DeepSeek API Integration Guide (2026): Models, Setup, Code Examples & the V4 Migration

Quick Answer

The DeepSeek API is an OpenAI-compatible (and Anthropic-compatible) REST API at https://api.deepseek.com that gives developers access to DeepSeek’s large language models – currently deepseek-v4-flash and deepseek-v4-pro, both with a 1-million-token context window. You authenticate with a Bearer API key from platform.deepseek.com and call POST /chat/completions using the standard OpenAI SDK format. Important: the legacy model names deepseek-chat and deepseek-reasoner are retired on July 24, 2026 – new integrations should use the V4 model names.

Introduction

The fast-paced AI environment today enables developers to build applications using large language models (LLMs) and multimodal APIs, resulting in intelligent chatbots and advanced content-generation tools. DeepSeek stands as a powerful innovation that is gaining momentum in the rapidly advancing field of AI development.

So what’s behind the buzz? The DeepSeek platform provides developers with open-weight, high-performance models – headlined in 2026 by the DeepSeek-V4 family (deepseek-v4-pro and deepseek-v4-flash), which pair frontier-level coding and reasoning performance with a 1M-token context window and some of the lowest per-token prices of any major API. The DeepSeek API enables developers to access powerful capabilities that are specifically designed for practical applications when working with text, images, or code.

This guide provides information about DeepSeek and its comparison to other leading APIs, as well as a step-by-step process for project integration. This walkthrough provides step-by-step instructions to help beginners start their first AI application development while showing them how to replace OpenAI or Claude with confidence. It also covers the one thing every existing DeepSeek user needs to act on right now: migrating off the deprecated deepseek-chat and deepseek-reasoner model names before they stop working on July 24, 2026. Start building smarter websites with DeepSeek API integration and unlock endless possibilities for your next project

Key Takeaways

• The DeepSeek API serves two models in 2026: deepseek-v4-flash (fast, low-cost default) and deepseek-v4-pro (strongest reasoning and agentic coding), both with a 1M-token context window.
• Deadline: the legacy model names deepseek-chat and deepseek-reasoner are fully retired on July 24, 2026, 15:59 UTC. Migrating is usually a one-line model-name change.
• The API is OpenAI-compatible and Anthropic-compatible – you can keep your existing OpenAI or Anthropic SDK and just change the base URL, key, and model name.
• Pricing is usage-based and very low: deepseek-v4-flash costs about $0.14 per 1M input tokens (cache miss) and $0.28 per 1M output tokens, with cache-hit input tokens roughly 1/50th of that. V4-Pro costs more per token for harder tasks.
• There are no fixed per-account rate limits, but DeepSeek applies dynamic concurrency limits under load and returns HTTP 429 – build in retries with exponential backoff.
• Easy to use with Python, JavaScript, Postman, or curl via OpenAI-compatible endpoints.
• No hard rate limits on the official API, but usage may vary with traffic or third-party hosts.
• Free/self-hosted access available via Hugging Face (V4 weights are MIT-licensed); hosted use via OpenRouter or Together.ai.
• Follow best practices: secure your API key, cache responses, and optimize prompts.
• Can be embedded into apps like Froala Editor for AI-assisted content creation.

DeepSeek API at a glance (July 2026)

What is DeepSeek?

DeepSeek operates as an open-source AI project that develops large language and vision models for developers, researchers, and tech enthusiasts. DeepSeek presents itself as a clear and fast API solution that provides open access to its capabilities while encouraging community involvement and state-of-the-art functionality.

In 2026, the DeepSeek API is built around the DeepSeek-V4 family, released on April 24, 2026:

•   deepseek-v4-pro – a 1.6-trillion-parameter Mixture-of-Experts model (49B active parameters) with a 1M-token context. It is the strongest open-weight model available for reasoning, math, and agentic coding, and the right choice when output quality matters more than cost.
•   deepseek-v4-flash – a 284B-parameter MoE model (13B active) with the same 1M-token context. It approaches Pro-level reasoning at a fraction of the price and latency, making it the default for most production workloads.

Both models support an optional “thinking” mode: the model generates an internal chain of thought before answering, which improves accuracy on math and multi-step coding at the cost of more output tokens.

Earlier open-weight models remain available for self-hosting and research:

DeepSeek-Coder functions as an LLM (Large Language Model) that specializes in code generation, completion, and understanding of multiple programming languages. The system proves beneficial for software development automation, code review support, and the development of AI-based developer tools. On the hosted API, its capabilities have been folded into the V4 models – use deepseek-v4-pro for serious coding work.

