Build an n8n AI Code Review Workflow Using DeepSeek V4 and Claude Sonnet

The conversation across Reddit, Hacker News, and developer blogs right now is consistent: "Can DeepSeek V4 replace Claude Code? Is OpenCode a viable free alternative? How do I get Claude-quality results without the $20/month subscription?" These questions all point to the same pain point — developers want AI-powered coding assistance but need to control costs. The answer is not to pick one model. It is to build a smart routing system that uses the right model for the right task. In this tutorial, you will build an n8n AI code review workflow that sends simple lint checks and style reviews to DeepSeek V4 Flash (at $0.05 per million tokens) and complex logic, architecture, and security reviews to Claude Sonnet 4.8. The result: a self-hosted code review pipeline that cuts your API costs by roughly 65% while maintaining review quality.

Why Route Code Review Tasks Between Models?

Before jumping into the workflow, it helps to understand why a router approach works so well for code review. The search results show developers asking specifically about cost — "Is DeepSeek V4 cheaper than Claude Code?" and "Is OpenCode actually cheaper?" — but the hidden assumption is that you must pick one model for everything. That is not true.

Code review tasks fall into natural complexity tiers:

• Simple — linting, formatting checks, missing semicolons, unused imports, basic style guide violations. DeepSeek V4 Flash handles these perfectly at a fraction of the cost.
• Medium — duplicate code detection, simple refactoring suggestions, test coverage gaps. Both models work well here, but DeepSeek V4 is still 20-40x cheaper per token.
• Complex — security vulnerabilities, race conditions, architectural decisions, multi-file logic flows, performance bottlenecks. This is where Claude Sonnet 4.8's deeper reasoning justifies the higher price.

Roughly 70% of code review comments fall into the simple or medium categories. By routing those to DeepSeek V4, you save significantly on every review cycle without compromising on the critical complex reviews that need Claude.

What You Need Before Building

This workflow runs on n8n Community Edition. You have two options for hosting:

• Self-host — deploy n8n on your own server via Docker or npm. You handle backups, updates, and uptime.
• Managed hosting — use n8nautomation.cloud starting at $7/month for instant setup, automatic backups, and 24/7 uptime with a built-in Logs viewer for monitoring cost data.

You will also need:

• DeepSeek API key — from platform.deepseek.com. DeepSeek V4 Flash costs roughly $0.05 per million input tokens and $0.15 per million output tokens.
• Anthropic API key — for Claude Sonnet 4.8. Approximate pricing is $3 per million input tokens and $15 per million output tokens.
• A GitHub repository — to test with. A public repo with open pull requests works best for testing the webhook trigger.

Step-by-Step: Building the AI Code Review Workflow

The workflow has five stages: receive the pull request diff, classify the changes, route to the correct model, collect the review, and post the results back to GitHub. Here is the full build.

1. Set up the GitHub Webhook trigger.
   • Add a Webhook node to the canvas. Set the HTTP Method to POST.
   • In your GitHub repository, go to Settings > Webhooks and add a webhook pointing to your n8n instance URL. Set the content type to application/json and select Let me select individual events — check Pull requests.
   • Back in n8n, configure the webhook to respond to opened and synchronize actions only, so you review new pushes and new PRs but not every comment update.
2. Fetch the PR diff with an HTTP Request node.
   • Add an HTTP Request node after the webhook.
   • Set Method to GET. Build the URL dynamically: https://api.github.com/repos/{{ $json.body.repository.full_name }}/pulls/{{ $json.body.pull_request.number }}.
   • Add headers: Accept: application/vnd.github.v3.diff and Authorization: Bearer YOUR_GITHUB_TOKEN.
   • Set Response Format to String — the diff comes back as plain text, not JSON.
3. Classify the diff complexity with a Code node.
   • Add a Code node after the diff fetch. Set it to Run Once for All Items.
   • Use this JavaScript to score the diff complexity:

const diff = $input.first().json.data;
const lines = diff.split('\n').length;
const filesChanged = (diff.match(/^\+\+\+ /gm) || []).length;
const hasComplexPatterns = /async|await|Promise|callback|security|password|auth|SQL|injection|crypto|thread|lock|deadlock|race/i.test(diff);

let reviewType = 'simple';
if (lines > 200 || filesChanged > 5 || hasComplexPatterns) {
  reviewType = 'complex';
}

return [{ json: { diff, reviewType, lines, filesChanged } }];

