Grok vs Perplexity for Coding

Grok is the stronger choice for coding tasks, offering superior math and reasoning capabilities with cheaper pricing ($8/month vs. $20/month), though it lacks code execution features. Perplexity's strength lies in research and fact-checking rather than practical coding work, making it better suited for learning concepts than writing code. Neither offers built-in code execution, so both require external testing environments, but Grok's reasoning prowess gives it an edge for debugging and algorithmic problem-solving.

Head-to-Head for Coding

Algorithm Development

Grok

Grok

Strong math/reasoning capabilities

Perplexity

Search-focused, less optimized

Bug Diagnosis & Solutions

Grok

Grok

Deep reasoning for debugging

Perplexity

Web search with citations

API & Documentation Research

Perplexity

Grok

Real-time web search

Perplexity

Real-time search with citations

Learning New Frameworks

Tie

Grok

Reasoning-based explanations

Perplexity

Cited references for credibility

See Them Answer a Coding Question

We asked both models the same coding-focused prompt.

β€œBuild a Python script that scrapes a website's sitemap, extracts all URLs, and checks each one for broken links, outputting results as a formatted CSV report.”

Grok 4.1 Fast

Build a Python script that scrapes a website's sitemap, extracts all URLs, and checks each one for broken links, outputting results as a formatted CSV report.

#!/usr/bin/env python3
"""
Broken Link Checker Script
==========================

This script:
1. Discovers sitemaps from robots.txt or common locations.
2. Parses sitemap(s) recursively to extract page URLs (limited to same domain).
3. For each page (up to --max-pages), fetches the page and extracts all <a href> links.
4. Checks each link (HEAD request, internal only by default) for broken status (>=400 or error).
5. Outputs ONLY broken links (including broken pages themselves) to a CSV report.

Usage:
    python broken_link_checker.py https://example.com --max-pages 50 --output report.csv

Dependencies:
    pip install requests beautifulsoup4 lxml

Notes:
- Sequential checking to respect servers (use --delay for slower sites).
- Filters out mailto:, tel:, javascript:, #, non-http(s) links.
- HEAD requests for speed; falls back to reporting errors.
- Limited to --max-pages to avoid overload.
- Add --check-external to include external links (slower).
"""

import argparse
import csv
import time
import xml.etree.ElementTree as ET
from datetime import datetime
from urllib.parse import urljoin, urlparse
import requests
from bs4 import BeautifulSoup


def get_sitemaps(base_url):
    """Fetch sitemaps from robots.txt or fallback locations."""
    sitemaps = []
    try:
        robots_url = urljoin(base_url, '/robots.txt')
        r = requests.get(robots_url, timeout=10)
        r.raise_for_status()
        for line in r.text.splitlines():
            if line.lower().startswith('sitemap:'):
                sitemap_url = line.split(':', 1)[1].strip()
                sitemaps.append(sitemap_url)
    except Exception:
        pass

    if not sitemaps:
        # Common fallback locations
        sitemaps = [
            urljoin(base_url, '/sitemap.xml'),
            urljoin(base_url, '/sitemap_index.xml'),
            urljoin(base_url, '/sitemap-index.xml')
        ]
    return sitemaps


def parse_sitemap(url):
    """Recursively parse sitemap XML and return list of page URLs."""
    try:
        r = requests.get(url, timeout=10)
        r.raise_for_status()
        root = ET.fromstring(r.content)
        ns = {'s': 'http://www.sitemaps.org/schemas/sitemap/0.9'}
        urls = []

        # Find all <loc> elements (works for both urlset and sitemapindex)
        for loc_elem in root.findall('.//s:loc', ns):
            loc_url = loc_elem.text.strip()
            if loc_url:
                # Recurse if it's a sitemapindex entry (contains <sitemap>)
                sub_root = None
                try:
                    sub_r = requests.get(loc_url, timeout=10)
                    sub_root = ET.fromstring(sub_r.content)
                except Exception:
                    pass
                if sub_root and sub_root.find('.//s:sitemap', ns) is not None:
                    urls.extend(parse_sitemap(loc_url))
                else:
                    urls.append(loc_url)

        return urls
    except Exception as e:
        print(f"Error parsing sitemap {url}: {e}")
        return []


def check_link(link):
    """Check a single link with HEAD request."""
    try:
        start_time = time.time()
        resp = requests.head(link, timeout=10, allow_redirects=True,
                             headers={'User-Agent': 'Mozilla/5.0 (compatible; LinkChecker/1.0)'})
        elapsed_ms = (time.time() - start_time) * 1000
        return resp.status_code, elapsed_ms, None
    except Exception as e:
        return 'ERROR', 0, str(e)


def check_page(page_url, base_domain, check_external=False):
    """Check a single page for broken links. Returns list of broken [source, link, status, time, timestamp, error]."""
    broken = []
    headers = {'User-Agent': 'Mozilla/5.0 (compatible; LinkChecker/1.0)'}
    try:
        page_resp = requests.get(page_url, timeout=10, headers=headers)
        timestamp = datetime.now().isoformat()
        if page_resp.status_code >= 400:
            broken.append([page_url, page_url, page_resp.status_code,
                           page_resp.elapsed.total_seconds() * 1000, timestamp, None])
            return broken

        soup = BeautifulSoup(page_resp.content, 'html.parser')
        links = set()
        for a in soup.find_all('a', href=True):
            href = a['href'].strip()
            if not href or href.startswith(('#', 'mailto:', 'tel:', 'javascript:')):
                continue
            full_url = urljoin(page_url, href)
            parsed = urlparse(full_url)
            if parsed.scheme not in ('http', 'https'):
                continue
            if not check_external and parsed.netloc != base_domain:
                continue
            links.add(full_url)