DeepSeek-VL functions as a strong vision-language model that accepts multiple input types, including images, charts, documents, and text, to support applications such as visual question answering, caption generation, and document comprehension.

Note that DeepSeek-VL is used via its open weights (e.g., self-hosted from Hugging Face or through third-party hosts); the official DeepSeek API is focused on text and code.

The models within DeepSeek provide users with a wide range of AI application possibilities, including:

• Coding assistance
• Research and data analysis
• Workflow automation
• Multimodal applications combining text and images
•   Long-document and long-codebase analysis (the 1M-token context fits entire repositories or book-length documents in a single request)

The open-source community supports DeepSeek as a flexible platform that serves developers who want to implement AI-driven development and users who need intelligent visual interfaces.

Urgent: migrating from deepseek-chat / deepseek-reasoner to V4

If you built your integration before April 2026 (including from an earlier version of this guide), your code probably calls deepseek-chat or deepseek-reasoner. Those names are legacy aliases that currently route to deepseek-v4-flash, and they will be fully retired and inaccessible after July 24, 2026, 15:59 UTC. After that timestamp, requests using the old names fail.

The migration itself is a one-line change:

"model": "deepseek-chat"

"model": "deepseek-v4-flash"

If you used deepseek-reasoner (chain-of-thought mode), switch to deepseek-v4-flash and enable thinking mode in the request body instead of switching model names:

{

  "model": "deepseek-v4-flash",

  "thinking": {"type": "enabled"},

  "reasoning_effort": "high",

  "messages": [{"role": "user", "content": "..."}]

}

Three practical tips:

1. The base URL and your API key do not change;
2. V4-Flash is a different model from the V3.2 weights the old alias served, so re-run your evals rather than assuming identical outputs.
3. Check any gateways (OpenRouter, LiteLLM, etc.) and monitoring dashboards that pin the old model names

Key features of the DeepSeek API

DeepSeek stands apart from other AI APIs through its distinctive features in the expanding AI API market. The DeepSeek API stands out through these distinctive features, which make it an attractive option for developers and researchers:

Advanced language and vision understanding

DeepSeek models surpass traditional LLMs through their ability to merge natural language processing with visual understanding capabilities. DeepSeek delivers state-of-the-art performance through accurate context-aware outputs when you need to analyze code or generate text, or interpret image-text combinations.

1M-token context window

Both V4 models accept up to one million tokens of context – enough for entire codebases, long legal documents, or multi-hour transcripts in a single request. DeepSeek’s hybrid sparse-attention architecture keeps long-context requests fast and affordable compared with earlier generations.

Built-in reasoning (“thinking”) mode

Instead of maintaining a separate reasoning model, V4 exposes thinking as a per-request toggle with adjustable reasoning_effort. You pay for deeper reasoning only on the prompts that need it.

Open-source friendly

The open nature of DeepSeek distinguishes it from proprietary alternatives because it promotes open collaboration among developers. The developer-friendly ecosystem of DeepSeek enables open environment development, model auditing, and contribution and extension through its supportive framework for innovation and trust. The DeepSeek-V4 weights are published on Hugging Face under the permissive MIT License.

Scalable and high-performance

DeepSeek operates at high speeds and maintains low response times, which makes it appropriate for projects ranging from solo work to large enterprise implementations. The robust architecture of this system enables smooth handling of traffic and load regardless of the number of users it serves.

Competitive edge over other APIs

DeepSeek provides distinct advantages compared to proprietary APIs like OpenAI, Anthropic Claude, and Google Gemini through its unique strengths:

• Open-source architecture with fewer usage limitations
•   Dramatically lower per-token pricing than comparable frontier APIs, with automatic context-cache discounts
•   Drop-in compatibility with both the OpenAI SDK and the Anthropic SDK – and direct support in agent tools like Claude Code, GitHub Copilot, and OpenCode

While each API has its strengths, DeepSeek stands out as a flexible and transparent solution, especially for those looking to build with open tools, experiment freely, or fine-tune for specific use cases.

Getting access to the DeepSeek API

The process to integrate DeepSeek API with your system remains simple, whether you are new to AI development or building it into an existing system.

Sign up for an account

The first step involves visiting deepseek.com or the official GitHub page to obtain registration or access instructions. DeepSeek models that are open on Hugging Face platforms do not require direct registration with DeepSeek unless you use their hosted API service.

