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7 leading LLMs over 7 challenges

  • Writer: Federico Carrasco
    Federico Carrasco
  • Jul 29
  • 2 min read

Updated: Jul 31


Over the past few days, I stress‑tested the world’s top LLMs using real engineering and business tasks, not trivia or creative prompts. I focused on the work I actually want to increase productivity, putting seven leading AI models through a rigorous seven‑challenge gauntlet.


  1. Python Code Generation, Clean, production-ready scripts under pressure

  2. CSS/HTML Debugging, Live rendering fixes, not just code dumps

  3. JavaScript Troubleshooting, Async bugs, memory leaks, and edge cases

  4. Unix Shell Scripting, One-liners and automation that actually work

  5. Legal Document Proofing, Spotting liability gaps and clause ambiguities

  6. 350-Page Document Synthesis, Retaining arguments, data, and nuance at scale

  7. Spreadsheet Data Analysis, Finding correlations others miss in raw data


Here is how Kimi, Claude, ChatGPT, Gemini, Copilot, Deepseek, and Grok performed:


🥇 1st Place: Kimi AI (The Undisputed Winner)

Kimi delivered astonishing performance across every single challenge:

  • CSS/HTML Debugging: Stole the show by providing an interactive on-screen visual simulation of the corrected layout.

  • Python Code Creation: Generated remarkably clean, brief, and elegant code with zero fluff.

  • Document Work: Mastered the 350-page document summary without losing context and caught subtle compliance errors during legal proofing.


🥈 2nd Place: Claude

Claude took a close second. It dominated JavaScript debugging with surgical precision and wrote robust, production-ready Unix scripts. Its legal proofing was exceptional, though it lacked Kimi’s interactive front-end execution capabilities.


🥉 3rd Place (Tie): ChatGPT, Gemini, Copilot & Deepseek

A fierce toe-to-toe battle where individual strengths emerged:

  • Gemini: Excelled at Spreadsheet data analysis and handled the 350-page context window smoothly.

  • ChatGPT & Copilot: Reliable all-rounders for JS debugging and Python creation, though slightly rigid on layout fixes.

  • Deepseek: Impressed with ultra-efficient Unix coding logic, but fell behind on complex legal document proofing.


📉 7th Place: Grok

Grok anchored the bottom of the list. It struggled with bloated Python syntax, missed edge cases in Unix scripting, and lost structural accuracy during long-document summarization.


📌 Takeaway: If you haven't tested Kimi's visual debugging or long-context reasoning yet, you're missing out on a serious productivity shift.


This was only Round One. Next up: text creation and image generation.

Stay tuned, the next results might surprise you even more. 🔥

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