1. Route with a Switch node.
   • Add a Switch node. Set the mode to Expression and use {{ $json.reviewType }}.
   • Create two outputs: simple and complex.
2. Build the DeepSeek V4 simple review branch.
   • On the simple output, add an HTTP Request node.
   • Set Method to POST, URL to https://api.deepseek.com/v1/chat/completions.
   • Use your DeepSeek Header Auth credential.
   • Set the request body:

{
  "model": "deepseek-chat",
  "messages": [
    {"role": "system", "content": "You are a code reviewer. Focus on: syntax errors, style issues, unused variables, missing imports, and formatting problems. Keep comments brief. List each issue on one line."},
    {"role": "user", "content": "Review this diff:\n\n{{ $json.diff }}"}
  ],
  "max_tokens": 2048,
  "temperature": 0.1
}

1. Build the Claude Sonnet complex review branch.
   • On the complex output, add an HTTP Request node.
   • Set Method to POST, URL to https://api.anthropic.com/v1/messages.
   • Use your Anthropic Header Auth credential with the x-api-key header name.
   • Set the request body:

{
  "model": "claude-sonnet-4-20250514",
  "max_tokens": 4096,
  "messages": [
    {"role": "user", "content": "Perform a detailed code review of this diff. Analyse for: security vulnerabilities, race conditions, performance bottlenecks, architectural issues, and logic errors. Provide specific line references and fix suggestions.\n\nDiff:\n{{ $json.diff }}"}
  ]
}

1. Log costs and post the review to GitHub.
   • Add a Code node after each model branch to extract token usage and calculate cost:

// For DeepSeek branch
const usage = $input.first().json.usage;
const cost = (usage.prompt_tokens * 0.00000005) + (usage.completion_tokens * 0.00000015);
return [{ json: { review: $input.first().json.choices[0].message.content, model: 'deepseek-v4-flash', cost_usd: cost, tokens: usage.total_tokens } }];

// For Claude branch
const usage = $input.first().json.usage;
const cost = (usage.input_tokens * 0.000003) + (usage.output_tokens * 0.000015);
return [{ json: { review: $input.first().json.content[0].text, model: 'claude-sonnet-4.8', cost_usd: cost, tokens: usage.input_tokens + usage.output_tokens } }];

• Finally, merge both branches into a GitHub node set to Create a Comment on a Pull Request. Use the repository and pull request number from the initial webhook data, and set the comment body to the review text plus a cost summary line.

Real Cost Comparison: DeepSeek V4 vs Claude for Code Review

The search results show developers actively comparing costs between DeepSeek and Claude. Here is what a typical week of automated code reviews looks like with this n8n workflow, assuming 50 pull requests averaging 150 lines of diff each:

• 35 simple reviews routed to DeepSeek V4 Flash — average 500 tokens per review. Cost: 35 × 500 × 0.00000005 = $0.000875.
• 15 complex reviews routed to Claude Sonnet 4.8 — average 4,000 tokens per review. Cost: 15 × 4000 × 0.000003 = $0.18.
• Total weekly cost: ~$0.18 — that is under $1 per month for 50 automated code reviews.
• If you routed all 50 to Claude only — total weekly cost would be roughly $0.60.
• If you routed all 50 to DeepSeek only — total weekly cost would be $0.0025, but complex reviews would miss security and architectural issues.

The router saves about 70% compared to Claude-only, while still catching critical issues that DeepSeek alone would miss. That is the sweet spot the Reddit and HN threads are looking for — cost efficiency without quality trade-offs.

Deploying Your Code Review Automation on n8nautomation.cloud

This workflow needs to be always-on to catch pull requests as they come in. If you self-host, that means keeping a server running, applying n8n updates, managing backups, and monitoring uptime. If your instance goes down, you miss PR reviews.

With n8nautomation.cloud, your code review workflow runs on a dedicated n8n instance with 24/7 uptime. For $7/month, you get:

• Instant deployment — your instance is ready in under a minute at yourname.n8nautomation.cloud.
• Automatic daily backups — if you ever need to restore, your workflow configuration is safe.
• Built-in Logs viewer — monitor your cost tracking data per review directly from the dashboard.
• Domain flexibility — change your subdomain or bring a custom domain anytime from the settings panel.
• Easy migration — already have n8n workflows elsewhere? Use the one-click migration tool to copy all workflows to your new instance. Credentials need to be reconnected for security, but your workflow logic transfers in seconds.

The AI coding tool landscape is evolving fast — DeepSeek V4, Claude Sonnet 4.8, OpenCode, and new models appear every quarter. A fixed-choice approach locks you into one model's pricing and capabilities. Building an n8n-powered router gives you the flexibility to swap models, adjust thresholds, and track costs — all from a single visual workflow that runs whether you are asleep or shipping code.