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To use the DeepSeek API, create an API key, send it as an HTTP Bearer credential, and call the OpenAI-compatible endpoint at https://api.deepseek.com. Start new integrations with the model ID deepseek-flash, which currently routes to DeepSeek-V4.1-Flash-1017. Choose between Flash and deepseek-v4-pro by comparing the feature you need, token volume, cache status, and whether your request falls in DeepSeek’s peak pricing window. The API supports OpenAI-compatible Chat Completions, an Anthropic-compatible endpoint, and a stateless Responses API. This guide shows the first authenticated request, model differences, token-cost calculations, Search and Fetch charges, and the checks that prevent common deployment mistakes.
What is the DeepSeek API and what can you call today?
The DeepSeek API is a hosted HTTP interface for sending prompts and receiving model output. DeepSeek documents OpenAI-compatible and Anthropic-compatible request formats, so an application that already uses one of those conventions can usually adapt its client configuration rather than replace its entire request layer. The current model and pricing table is published in the DeepSeek Models & Pricing documentation; model routing and prices can change, so check that page before deployment.
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deepseek-flashis served as DeepSeek-V4.1-Flash-1017. The October 17, 2026 changelog says users should continue sendingdeepseek-flashto receive that upgraded service.deepseek-v4-prois served as DeepSeek-V4-Pro-0813.
Earlier V4 Flash and V4 Flash Vision Exp names are legacy aliases. DeepSeek says they remain temporarily accepted and route to the V4.1 Flash service, but they are not the right starting point for new code. Use the current ID so a later alias retirement does not become an avoidable migration.
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Both listed models support thinking and non-thinking modes, with thinking enabled by default; DeepSeek points to a separate mode guide for changing that behavior. The vendor table lists a 1 million-token context length and a maximum output of 384,000 tokens for each model. It marks vision support for Flash and not for Pro. Those are published specifications, not independent quality or speed benchmarks.
Which DeepSeek model should I call?
There is no documented universal winner. Select the model from the capability, context, and cost requirements of your workload, then measure your own prompts and outputs. The current published comparison is:
| Model ID | Served version | Context length | Maximum output | Vision | Thinking modes |
|---|---|---|---|---|---|
deepseek-flash |
DeepSeek-V4.1-Flash-1017 | 1M tokens | 384K tokens | Supported | Thinking and non-thinking; thinking enabled by default |
deepseek-v4-pro |
DeepSeek-V4-Pro-0813 | 1M tokens | 384K tokens | Not supported | Thinking and non-thinking; thinking enabled by default |
Use Flash when its documented vision support or lower published token rates fit the job. Use Pro when your application specifically needs the Pro service and its higher rates are acceptable. The documentation does not establish comparative accuracy, latency, or reliability, so do not present either choice as objectively better without your own evaluation.
What does the Flash model name mean?
deepseek-flash is an endpoint model identifier, not a permanent version string. DeepSeek upgraded the service to V4.1-Flash-1017 on October 17, 2026 while keeping that identifier. Your application should therefore log the model ID it sent and periodically review the changelog at https://api-docs.deepseek.com/updates/ for routing changes.
Do the context and output limits mean the same thing?
No. A 1M-token context length is the maximum amount of input and conversation context the service lists for the model. The 384K maximum output is a separate ceiling. A request must still fit within the service’s combined context rules, and your application should set a practical output limit for predictable bills and response sizes.
How do I create a key and authenticate?
Create an API key in your DeepSeek account before making a request. Every API call must carry that key as an HTTP Bearer credential, as described in the DeepSeek API specification. Treat the key like a password: keep it out of source control, browser code, screenshots, and error logs.
- Create the key in the DeepSeek account interface and copy it once into your secret store.
- Expose it to the process as an environment variable such as
DEEPSEEK_API_KEY. Do not hard-code the value in a repository that other people or build systems can read. - Send the key in the HTTP
Authorizationheader using the formBearer YOUR_KEY. SDKs that accept anapi_keyoption generate this header for you. - Use the documented base URL for the request format you selected, and record the model ID separately from the secret.
If a request returns an authentication error, first confirm that the environment variable is present in the same process that runs the client, that no extra spaces or quotation marks were copied into the value, and that the key has not been revoked. Never solve an authentication problem by printing the key in a production log.
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How do I make a first OpenAI-compatible request?
The quick-start example uses the OpenAI Python SDK with DeepSeek’s OpenAI-compatible base URL. Install a current version of the SDK in your project, set DEEPSEEK_API_KEY, and run a request like this:
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ.get('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)The important settings are the key, the exact base URL, the current model ID, and a valid message list. The call uses the Chat Completions surface documented in DeepSeek’s first-call guide. The response object shown by the SDK contains the generated text at response.choices[0].message.content.
