Battle of the AI Code Assistants: Who Writes the Best Python Integration Code?
In today’s tech landscape, AI code assistants are revolutionizing how developers work — but which one actually writes the best code? Instead of relying on marketing claims or anecdotal evidence, we put four leading AI coding assistants to the test in a head-to-head battle. The results might surprise you.
This isn’t just another theoretical comparison. We gave each AI the exact same real-world challenge: build a production-ready Python integration with the DeepL translation API. Then, we had each AI evaluate all four solutions using the same strict criteria a senior developer would apply.
Forget subjective opinions — this is an objective, data-driven showdown where the code speaks for itself.
The Mission: DeepL API Integration
The AI assistants were given an explicit task: create Python code integrating with DeepL’s translation API, with precise guidelines:
- Secure handling of API keys (using environment variables)
- Memory-efficient coding practices
- Adherence to software principles: SOLID, DRY, KISS, and appropriate design patterns
- Comprehensive documentation and structured README
- Modular, reusable, and maintainable code
- Rigorous testing (unit and integration tests separately organized)
- A working
demo.pytranslating "Un Saludos a mis queridos colegas de IOL" into English, Ukrainian, and Italian
The Contenders:
- Amazon Q: Amazon’s coding maestro, known for enterprise-grade solutions.
- Claude Code: Anthropic’s thoughtful coder, blending clarity with precision.
- OpenAI Codex: The powerhouse behind GitHub Copilot, famed for its versatility.
- Plandex AI: The underdog, promising modular and reusable code.
Each was tasked with crafting a Python integration for the DeepL API, adhering to strict requirements: secure API key handling, SOLID principles, modular design, comprehensive tests, and a demo translating “Un Saludos a mis queridos colegas de IOL” into English, Ukrainian, and Italian. The code lives in this GitHub repo. Let’s dive into the evaluation!
The prompt:
Write all the Python code required to integrate with the DeepL API. See the DeepL integration documentation at https://developers.deepl.com/docs.
Ensure that:
1. Your code is as secure as possible.
2. Your code does not create or promote memory leaks.
3. You follow best software‐development practices and patterns — including SOLID principles, DRY, KISS, and relevant design patterns (Gang of Four or newer).
4. All code is properly documented.
5. You include a README.md explaining the repository structure.
6. Your code is modular and reusable, with high cohesion and loose coupling.
7. Your code reads the DEEPL_API_KEY environment variable to obtain the API key.
8. You include unit tests and integration tests for every method you implement.
9. You separate unit tests and integrations tests into multiple files (not one big test file).
10. After completing your implementation, create demo.py that uses your code to translate the following string into English, Ukrainian, and Italian: “Un Saludos a mis queridos colegas de IOL”
MAKE SURE TIO GENERATE THE demo.py file at the end asper the
Evaluation Criteria for AI-generated Python Code
The contenders were judged rigorously on the following metrics:
1. Security (20%)
- Proper handling of API keys (environment variables, no hard-coded secrets).
- Use of secure libraries and methods to prevent injection attacks.
- Adherence to secure coding best practices.
2. Code Quality and Design Principles (25%)
- Adherence to SOLID principles (Single Responsibility, Open-Closed, Liskov Substitution, Interface Segregation, Dependency Inversion).
- DRY (Don’t Repeat Yourself) principle followed to avoid redundancy.
- KISS (Keep It Simple, Stupid) principle ensuring clarity and simplicity.
- Application of appropriate design patterns (Gang of Four or modern ones).
3. Modularity and Reusability (15%)
- Clear separation of concerns and cohesive methods/classes.
- Loose coupling between components for easy maintenance.
- Modular structure facilitating easy integration and reuse.
4. Documentation (15%)
- Code documentation clarity (docstrings, inline comments).
- Comprehensive README.md clearly explaining repository structure and setup instructions.
- Explicit and helpful descriptions for methods, classes, and functionality.
5. Testing (15%)
- Presence of both unit and integration tests.
- Proper separation and organization of test files (multiple files, not a single large test file).
- Coverage and thoroughness of tests ensuring robustness and reliability.
6. Resource Management (Memory and Performance) (10%)
- Code does not contain patterns that could lead to memory leaks.
- Efficient management of resources and performance considerations in code implementation.
