Deepseek API

API ID 13017

LLM API providing text generation, reasoning, and multi-turn conversation capabilities via OpenAI-compatible and Anthropic-compatible endpoints.

3,508 ms avg response

API Documentation

Endpoints

Request

Generate text responses using large language models. Supports single-turn and multi-turn conversations with system prompts, function calling, streaming output, and context windows up to 1M tokens. Input accepts user messages, assistant history, and system instructions. Output returns generated text with token usage statistics.

Endpoint ID: 26129
POST https://docs.zylalabs.com/api/13017/deepseek+api/26129/chat+completions
INPUT PARAMETERS

Chat Completions — Endpoint Features

Object Description
Request Body Required Json

Free test requests remaining: 3 of 3.


INPUT PARAMETERS

API EXAMPLE RESPONSE
JSON
{"id":"83ed9b00-0083-4a0f-88fc-8d357b980eab","object":"chat.completion","created":1782683642,"model":"deepseek-v4-flash","choices":[{"index":0,"message":{"role":"assistant","content":"Here's a Python function to calculate Fibonacci numbers with multiple approaches:\n\n## Method 1: Iterative (Most Efficient)\n\n```python\ndef fibonacci_iterative(n):\n    \"\"\"\n    Calculate the nth Fibonacci number using iteration.\n    \n    Args:\n        n: Non-negative integer\n    \n    Returns:\n        The nth Fibonacci number\n    \"\"\"\n    if n < 0:\n        raise ValueError(\"n must be non-negative\")\n    if n <= 1:\n        return n\n    \n    a, b = 0, 1\n    for _ in range(2, n + 1):\n        a, b = b, a + b\n    return b\n```\n\n## Method 2: Recursive (Simple but Inefficient)\n\n```python\ndef fibonacci_recursive(n):\n    \"\"\"\n    Calculate the nth Fibonacci number using recursion.\n    \n    Args:\n        n: Non-negative integer\n    \n    Returns:\n        The nth Fibonacci number\n    \"\"\"\n    if n < 0:\n        raise ValueError(\"n must be non-negative\")\n    if n <= 1:\n        return n\n    return fibonacci_recursive(n - 1) + fibonacci_recursive(n - 2)\n```\n\n## Method 3: Recursive with Memoization (Optimized)\n\n```python\nfrom functools import lru_cache\n\n@lru_cache(maxsize=None)\ndef fibonacci_memoized(n):\n    \"\"\"\n    Calculate the nth Fibonacci number using recursion with memoization.\n    \n    Args:\n        n: Non-negative integer\n    \n    Returns:\n        The nth Fibonacci number\n    \"\"\"\n    if n < 0:\n        raise ValueError(\"n must be non-negative\")\n    if n <= 1:\n        return n\n    return fibonacci_memoized(n - 1) + fibonacci_memoized(n - 2)\n```\n\n## Method 4: Generator for Fibonacci Sequence\n\n```python\ndef fibonacci_generator(n):\n    \"\"\"\n    Generate the first n Fibonacci numbers.\n    \n    Args:\n        n: Number of Fibonacci numbers to generate\n    \n    Yields:\n        Fibonacci numbers one by one\n    \"\"\"\n    a, b = 0, 1\n    for _ in range(n):\n        yield a\n        a, b = b, a + b\n```\n\n## Usage Examples:\n\n```python\n# Test the functions\nprint(fibonacci_iterative(10))  # Output: 55\nprint(fibonacci_recursive(10))  # Output: 55\nprint(fibonacci_memoized(10))   # Output: 55\n\n# Using the generator\nprint(list(fibonacci_generator(10)))  # Output: [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]\n\n# Generate first 10 Fibonacci numbers\nfor i in range(10):\n    print(f\"F({i}) = {fibonacci_iterative(i)}\")\n```\n\n## Performance Comparison:\n\n- **Iterative**: O(n) time, O(1) space - Best for single calculations\n- **Recursive**: O(2^n) time, O(n) space - Very slow for large n\n- **Memoized**: O(n) time, O(n) space - Good for multiple calls\n- **Generator**: O(n) time, O(1) space - Best for generating sequences\n\nThe iterative approach is generally recommended for most use cases due to its efficiency and simplicity."},"logprobs":null,"finish_reason":"stop"}],"usage":{"prompt_tokens":20,"completion_tokens":703,"total_tokens":723,"prompt_tokens_details":{"cached_tokens":0},"prompt_cache_hit_tokens":0,"prompt_cache_miss_tokens":20},"system_fingerprint":"fp_8b330d02d0_prod0820_fp8_kvcache_20260402"}
Chat Completions — CODE SNIPPETS

