Use DeepSeek with Vercel AI SDK in 5 Minutes

Vercel AI SDK is the fastest way to ship an AI chat feature in Next.js — and DeepSeek is the cheapest model worth shipping. Combined, you get a streaming chatbot that costs almost nothing to run: DeepSeek V4 API pricing per 1M tokens starts at just $0.14 for input.

No custom adapters, no proxies, no Chinese phone number. DeepSeek speaks the OpenAI protocol, so @ai-sdk/openai works as-is. Here's the whole thing in five minutes.

What It Costs (per 1M tokens)

For a typical assistant, V4 Flash is 96% cheaper than GPT-5.6 Sol on input — and it scores 82.7 on Terminal Bench 2.1, so the quality holds up.

Step 1 — Install and Configure

npm i ai @ai-sdk/openai

Create a .env.local file:

DEEPSEEK_API_KEY=your-tokenpapa-key

Grab the key from tokenpapa.ai — you get $1 free credit on signup, no credit card required.

Step 2 — Create the Route Handler

app/api/chat/route.ts:

import { streamText } from 'ai';
import { createOpenAI } from '@ai-sdk/openai';

const openai = createOpenAI({
  apiKey: process.env.DEEPSEEK_API_KEY,
  baseURL: 'https://tokenpapa.ai/v1', // OpenAI-compatible
});

export async function POST(req: Request) {
  const { messages } = await req.json();
  const result = streamText({
    model: openai('deepseek-v4-flash'),
    messages,
  });
  return result.toDataStreamResponse();
}

That's the entire backend. streamText handles token streaming, cancellation, and errors for you.

Step 3 — Build the Chat UI with useChat

app/page.tsx:

'use client';
import { useChat } from 'ai/react';

export default function Chat() {
  const { messages, input, handleInputChange, handleSubmit } = useChat();

  return (
    <div style={{ maxWidth: 640, margin: '0 auto', padding: 24 }}>
      {messages.map(m => (
        <div key={m.id}>
          <strong>{m.role === 'user' ? 'You' : 'DeepSeek'}:</strong> {m.content}
        </div>
      ))}
      <form onSubmit={handleSubmit}>
        <input
          value={input}
          onChange={handleInputChange}
          placeholder="Ask anything…"
          style={{ width: '80%', padding: 8 }}
        />
        <button type="submit">Send</button>
      </form>
    </div>
  );
}

Run npm run dev and you have a streaming chat. The first token arrives in roughly 0.4s on V4 Flash.

Step 4 — Deploy to Vercel

1. Push the repo to GitHub and import it into Vercel.
2. Add DEEPSEEK_API_KEY to Project → Settings → Environment Variables.
3. Deploy. Done — zero servers to manage.

What It Costs to Run

DeepSeek V4 API pricing per 1M tokens is the whole story here: $0.14 in / $0.42 out on Flash. Concretely:

• $1 free credit ≈ 2,800 requests (~1.5K tokens each) — enough to build and demo an MVP.
• $10 ≈ 20,000+ requests — a real production pilot.
• Context caching cuts repeated input by up to ~90% when users resend long prompts.
• Set max_tokens on long generations — output tokens cost 3x input on Flash.

FAQ

Q: Can I use DeepSeek with Vercel AI SDK? A: Yes — DeepSeek is OpenAI-compatible. Point createOpenAI at https://tokenpapa.ai/v1 and stream with useChat in minutes.

Q: Which DeepSeek model should I use in production? A: Start with deepseek-v4-flash ($0.14/$0.42 per 1M tokens); switch to deepseek-v4-pro ($0.28/$0.84) for harder reasoning tasks.

Q: How much does DeepSeek V4 API cost per 1M tokens? A: DeepSeek V4 Flash is $0.14 input and $0.42 output per 1M tokens — about 100x cheaper than GPT-5.6 Sol. The $1 free credit covers roughly 2,800 requests.

Q: Does streaming work with Vercel AI SDK? A: Yes — streamText returns a data stream response that useChat consumes out of the box; DeepSeek V4 Flash has a time-to-first-token around 0.4s.

Get Started

1. Sign up at tokenpapa.ai — get $1 free credit
2. Create your API key — OpenAI-compatible
3. Ship your chat app — code above, live

The same key also works with the Python SDK if your backend isn't Node:

from openai import OpenAI

client = OpenAI(base_url="https://tokenpapa.ai/v1", api_key="your-key")

stream = client.chat.completions.create(
    model="deepseek-v4-flash",
    messages=[{"role": "user", "content": "Hello!"}],
    stream=True,
)
for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="")