Sign up for the DeepSeek API at platform.deepseek.com/sign_up.

Generate your API key

If you’re using an official DeepSeek-hosted API endpoint (or accessing via a third-party provider like Together.ai or OpenRouter), you’ll typically need to sign up and retrieve an API key from your dashboard. This key is used to authenticate your requests and track usage.

Pricing and free access

DeepSeek’s model weights – including the current DeepSeek-V4 series – are openly available on Hugging Face under the MIT License, which permits free research and commercial use, including self-hosting.

On the official hosted API (as of July 2026): deepseek-v4-flash costs about $0.14 per 1M input tokens on a cache miss and $0.28 per 1M output tokens, with cache-hit input tokens priced around $0.0028 per 1M. deepseek-v4-pro is roughly 3× the Flash price and is aimed at the hardest reasoning and agentic tasks. Billing is purely usage-based with no monthly minimum, which makes DeepSeek one of the cheapest ways to prototype and ship AI features. Always check the official pricing page for current numbers, since DeepSeek adjusts prices and runs launch discounts.

If you’re using the models via hosted APIs (e.g., Together.ai or OpenRouter), pricing will vary by provider and usage volume. DeepSeek’s open-source approach makes it a budget-friendly option for testing, development, and even production use when self-hosted.

DeepSeek Models & Pricing

Documentation and resources

The official API documentation provides a centralized reference for DeepSeek API integration. It includes:

• Quick start guides
• Input/output formats
• Supported endpoints
• Model capabilities
• Sample scripts and integration examples

You can also find additional resources and real-world usage patterns on platforms like GitHub, Hugging Face model cards, and community forums. These sources offer valuable implementation tips and troubleshooting insights shared by developers.

Understanding the API basics

Before diving into implementation, it’s important to get familiar with the core components of the DeepSeek API. This section walks you through the base URL, authentication process, and any rate limits you should be aware of.

Base URL and endpoint structure

The DeepSeek API is accessible via:

https://api.deepseek.com

(For OpenAI‑compatible clients, you may also use `https://api.deepseek.com/v1`; this “v1” namespace is for compatibility, not model versions.)

DeepSeek also exposes an Anthropic-compatible endpoint, so teams using the Anthropic SDK can point it at DeepSeek without rewriting their integration.

Endpoints follow a consistent RESTful structure. For example:

POST /chat/completions – For generating text completions using models like deepseek-v4-flash or deepseek-v4-pro

Each request typically includes a `model` parameter, a `messages` array, and hyperparameters such as `temperature`, `max_tokens`, `frequency_penalty`, etc. V4 requests can additionally include a thinking object and a reasoning_effort level to control chain-of-thought reasoning.

Authentication methods

Authentication is handled via API keys. Once you create an account or access DeepSeek via a third-party provider (like OpenRouter), you’ll receive a token.

To authenticate a request, include the following header:

Authorization: Bearer YOUR_API_KEY

Never expose your key in client-side applications. Store it securely and rotate it regularly if needed.

Rate limits and usage caps

DeepSeek does not publish fixed per-account rate limits, but it does apply dynamic concurrency limits based on overall server load. When your concurrency ceiling is reached, the API returns HTTP 429 (Too Many Requests) – so production code should always implement retries with exponential backoff, even on the official API.

During periods of high load:

• Non-streaming requests may return continuous empty lines.
• Streaming requests may return keep-alive events (e.g., : keep-alive) until a full response is ready.

If a response cannot be completed within 30 minutes, the server will automatically close the connection.

⚠️ If you’re using DeepSeek through a third-party API provider (like OpenRouter or Together.ai), additional plan-based rate limits may apply on top of DeepSeek’s own concurrency behavior.

And if you’re self-hosting (e.g., via Hugging Face), usage is only limited by your compute power (e.g., GPU/CPU/memory).

For up-to-date behavior, check the official DeepSeek API docs or your provider’s dashboard.

Step-by-step integration guide

Let’s learn to integrate the DeepSeek API step by step.

Prerequisites

Before you begin your DeepSeek API integration, make sure you have the following:

• Basic programming knowledge in a language like Python or JavaScript
• Access to an API client such as:
  • Postman
  • curl (command-line tool)
  • A language-specific HTTP library like requests (Python) or fetch (JavaScript)
• An API key from DeepSeek or a third-party provider (e.g., OpenRouter)

Making your first request

Let’s walk through a basic “Hello World”-style text generation request using the /chat/completions endpoint.

