DeepSeek API Integration Steps

⚠️ Pending Update · 2026-08-29 Verification · Content may be outdated, please refer to official docs Updated: 2026-08-29 · Status: Pending Verification

Introduction

DeepSeek is a top-tier open-source LLM team. Its deepseek-chat (V3) and deepseek-reasoner (R1) excel at reasoning and coding tasks, with token prices far below GPT-4o. This article shows how to integrate them, covering standard chat, the reasoning model, streaming, and function calling.

架构图

flowchart TD A[Register at platform.deepseek.com] --> B[Verify phone] B --> C[Top up ¥10 or use bonus] C --> D[Create API Key] D --> E[Use OpenAI SDK] E --> F{Choose model} F -->|deepseek-chat| G[V3: general chat] F -->|deepseek-reasoner| H[R1: math/code reasoning]

Get an API Key

Visit https://platform.deepseek.com, register, and verify your phone number.
Go to API Keys → Create API Key and copy the sk-... value.
Top up at least ¥10 in Billing (or use the welcome credit).

Install and Configure

DeepSeek is fully OpenAI-compatible, so reuse the openai SDK:

pip install openai
export DEEPSEEK_API_KEY="sk-..."

Standard Chat (deepseek-chat)

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["DEEPSEEK_API_KEY"],
    base_url="https://api.deepseek.com/v1",
)

resp = client.chat.completions.create(
    model="deepseek-chat",
    messages=[
        {"role": "system", "content": "You are a concise assistant."},
        {"role": "user", "content": "Explain an inverted index in one sentence."},
    ],
    temperature=0.3,
    max_tokens=256,
)
print(resp.choices[0].message.content)

Reasoning Model (deepseek-reasoner)

R1 returns a chain-of-thought — great for math, logic, and complex code:

resp = client.chat.completions.create(
    model="deepseek-reasoner",
    messages=[
        {"role": "user", "content": "A pool has two inlet pipes A (3h to fill) and B (6h to fill) and an outlet C (4h to drain). How long to fill with all three open?"},
    ],
)
print("Reasoning:")
print(resp.choices[0].message.reasoning_content)
print("\nFinal answer:")
print(resp.choices[0].message.content)

Note: reasoning_content is R1-specific; V3 does not return it.

Streaming

stream = client.chat.completions.create(
    model="deepseek-chat",
    messages=[{"role": "user", "content": "Write a haiku about autumn."}],
    stream=True,
)
for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="", flush=True)
print()

Function Calling

tools = [{
    "type": "function",
    "function": {
        "name": "get_stock_price",
        "description": "Get the real-time price of a stock",
        "parameters": {
            "type": "object",
            "properties": {"symbol": {"type": "string"}},
            "required": ["symbol"],
        },
    },
}]
resp = client.chat.completions.create(
    model="deepseek-chat",
    messages=[{"role": "user", "content": "What's the price of AAPL?"}],
    tools=tools,
)
print(resp.choices[0].message.tool_calls[0].function)

Pricing (reference)

deepseek-chat: ¥0.5 / 1M input (¥0.1 cache hit), ¥8 / 1M output
deepseek-reasoner: ¥4 / 1M input, ¥16 / 1M output (non-cache)

About an order of magnitude cheaper than GPT-4o. Prefix caching saves another 80% for repeated system prompts.

Context Caching

DeepSeek supports context caching for repeated system prompts — cache hits drop input price to ¥0.1/1M (from ¥0.5). This is great for multi-turn agents and RAG lookups. Caching is automatic, but the prompt prefix must be byte-identical. Put fixed system instructions and tool definitions at the start of the messages array to maximize hit rate.

Troubleshooting

402 Insufficient Balance: Out of credit. Top up in Billing.
R1 output too long: Cap with max_tokens or truncate reasoning_content.
Self-host: Download the open weights and serve with vLLM or SGLang. R1 distill fits on 2x A100.
Slow responses from abroad: Latency may be high. Use a proxy or the DeepSeek mirror on OpenRouter.

DeepSeek is one of the most cost-effective choices for Chinese-language workloads.

Best Practices

Use deepseek-reasoner for reasoning tasks: R1 for math/code review/logic; V3 for daily chat saves tokens.
Pricing is per-token: input 0.27 yuan/M, output 1.1 yuan/M (CNY) — 100x cheaper than GPT-4o.
Always enable streaming: reasoner takes long to think; without streaming users think it's stuck.
Never hardcode API Key in frontend: DeepSeek bills per key; frontend hardcode is giving money away.

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