curl --location --request POST 'https://zylalabs.com/api/13017/deepseek+api/26129/chat+completions' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{
  "model": "deepseek-chat",
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful coding assistant."
    },
    {
      "role": "user",
      "content": "Write a Python function to calculate fibonacci numbers."
    }
  ],
  "max_tokens": 1024,
  "temperature": 0.7,
  "stream": false
}'

    

API Access Key & Authentication

After signing up, every developer is assigned a personal API access key, a unique combination of letters and digits provided to access to our API endpoint. To authenticate with the Deepseek API simply include your bearer token in the Authorization header.

Headers
Header Description
Authorization Required Should be Bearer access_key. See "Your API Access Key" above when you are subscribed.

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Overview

This API provides access to large language models supporting text generation, complex reasoning, code generation, and multi-turn conversational workflows.

Supported capabilities:
- Single and multi-turn chat completions
- System prompt and instruction following
- Structured reasoning with chain-of-thought output
- Function/tool calling for agentic workflows
- Vision input processing (image + text prompts)
- Streaming responses via server-sent events
- Context window up to 1,000,000 tokens

Available models:
- deepseek-v4-pro: Full-capability reasoning model with 1M token context
- deepseek-v4-flash: Lightweight model optimized for speed with 128K token context

Endpoint compatibility:
- OpenAI Chat Completions format (/v1/chat/completions)
- Anthropic Messages format (/v1/messages)

Request parameters include model selection, max_tokens, temperature, top_p, presence/frequency penalties, and stop sequences. Responses return token usage statistics including prompt tokens, completion tokens, and cached token counts.

Rate limits and pricing vary by model tier. API key authentication required via Bearer token or x-api-key header.

Deepseek API FAQs

The Deepseek API's endpoints return generated text responses based on user input, including single-turn and multi-turn conversations. The output includes the assistant's message content and token usage statistics, such as prompt tokens and completion tokens.

Key fields in the response data include "id" (unique identifier), "object" (type of response), "created" (timestamp), "model" (used model), and "choices" (array containing the generated message and its role).

Users can customize requests with parameters such as "model" (to select the desired model), "max_tokens" (to limit response length), "temperature" (to control randomness), "top_p" (for nucleus sampling), and penalties for presence/frequency.

The response data is organized in a JSON format. It includes a main object with metadata (like "id" and "created") and a "choices" array, where each choice contains the assistant's message and its role, facilitating easy parsing and usage.

The endpoints provide information such as generated text responses, reasoning outputs, and code snippets. They support multi-turn conversations, structured reasoning, and function/tool calling, making them versatile for various applications.

Users can utilize the returned data by extracting the "content" field from the "choices" array for the generated response. Additionally, they can analyze token usage statistics to optimize their requests and manage costs effectively.

Typical use cases include chatbots for customer support, educational tools for tutoring, content generation for blogs, and coding assistance for developers. The API's multi-turn capabilities enhance interactive applications.

Data accuracy is maintained through the use of advanced language models trained on diverse datasets. Continuous updates and improvements to the models help ensure high-quality outputs, although users should validate critical information independently.

General FAQs

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