Python example:

import requests

url = "https://api.deepseek.com/v1/chat/completions"

headers = {

    "Authorization": "Bearer YOUR_API_KEY",

    "Content-Type": "application/json"

}

data = {

    "model": "deepseek-v4-flash", # legacy name "deepseek-chat" is retired on July 24, 2026

    "messages": [{"role": "user", "content": "Say hello to the world!"}],

    "temperature": 0.7

}

response = requests.post(url, headers=headers, json=data)

print(response.json())

cURL example:

curl https://api.deepseek.com/v1/chat/completions \

  -H "Authorization: Bearer YOUR_API_KEY" \

  -H "Content-Type: application/json" \

  -d '{

        "model": "deepseek-v4-flash",

        "messages": [{"role": "user", "content": "Say hello to the world!"}],

        "temperature": 0.7

      }'

Bonus – using the OpenAI SDK (recommended):

# pip install openai
from openai import OpenAI
 
client = OpenAI(api_key="YOUR_API_KEY", base_url="https://api.deepseek.com")
 
response = client.chat.completions.create(
    model="deepseek-v4-flash",
    messages=[{"role": "user", "content": "Say hello to the world!"}],
)
print(response.choices[0].message.content)

Response format:

The response will be a JSON object with a choices array. You can extract the generated text like this:

{

  "choices": [

    {

      "message": {

        "role": "assistant",

        "content": "Hello, world!"

      }

    }

  ]

}

Step-by-step setup in Postman

You can run the equivalent of the above request code in Postman by setting up a POST request with the correct headers and JSON body.

Here’s how to do it step by step in Postman:

1. Set the request method to POST

URL:

https://api.deepseek.com/v1/chat/completions

2. Set the headers

Go to the “Headers” tab and add:

Replace YOUR_API_KEY with your actual DeepSeek API key.

3. Set the request body

Go to the “Body” tab → Select “raw” → Choose JSON (from the dropdown).

Paste the following JSON:

{

  "model": "deepseek-v4-flash",
  "messages": [

    {

      "role": "user",

      "content": "Say hello to the world!"

    }

  ],

  "temperature": 0.7

}

4. Send the request

Click the “Send” button.

5. View the response

You’ll get a JSON response like:

{

  "choices": [

    {

      "message": {

        "role": "assistant",

        "content": "Hello, world!"

      }

    }

  ]

}

Handling errors and debugging

When working with any API, it’s important to know how to handle errors gracefully.

Common error messages:

• 401 Unauthorized: Check if your API key is missing or incorrect.
• 400 Bad Request: Often due to malformed JSON or missing parameters – including calling a retired model name such as deepseek-chat after July 24, 2026.
• 402 Payment Required: Your account has insufficient balance to process this request.
• 429 Too Many Requests: Returned by the official API when DeepSeek’s dynamic concurrency limit is reached (and by third-party providers when you exceed plan limits). Retry with exponential backoff.
• 500 Internal Server Error: May occur under heavy load or internal issues.

Retry strategies:

• Use exponential backoff for retrying failed requests (especially 429, 500, 503 or timeout errors).
• Avoid retrying 400 or 401 errors unless corrected.

Logging best practices:

• Log the full request payload and response body (excluding API keys).
• Tag logs with timestamps and response status codes for traceability.

A quick note on Froala integration

Because DeepSeek is an API-based service, it can be embedded into various applications, including our very own Froala WYSIWYG Editor. While we won’t go deep into this here, the potential is exciting: AI-assisted content suggestions, intelligent grammar corrections, or auto-generated text snippets—all triggered live within the editing interface.

Imagine building a rich-text editor that not only formats your content but also helps generate it. That’s the kind of seamless experience developers can create by combining the Froala Editor with DeepSeek API integration.

For example, you could build a note-taking or documentation app where DeepSeek autocompletes sentences or summarizes content directly inside Froala’s editor frame.

DeepDive: Example use cases

Let’s explore how DeepSeek’s models perform in real-world scenarios—from writing clean code to understanding multimodal content.

Code completion or generation with DeepSeek-Coder

deepseek-v4-pro is DeepSeek’s strongest model for software development tasks such as autocompletion, refactoring suggestions, debugging, and generating functions from plain-text prompts – it currently posts open-source state-of-the-art results on agentic coding benchmarks. For lighter coding tasks, deepseek-v4-flash is usually sufficient and far cheaper.