The Prompt for evaluating the code
In the current directory, you will find 4 repositories:
DeepL.AmazonQ, created by Amazon Q
DeepL.ClaudeCLI, created by Claude COde
DeepL.Codex, Created by OpenAI Codex
DeepL.Plandex, Created by Plandex AIAll of them solve the same problem: API Integration to DeepL API
Do an exhaustive code review of the 4 repos with the following criteria:
Evaluation Criteria for AI-generated Python Code1. Security (20%)
Proper handling of API keys (environment variables, no hard-coded secrets).
Use of secure libraries and methods to prevent injection attacks.
Adherence to secure coding best practices.
2. Code Quality and Design Principles (25%)
Adherence to SOLID principles (Single Responsibility, Open-Closed, Liskov Substitution, Interface Segregation, Dependency Inversion).
DRY (Don’t Repeat Yourself) principle followed to avoid redundancy.
KISS (Keep It Simple, Stupid) principle ensuring clarity and simplicity.
Application of appropriate design patterns (Gang of Four or modern ones).
3. Modularity and Reusability (15%)
Clear separation of concerns and cohesive methods/classes.
Loose coupling between components for easy maintenance.
Modular structure facilitating easy integration and reuse.
4. Documentation (15%)
Code documentation clarity (docstrings, inline comments).
Comprehensive README.md clearly explaining repository structure and setup instructions.
Explicit and helpful descriptions for methods, classes, and functionality.
5. Testing (15%)
Presence of both unit and integration tests.
Proper separation and organization of test files (multiple files, not a single large test file).
Coverage and thoroughness of tests ensuring robustness and reliability.
6. Resource Management (Memory and Performance) (10%)
Code does not contain patterns that could lead to memory leaks.
Efficient management of resources and performance considerations in code implementation.
Evaluate all the repos based on this criteria and write the report in a markdown file called Evaluation.md
The code generated by the AI assistants
You can find the code generated by the AI assistants in the repo shown below
https://github.com/wjleon/cli-code-assistants-battle.gitResults:
Let’s break down the key highlights for each contender, drawing directly from the AI reviewers’ comments:
🥇 Plandex AI (Average Score: 94.75/100)
Consistently ranked at the top, Plandex impressed the other AIs with its sheer thoroughness.
Strengths (Highlighted by Reviewers):
- “Most comprehensive and robust implementation” (Codex Review). Claude’s review noted it was the “Most comprehensive, secure, and well-engineered solution”.
- Excellent Security: Praised for features like “API key sanitization for logging purposes” (Codex Review) and “Comprehensive security measures including API key validation” using regex (Claude Review).
- Superior Resource Management: Standout features included “Automatic file handle closing”, “Chunking mechanism for large texts”, and “Streaming downloads for efficient document handling” (Codex & Claude Reviews).
- Testing: Described as having the “Most comprehensive test suite with extensive coverage” including edge cases and advanced features like chunking (Codex Review).
- Features: The only one to implement Document Translation and Glossary Management (Plandex Review Feature Comparison).
Weaknesses (Minor, noted by Reviewers):
- Some methods were occasionally flagged as “quite long and could be further decomposed” (Claude & Amazon Q Reviews).
- Documentation, while good, was sometimes rated slightly below Claude’s (e.g., Plandex’s own review).
🥈 Claude Code (Average Score: 93.75/100)
A very close second, Claude shone with its excellent design, clarity, and documentation.
Strengths (Highlighted by Reviewers):
- “Excellent adherence to SOLID principles” and “Clean, modular design” (Amazon Q & Plandex Reviews).
- Code Quality: Use of “Pydantic models for type validation and data conversion” was widely praised (Codex & Claude Reviews).
- Documentation: Consistently rated as having the “best documentation” with “Excellent README with comprehensive examples” and clear explanations of design choices (Amazon Q & Plandex Reviews).
- Resource Management: “Excellent resource management” with context managers, explicit close methods, and robust “retry mechanisms with backoff” (Amazon Q & Plandex Reviews). Scored a perfect 10/10 here from multiple reviewers.
- Modularity: Often rated highest for modularity, “Excellent modularity with clear component separation” (Amazon Q & Plandex Reviews).
Weaknesses (Minor, noted by Reviewers):
- Testing was good but sometimes seen as slightly less comprehensive on edge cases compared to Plandex (Claude Review).
- Lacked the advanced features (document/glossary) implemented by Plandex.