How can I test the Bearer header without an SDK?
A direct HTTP request is useful for separating an SDK configuration problem from an API or key problem. Keep the key in an environment variable and let the shell expand it:
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": "Hello"}
]
}'Do not paste a real key into a shared terminal transcript. If this request succeeds but the SDK call fails, compare the SDK’s base URL, model value, and environment handling with the working HTTP request.
Should I use the OpenAI format, Anthropic format, or Responses API?
Choose the format that matches the client library and orchestration system you already operate. DeepSeek documents the OpenAI-compatible base URL as https://api.deepseek.com and the Anthropic-compatible base URL as https://api.deepseek.com/anthropic. The same authentication principle applies: send the API key as a Bearer credential.
| Option | Endpoint | Best fit | Conversation consideration |
|---|---|---|---|
| OpenAI-compatible Chat Completions | https://api.deepseek.com |
Applications or SDKs built around OpenAI-style chat requests | Send the message structure expected by Chat Completions |
| Anthropic-compatible API | https://api.deepseek.com/anthropic |
Applications already using Anthropic-format clients | Use the request and response shape documented for that compatibility layer |
| Responses API | https://api.deepseek.com |
Clients using DeepSeek’s documented Responses surface | Stateless; include the full conversation history for a multi-turn exchange |
The Responses API documentation at https://api-docs.deepseek.com/api/create-response/ lists deepseek-flash and deepseek-v4-pro. Its stateless behavior is an application responsibility: after the first turn, your code must retain and resend the earlier messages or input items needed to maintain context. The documentation does not establish that one format is faster, cheaper, or more reliable than another; those properties depend on the model, payload, and your implementation.
How should I choose between Chat Completions and Responses?
Use the surface your existing tooling supports with the least translation. Chat Completions is shown in the first-call example and is a straightforward choice for a message list. Responses can be appropriate when your application is standardized on that API shape, but plan storage and replay of conversation history because the endpoint does not keep state for you.
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How is DeepSeek API pricing calculated?
DeepSeek quotes model prices per 1 million tokens. Input and output are charged separately, and input is split into cached and uncached usage. The price also depends on whether the request arrives during a defined peak window. The following values are the vendor’s published US-dollar rates in 2026; verify the live table before relying on them because DeepSeek reserves the right to change prices.
| Model | Cached input, off-peak | Cached input, peak | Uncached input, off-peak | Uncached input, peak | Output, off-peak | Output, peak |
|---|---|---|---|---|---|---|
deepseek-flash |
$0.003 per 1M tokens | $0.006 per 1M tokens | $0.15 per 1M tokens | $0.30 per 1M tokens | $0.60 per 1M tokens | $1.20 per 1M tokens |
deepseek-v4-pro |
$0.022 per 1M tokens | $0.044 per 1M tokens | $0.66 per 1M tokens | $1.32 per 1M tokens | $1.98 per 1M tokens | $3.96 per 1M tokens |
These are token charges, not a flat per-request fee. A request with a long uncached prompt and a short answer can cost more than a request with a short prompt and a long cached prefix, depending on the model and time window.
When are peak rates applied?
Peak hours are 01:00–04:00 UTC and 06:00–10:00 UTC, Monday through Friday, excluding Chinese public holidays. All other times are off-peak, including weekends and Chinese public holidays for the full day. Off-peak rates are one-half of the corresponding peak rates. The schedule is a billing rule, not a guarantee of better or worse response latency.
How do I calculate a request cost?
Convert each token count to a fraction of one million, multiply it by the applicable rate, and add the input and output amounts:
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cost = (cached_input_tokens / 1,000,000 × cached_input_rate)
+ (uncached_input_tokens / 1,000,000 × uncached_input_rate)
+ (output_tokens / 1,000,000 × output_rate)For example, an off-peak Flash request with 100,000 uncached input tokens and 20,000 output tokens costs $0.027: $0.015 for input (0.1 × $0.15) plus $0.012 for output (0.02 × $0.60). At peak rates, the same usage costs $0.054. If the same 100,000 input tokens are classified as cached, the off-peak total is $0.0123: $0.0003 for cached input plus $0.012 for output.
For Pro, 100,000 uncached input tokens and 20,000 output tokens cost $0.1056 off-peak: $0.066 for input plus $0.0396 for output. At peak rates, that usage costs $0.2112. These examples assume the entire input falls into one cache category and exclude any separate tool-request charges.
What does cached input mean for a budget?
Cache status is supplied as part of usage accounting and can materially change the input rate. A stable, reusable prompt prefix may be classified differently from newly varying content, but your application should rely on the usage fields returned for actual requests rather than assume every repeated string will receive the cached rate. Record cached and uncached input tokens separately in your cost telemetry.
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What do Search and Fetch cost?