        for link in links:
            status, elapsed_ms, error = check_link(link)
            row_timestamp = datetime.now().isoformat()
            if status == 'ERROR' or (isinstance(status, int) and status >= 400):
                broken.append([page_url, link, status, elapsed_ms, row_timestamp, error])

        return broken
    except Exception as e:
        timestamp = datetime.now().isoformat()
        broken.append([page_url, page_url, 'ERROR_PAGE', 0, timestamp, str(e)])
        return broken


def main():
    parser = argparse.ArgumentParser(description='Check website sitemap for broken links.')
    parser.add_argument('url', help='Base website URL (e.g., https://example.com)')
    parser.add_argument('--output', '-o', default='broken_links_report.csv',
                        help='Output CSV file (default: broken_links_report.csv)')
    parser.add_argument('--max-pages', '-m', type=int, default=100,
                        help='Max pages to check (default: 100)')
    parser.add_argument('--delay', type=float, default=0.5,
                        help='Delay between requests in seconds (default: 0.5)')
    parser.add_argument('--check-external', '-e', action='store_true',
                        help='Check external links too (default: internal only)')
    args = parser.parse_args()

    base_url = args.url.rstrip('/')
    base_parsed = urlparse(base_url)
    base_domain = base_parsed.netloc

    print(f"Discovering sitemaps for {base_url}...")
    sitemaps = get_sitemaps(base_url)

    all_page_urls = []
    for sitemap in sitemaps:
        print(f"Parsing sitemap: {sitemap}")
        all_page_urls.extend(parse_sitemap(sitemap))

    # Filter to same domain and unique
    page_urls = list(set(
        u for u in all_page_urls
        if u and urlparse(u).netloc == base_domain
    ))[:args.max_pages]

    print(f"Found {len(page_urls)} unique internal pages to check (limited to {args.max_pages}).")

    results = []
    total_pages = len(page_urls)
    for i, page_url in enumerate(page_urls, 1):
        print(f"[{i}/{total_pages}] Checking: {page_url}")
        broken = check_page(page_url, base_domain, args.check_external)
        results.extend(broken)
        time.sleep(args.delay)

    # Write CSV report
    with open(args.output, 'w', newline='', encoding='utf-8') as f:
        if results:
            writer = csv.writer(f)
            writer.writerow(['Source_Page', 'Link_URL', 'Status_Code', 'Response_Time_ms', 'Timestamp', 'Error'])
            writer.writerows(results)
            print(f"\nReport saved: {args.output} ({len(results)} broken links found)")
        else:
            print(f"\nNo broken links found. Empty report: {args.output}")

    print("Done!")


if __name__ == '__main__':
    main()

Features & Usage Notes

  • Robust sitemap discovery: Checks robots.txt first, falls back to /sitemap.xml etc.
  • Recursive sitemap parsing: Handles sitemap indexes automatically.
  • Efficient link checking: Unique links per page, HEAD requests (fast), filters junk protocols.
  • Internal by default: --check-external to include outbound links.
  • Rate limiting: --delay prevents bans.
  • Limits: --max-pages avoids overload (e.g., huge sites).
  • CSV Output: Only broken items (pages/links with 4xx/5xx/ERROR), with timings/timestamps.
  • Error resilient: Continues on failures, logs errors.
  • Example:
    python broken_link_checker.py https://example.com --max-pages 20 --delay 1.0 --output my_report.csv
    
  • Customization: Edit check_link() for GET fallback if HEAD fails often.
  • Performance: Sequential for politeness; for speed, replace loop with concurrent.futures.ThreadPoolExecutor(max_workers=5) (but monitor server response).

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Detailed Breakdown

When it comes to coding tasks, Grok holds a clear advantage over Perplexity. While neither tool was purpose-built as a coding assistant, Grok's underlying reasoning capabilities make it a far more practical choice for developers than Perplexity's search-first architecture.

Grok's strength in coding stems from its strong general reasoning model. With benchmark scores of 85.4% on MMLU Pro and 85.3% on GPQA Diamond, the model demonstrates the kind of logical and technical reasoning that translates well to writing, debugging, and explaining code. In practice, Grok handles tasks like generating boilerplate, refactoring functions, explaining error messages, and walking through algorithmic logic with solid competence. Its 128K context window also means you can paste in larger files or multi-file snippets without hitting limits too quickly. The extended thinking feature is particularly useful for tackling complex problems β€” asking Grok to reason through a tricky recursive function or a database query optimization benefits from that extra step-by-step processing.

Perplexity, by contrast, is fundamentally a research and search tool. Its core value proposition is sourced answers from the web, and while that's genuinely useful for looking up documentation, finding Stack Overflow threads, or checking library version compatibility, it doesn't make Perplexity good at actually writing or reasoning through code. Asking Perplexity to debug a function or architect a small feature often produces answers that feel like summarized search results rather than thoughtful engineering responses. The absence of code execution is a limitation both tools share, but it hurts Perplexity more because its alternative β€” real-time web retrieval β€” adds less value in pure coding scenarios than Grok's reasoning depth does.

Where Perplexity does earn its place in a developer's workflow is as a research companion. When you need to quickly compare two frameworks, find the latest changelog for a dependency, or verify whether a specific API endpoint still works the same way, Perplexity's cited, real-time search results are genuinely faster and more trustworthy than manually browsing docs. Think of it as a smarter search bar, not a coding assistant.

For pricing, Grok is also the more accessible option β€” available through an X Premium subscription starting at $8/month, compared to Perplexity Pro at $20/month.

Recommendation: Choose Grok for hands-on coding work β€” writing functions, debugging, code review, and technical problem-solving. Use Perplexity as a supplementary research tool when you need sourced, up-to-date documentation lookups. For most developers, Grok will be the primary tool in this pairing.

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