Input/output sample:

Input prompt:

{"role": "user", "content": "Write a Python function to check if a number is prime."}

Output:

def is_prime(n):

    if n <= 1:

        return False

    for i in range(2, int(n**0.5) + 1):

        if n % i == 0:

            return False

    return True

This makes DeepSeek ideal for coding assistants, educational platforms, and documentation generators.

Editor and agent integrations: The DeepSeek API is now supported as a backend model by popular AI coding tools – including Claude Code, GitHub Copilot, and OpenCode – so you can use DeepSeek inside your IDE with no custom wrapper code. See DeepSeek’s Agent Integrations guide for setup.

Multimodal tasks with DeepSeek-VL

DeepSeek-VL enables models to process both visual and text inputs, making it useful for applications involving documents, images, and diagrams.  (Reminder: run DeepSeek-VL via its open weights or a third-party host – the official hosted API is text/code-focused.)

Example input:

• Image: A scanned document or screenshot
• Text prompt: “Summarize the key points from this image.”

The model interprets the visual content and responds with a meaningful summary or extracted data.

Sample application:

• Caption generation: Upload an image and receive an accurate caption like:
  “A group of engineers reviewing a circuit board schematic on a table.”
  

• Document Q&A: Upload a PDF and ask:
  “What is the main clause in paragraph 3?”
  → DeepSeek-VL reads the image-based document and extracts answers.

These use cases show how DeepSeek API integration can power next-gen apps across development, education, productivity, and visual understanding.

Popular DeepSeek API prompts and questions developers are searching

These are the highest-demand questions and prompt patterns people type into Google and AI assistants about the DeepSeek API right now. Each gets a direct answer here (and doubles as a copy-paste starting point).

“How do I migrate from deepseek-chat to deepseek-v4-flash?”

Change the model string from deepseek-chat to deepseek-v4-flash. Your base URL and API key stay the same. If you used deepseek-reasoner, use deepseek-v4-flash with “thinking”: {“type”: “enabled”}. Complete this before July 24, 2026, when the old names stop working.

“How do I use the DeepSeek API with the OpenAI Python SDK?”

Install the openai package, set base_url=“https://api.deepseek.com”, pass your DeepSeek key, and call chat.completions.create with model=“deepseek-v4-flash”. Streaming, function calling, and the messages array work as they do with OpenAI.

“DeepSeek API vs OpenAI API – which is cheaper?”

DeepSeek is substantially cheaper per token: deepseek-v4-flash runs about $0.14/1M input and $0.28/1M output tokens, an order of magnitude below comparable frontier-model pricing, with cache-hit input tokens discounted a further ~50×. For most high-volume text and coding workloads, DeepSeek is the lowest-cost frontier-adjacent option.

“How do I enable thinking / reasoning mode in the DeepSeek API?”

Add “thinking”: {“type”: “enabled”} to the request body (in the OpenAI SDK, pass it via extra_body) and optionally set reasoning_effort to low, medium, or high. Reserve it for math, debugging, and multi-step tasks – it improves accuracy but produces more output tokens.

“What is the DeepSeek API rate limit?”

There is no fixed published rate limit; DeepSeek dynamically limits concurrency based on server load and returns HTTP 429 when the ceiling is hit. Implement exponential-backoff retries and, for bursty workloads, consider a third-party host with guaranteed throughput.

“Write a prompt that makes DeepSeek generate production-ready code”

A pattern that works well with deepseek-v4-pro: “You are a senior [language] engineer. Write a [function/module] that [exact requirement]. Include type hints, docstrings, error handling, and unit tests. Do not include explanations outside code comments.” Being explicit about output structure is the single biggest quality lever.

Best practices for working with DeepSeek API

To get the most out of your DeepSeek API integration, follow these best practices for security, performance, and cost-efficiency.

Token management & security tips

• Never expose API keys in frontend code or public repositories.
• Use environment variables or secret managers to store keys securely.
• Rotate keys periodically and revoke them immediately if compromised.
• Consider using IP whitelisting if supported by your API provider.

Optimizing input prompts

• Keep prompts clear and context-rich, especially when working with coding or multimodal tasks.
• Avoid unnecessary verbosity—it can waste tokens and reduce model accuracy.
• Test prompt variations to identify the most efficient phrasing for consistent results.

Example:

Instead of saying:

“Please can you help me by writing a function that checks whether a number is prime?”
Try:
“Write a Python function to check if a number is prime.”