Search and Fetch are separate APIs with their own successful-request charges. The pricing documentation lists $0.75 per 1,000 successful Search requests and $0.75 per 1,000 successful Fetch requests. Their guide at https://api-docs.deepseek.com/guides/search/ defines successful requests for billing and documents Bearer authentication.
Keep these charges separate from model token estimates. A workflow that performs several successful searches or fetches can incur tool costs even when the final model response is short. Track the count of successful calls, the model token usage, and the time window as separate fields.
The documentation also says server-side tool calls made through DeepSeek Harness, Claude Code, or Codex are currently free, with per-request billing scheduled to begin at 00:00 Beijing time on November 1, 2026. As of October 26, 2026, that date is in the future; confirm the current status and rate near the time you deploy because this policy is explicitly time-sensitive.
How should I design a safe production integration?
A working hello-world request is not enough for a service that handles customer data or generates billable traffic. Put the following controls around the API call.
- Protect the secret. Load the key from a secret manager or protected environment variable. Restrict who can read it, rotate it when staff or systems change, and redact authorization headers from logs.
- Pin the public model ID. Use
deepseek-flashordeepseek-v4-prorather than a retired alias, and log the selected ID with each request. - Bound output. Set a practical output limit for your application instead of allowing every request to approach the 384K published maximum. This protects latency, memory, and spend.
- Measure token categories. Capture input, output, cached-input, and uncached-input usage where the response exposes them. Multiply by the rate for the request’s UTC time window when producing a bill estimate.
- Preserve state deliberately. Chat-style calls carry their message list in each request. Responses API calls are stateless, so persist the history your next turn needs.
- Separate tool budgets. Count successful Search and Fetch calls independently from model tokens.
- Review changes. Check the pricing page and changelog before a major release, after a model announcement, and whenever a spending forecast changes.
What should I check when a request fails?
401 or authentication errors
Verify that the key exists in the running process, that the client is sending Authorization: Bearer ..., and that the key has not been revoked. Check for an accidental newline or surrounding quote in the environment variable. Compare the SDK configuration with a minimal direct HTTP request, but never print the secret while debugging.
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Check spelling and case for deepseek-flash and deepseek-v4-pro. Do not start with a legacy V4 Flash alias; the current Flash ID is the supported way to receive V4.1-Flash-1017. If the vendor has announced a routing change, read the current changelog and pricing table before changing code.
Unexpectedly high bills
Inspect input and output token counts, cache classification, and the UTC timestamp for each request. Long conversation histories can make every turn’s input expensive, particularly when input is uncached. Add output limits, trim history according to your product’s requirements, and distinguish model charges from Search and Fetch request counts.
Missing context in a multi-turn exchange
For a stateless Responses API integration, resend the required conversation history on every turn. For any format, log the message or input-item count and token estimate before sending, while removing personal data and secrets from logs.
What is a practical launch checklist?
- Create a key and store it outside the repository.
- Make one authenticated request to
https://api.deepseek.comwithdeepseek-flash. - Confirm that the response content is read from the SDK field your application expects.
- Choose OpenAI-compatible, Anthropic-compatible, or Responses format based on your existing client and state-management design.
- Verify whether your workload needs Flash vision support or the Pro model, rather than assuming model quality from the name.
- Instrument cached input, uncached input, output tokens, model ID, UTC timestamp, and successful Search or Fetch counts.
- Apply the published off-peak and peak rates to a small sample of real usage, then set a budget alert.
- Recheck pricing and the changelog before production rollout.
The shortest reliable path is therefore: use a current model ID, authenticate with a Bearer key, send a small test request through the compatibility layer your code already supports, and calculate cost from the returned token categories and UTC pricing window. Treat model routing, rates, and the planned Search and Fetch billing change as live service details rather than permanent constants.
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Can I keep using an older DeepSeek model alias in an existing application?
DeepSeek currently says older V4 Flash aliases are temporarily accepted and routed to the V4.1 Flash service, but it does not promise how long that compatibility will last. Migrate during a planned release to deepseek-flash, then monitor the changelog for a retirement notice.
Does a larger context window automatically make a request more expensive?
The 1M-token context limit is a capacity specification, not a fee by itself. Your bill is driven by the tokens actually processed, their cached or uncached status, the output count, the selected model, and the applicable time window.
Are peak hours a performance or availability guarantee?
No. DeepSeek defines peak and off-peak periods for pricing. The published schedule does not establish that requests are faster, slower, or more available in either period, so use your own latency and error measurements for scheduling decisions.
How should I budget a workload whose cache rate changes over time?
Create separate estimates for cached and uncached input, then multiply each by its own rate instead of applying one blended input price. Reconcile the estimate with usage records after a representative batch and revise the cached fraction as your prompts evolve.
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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
