How to Use DeepSeek V4 Pro in VSCode with KiloCode

KiloCode is a VSCode extension that brings AI coding assistance directly into your editor. Combined with DeepSeek V4 Pro through AIWave's API, you get a powerful coding setup at a fraction of the cost of GitHub Copilot. Here's how to set it up end-to-end.

Prerequisites

Step 1: Get Your AIWave API Key

2. Navigate to the dashboard and generate an API key.

3. Copy the key — you'll need it in the next step.

Step 2: Configure KiloCode

Open VSCode and go to Settings → Extensions → KiloCode (or search @ext kilocode in settings). Configure these fields:

In VSCode's settings.json, this looks like:

{
  "kilocode.apiBaseUrl": "https://aiwave.live/v1",
  "kilocode.apiKey": "sk-your-key-here",
  "kilocode.model": "deepseek-v4-pro"
}

Step 3: Verify the Connection

Open any project in VSCode, press Ctrl+Shift+P (or Cmd+Shift+P on macOS), and run the "KiloCode: Test Connection" command. You should see a success message confirming the API is reachable.

Step 4: Start Coding

Once configured, KiloCode works just like any AI coding assistant:

Model Recommendations by Task

Not every task needs DeepSeek V4 Pro. Here's a practical guide based on current AIWave pricing:

Pro tip: Set KiloCode's default model to deepseek-v4-pro for quality, but create a separate profile with deepseek-v4-flash for high-volume autocomplete — it's significantly cheaper and nearly as good for short suggestions.

Real Example: Generating a FastAPI Endpoint

With KiloCode's chat panel open and deepseek-v4-pro selected, try:

"Create a FastAPI endpoint that accepts a PDF upload, extracts text using pdfplumber, and returns a JSON summary with page count and word count."

The model will generate something like this:

from fastapi import FastAPI, UploadFile, File, HTTPException
import pdfplumber
import tempfile
import os

app = FastAPI()

@app.post("/summarize-pdf")
async def summarize_pdf(file: UploadFile = File(...)):
    if not file.filename.endswith(".pdf"):
        raise HTTPException(400, "Only PDF files are accepted")

    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
        content = await file.read()
        tmp.write(content)
        tmp_path = tmp.name

    try:
        with pdfplumber.open(tmp_path) as pdf:
            pages = []
            total_words = 0
            for page in pdf.pages:
                text = page.extract_text() or ""
                pages.append(text)
                total_words += len(text.split())
        return {
            "filename": file.filename,
            "page_count": len(pages),
            "total_words": total_words,
            "preview": pages[0][:500] if pages else ""
        }
    finally:
        os.unlink(tmp_path)

Switch the model to deepseek-v4-flash for the same prompt and you'll get comparable output at lower cost. For complex multi-file refactors though, stick with deepseek-v4-pro.

Troubleshooting

Switching Models at Runtime

You can also set the model per-request by adding a comment directive in KiloCode:

@model deepseek-v4-flash
Explain this function briefly.

This lets you use cheaper models for simple questions and reserve DeepSeek V4 Pro for the hard problems.

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DeepSeek V4 API pricing · ERNIE API pricing