias, token usage, cache status, and a request correlation ID.
- Redact API keys, personal data, credentials, and full proprietary prompts from logs.
- Use schema validation around structured output and reject malformed data before it reaches a database or workflow.
- Set per-user and per-service quotas so one loop cannot exhaust the account.
Prompt and data safety
Assume model output can be wrong, incomplete, or adversarially influenced by untrusted text. Delimit retrieved documents, validate tool arguments, escape generated HTML, and require human or rule-based checks for financial, legal, medical, authentication, and destructive operations. The API transports your data; it does not replace your application’s authorization model.
What do common DeepSeek API errors mean?
| Symptom | Likely cause | Fix |
|---|---|---|
| 401 or authentication error | Missing, revoked, malformed, or incorrectly injected key. | Check the environment variable in the running process, send the bearer header exactly once, and issue a new key if exposure is suspected. |
| 404 model not found | Alias changed, model is unavailable to the account, or the base URL is wrong. | Confirm the base URL, query the live model list, and replace stale configuration with a currently returned identifier. |
| 400 unknown parameter | A field belongs to another API format or is unsupported by this model. | Remove optional fields, verify whether the call is Chat Completions or Responses, then follow that format’s current parameter names. |
| Context or token-limit error | History, instructions, and requested output exceed the model’s available context. | Summarize older turns, remove duplicate documents, reduce output limits, and retry with a measured token budget. |
| Stream ends without a final answer | Network interruption or an incomplete/failed terminal event. | Keep partial output separate, inspect the terminal event, and retry safely when the operation is idempotent. |
| Unexpectedly high bill | Large repeated prompts, no cache hits, long outputs, high reasoning effort, or peak-hour traffic. | Inspect per-request usage, move stable text to a reusable prefix, cap output, and schedule batch work outside the published peak windows. |
What is a sensible implementation checklist?
- Store
DEEPSEEK_API_KEYoutside source code and verify it is unavailable to clients. - Choose the OpenAI or Anthropic-compatible base URL intentionally.
- Check the current model list and use
deepseek-flashordeepseek-v4-proonly after confirming availability. - Send one minimal non-streaming request and inspect the complete response shape.
- Add explicit timeout, retry, logging-redaction, output limits, and schema validation.
- For conversations, persist and resend history because Responses requests are stateless.
- For streaming, process semantic events and handle completed, incomplete, and failed terminal states.
- Measure cache hits and token usage before promising a cost target.
- Recheck model mappings, prices, parameter names, and peak windows whenever you upgrade your integration.
Frequently Asked Questions
Can I call DeepSeek directly from a browser application?
You can technically issue an HTTP request from a browser, but placing the API key there exposes it to every visitor. Send the request through your own authenticated backend, enforce user quotas, and return only the data the browser needs.
Do DeepSeek model aliases guarantee the same behavior forever?
No. An alias such as deepseek-flash can continue working while DeepSeek upgrades the served model behind it. Pin a tested configuration in your deployment records and run regression tests after a documented model upgrade.
How can I estimate tokens before sending a request?
Count the complete serialized input, including system instructions, conversation history, and tool results, using a tokenizer compatible with the selected model when available. Treat estimates as approximate and rely on the response usage fields for billing reconciliation.
What should I do if a request contains confidential customer data?
Minimize and redact data before transmission, obtain the permissions required by your privacy policy, and avoid logging raw prompts or outputs. Add retention, access-control, and deletion procedures around the service rather than assuming an SDK provides them.
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