🥉 Amazon Q (Average Score: 89.5/100)
A solid and reliable performer, delivering clean, well-structured code.
Strengths (Highlighted by Reviewers):
- “Clear adherence to SOLID principles” and “Well-structured class hierarchy” (Amazon Q & Plandex Reviews).
- Testing: Praised for “Comprehensive unit tests using pytest” and good separation of unit/integration tests (Codex & Amazon Q Reviews).
- Clean Code: Generally noted for good structure, clean separation of concerns, and helpful utility functions.
- Security: Good basic security practices like using environment variables and HTTPS (All Reviews).
Weaknesses (Noted by Reviewers):
- Lacked more advanced features like robust retry mechanisms found in Claude/Plandex (Codex Review).
- Security was good but missed details like API key sanitization in logs (Claude Review).
- Less comprehensive feature set (no document/glossary translation).
🎗️ OpenAI Codex (Average Score: 66.75/100)
Codex took a distinctly minimalist approach, focusing on core functionality.
Strengths (Highlighted by Reviewers):
- “Minimalist approach focused on core functionality” (Codex Review).
- Simplicity: “Simple, straightforward implementation”; basic adherence to KISS (Amazon Q & Claude Reviews). Easy to understand for basic use cases.
Weaknesses (Noted by Reviewers):
- Lack of Robustness: Consistently flagged for “Limited implementation of SOLID principles”, “minimal abstraction”, and “basic error handling” (Multiple Reviews).
- Security: Criticized for “limited input validation”, basic error handling, and even passing API key in URL params initially (though likely corrected later in dev) (Claude Review). Scored lowest on security.
- Testing: “Limited test coverage”, minimal error/edge case testing (Multiple Reviews).
- Resource Management: “No explicit session management”, no context manager, no retry logic (Codex & Amazon Q Reviews). Scored lowest here.
- Features: Missed several API features like getting supported languages or usage info (Plandex Review Feature Comparison).
The Final Verdict: Who Won the AI Code-Off?
Based on the aggregated scores and the qualitative feedback from the AI judges themselves:
- 🏆 Winner: Plandex AI (94.75/100) — Delivered the most feature-complete, robust, and security-conscious solution, excelling particularly in resource management and testing for complex scenarios.
- 🏅 Runner-Up: Claude Code (93.75/100) — A very close second, standing out for its exceptional code design, clarity, documentation, and strong implementation of best practices like SOLID and resource management.
- 🎖️ Third Place: Amazon Q (89.5/100) — A solid, reliable contender providing well-structured, clean code with good testing, adhering well to core software principles.
- 🎗️ Fourth Place: OpenAI Codex (66.75/100) — Provided a functional but minimal solution, lacking the depth, robustness, security features, and adherence to advanced practices seen in the others. Suitable for very simple tasks or as a starting point.
Conclusion: Lessons from the AI Coding Battle
This head-to-head comparison reveals a fascinating snapshot of the current AI code generation landscape.
- AI Can Deliver Complex Code: All four assistants successfully generated functional DeepL integrations, demonstrating capabilities beyond simple snippets.
- Quality Varies Significantly: The difference between the top contenders (Plandex, Claude) and the minimalist approach (Codex) is vast. Robustness, security, testing, and adherence to design principles are clear differentiators.
- Strengths Emerge: Plandex excelled in comprehensiveness and handling complexity (like large texts). Claude shone in design clarity, documentation, and best-practice implementation (like retries). Amazon Q offered solid, dependable structure. Codex prioritized simplicity.
- Beyond Functionality: For production use, aspects like security hardening (sanitization, validation), resource management (session handling, retries), comprehensive testing, and clear design patterns are crucial — areas where the top AIs significantly outperformed.
- The “Best” Depends on Needs: While Plandex and Claude produced arguably superior code for a production scenario, Codex’s simplicity might be sufficient (or even preferred) for a quick script or learning exercise.
The era of AI merely assisting developers is rapidly evolving. As these tools mature, they become capable of generating entire modules and integrations. However, as this battle shows, critical evaluation, understanding the nuances of different AI approaches, and knowing when to trust (and when to refine) the output remains paramount. The future of development isn’t just about AI writing code; it’s about developers and AI collaborating to build better, more robust software, faster.
What are your thoughts? Which AI would you trust for your next project?
Explore the code and the full AI evaluations yourself:
https://github.com/wjleon/cli-code-assistants-battle.git