Choose the right model (and mode) per task

• Route routine tasks (summaries, edits, formatting, simple generation) to deepseek-v4-flash in non-thinking mode – it’s the cheapest and fastest path.
• Escalate hard reasoning, math, and complex debugging to thinking mode or deepseek-v4-pro, where the extra output tokens pay for themselves in accuracy.
• Keep stable system prompts and shared context identical across requests to maximize context-cache hits – cache-hit input tokens cost roughly 1/50th of cache-miss tokens.

Caching strategies for repeated calls

• If your app sends the same or similar requests frequently, implement caching at the application level.
• Cache both input-response pairs and processed outputs to reduce token consumption and latency.
• Use hash keys of prompt strings as identifiers for quick retrieval.

Rate limiting and efficient usage

Although DeepSeek’s official API does not enforce fixed rate limits, dynamic concurrency limits apply, and usage can still be impacted by:

• Server congestion
• Timeout thresholds (e.g., 30-minute max response window)

To ensure efficient usage:

• Batch smaller queries when possible
• Use streaming mode for faster response delivery
• Implement retry logic with exponential backoff for handling transient errors (especially HTTP 429, 500, and 503)

By following these practices, you’ll build more stable, secure, and scalable applications while keeping your DeepSeek usage optimized and cost-effective.

Conclusion

In this guide, we explored everything you need to know about DeepSeek API integration—from understanding its models and features to setting up your first request and handling advanced use cases. Whether you’re shipping high-volume features on deepseek-v4-flash, tackling hard reasoning with deepseek-v4-pro, or self-hosting the MIT-licensed open weights, the API offers flexibility, speed, and open accessibility for modern AI development. And if you’re on the legacy deepseek-chat or deepseek-reasoner names, migrate to the V4 model names before July 24, 2026.

Now it’s your turn. Start small with a simple prompt, or go big by integrating DeepSeek into your next project. There’s a lot to discover—and even more to build.

FAQs

What is the DeepSeek API used for?

The DeepSeek API enables developers to integrate advanced AI models – currently deepseek-v4-flash and deepseek-v4-pro, both with a 1M -token context window-into their applications. It’s ideal for building chatbots, code assistants, and AI‑powered content tools using open‑source, high‑performance models.

How do I get started with DeepSeek API integration?

To start using the DeepSeek API, create an account at platform.deepseek.com and generate an API key, then make API calls using Python, JavaScript, Postman, or curl. DeepSeek supports OpenAI-compatible and Anthropic-compatible endpoints, making it easy to switch from other APIs such as OpenAI or Claude by changing only the base URL, key, and model name.

Is the DeepSeek API free to use?

The hosted API is paid but very inexpensive (usage-based, no subscription – roughly $0.14–$0.28 per million tokens on deepseek-v4-flash). Many DeepSeek models, including the DeepSeek-V4 series, are available for free as open weights on Hugging Face under the MIT License, so you can self-host at no licensing cost. If you use hosted endpoints via providers such as OpenRouter or Together.ai, usage fees may apply depending on your plan and traffic volume.

What happened to deepseek-chat and deepseek-reasoner?

They became legacy aliases when DeepSeek-V4 launched on April 24, 2026 (routing to deepseek-v4-flash’s non-thinking and thinking modes), and they are fully retired on July 24, 2026, 15:59 UTC. After that, requests using the old names fail – update your model strings to deepseek-v4-flash or deepseek-v4-pro.

What is the difference between deepseek-v4-flash and deepseek-v4-pro?

Both have a 1M-token context. deepseek-v4-flash (284B parameters, 13B active) is the fast, low-cost default for most workloads. deepseek-v4-pro (1.6T parameters, 49B active) costs roughly 3× more per token and is the pick for the hardest reasoning, math, and agentic coding tasks.

Does the DeepSeek API have rate limits?

No fixed quotas are published, but DeepSeek applies dynamic concurrency limits based on server load and returns HTTP 429 when they’re hit. Build retry logic with exponential backoff, and note that third-party hosts (OpenRouter, Together.ai) add their own plan-based limits.

Is the DeepSeek API compatible with the OpenAI SDK?

Yes. Point the OpenAI SDK’s base_url at https://api.deepseek.com, use your DeepSeek API key, and pass a DeepSeek model name. An Anthropic-compatible endpoint is also available. Compatibility isn’t identical behavior, though – re-test outputs, tool calls, and JSON reliability after switching.

What is the DeepSeek API context window?

Both deepseek-v4-flash and deepseek-v4-pro support a context window of 1,000,000 tokens – large enough for entire codebases or book-length documents in a single request.