Like a lot of developers experimenting with large language models (LLMs), I ran into a common problem:
I wasn’t sure which LLM to use for code generation.
Some models seemed to excel at performance, others at documentation. Some were more secure, others more adaptable. But evaluating them side-by-side was tedious. Copy-pasting outputs into an editor and squinting at differences didn’t give me real insight.
So I built FirstPassModelCompare — a modular comparison tool that helps you quickly benchmark and visualize how different LLMs handle the same coding task.
Why Compare LLMs at All?
It’s easy to assume “better model = better code,” but in reality, different LLMs have different strengths.
- Some nail requirements traceability but stumble on performance.
- Some generate beautifully readable code that’s insecure under the hood.
- Others prioritize adaptability or documentation.
If you’re choosing a model for a project (or just want to know if the hype matches your needs), you need more than gut feel. You need a structured way to compare.
What the Tool Does
FirstPassModelCompare evaluates up to four LLM outputs at once, scoring them across seven dimensions:
- Requirements Traceability
- Performance (not run-time)
- Readability
- Security
- Adaptability
- Code Quality
- Documentation
The tool uses a plugin-based architecture so you can extend or tweak the scoring. Want to add a test framework check? Or runtime benchmarking? Just drop in a new analyzer.
Results aren’t just numbers on a screen. You get:
- An interactive HTML dashboard with sliders to adjust weights in real time.
- Preset weighting schemes like Balanced, Security First, or Performance Focus.
- Exportable Markdown reports, CSV summaries, and JSON data for your own analysis.
How It Works
- Collect Outputs
Save each model’s code response intollm1,llm2, etc. (You can include documentation if the model provided it.) Keep the mapping written somewhere (the tool is agnostic if LLM1 GPT5 and LLM2 is Claude). - Configure Weights
Use a preset scheme or adjust your own scoring balance. - Run the Analyzer
Fire up the tool with:python modular_analyzer.py - Explore Results
Check the interactive dashboard. See which model wins overall, or dive into individual categories to understand tradeoffs.
A Quick Example
Here’s a snapshot from one of my test runs:
| Rank | LLM | Score | Key Strengths |
|---|---|---|---|
| 1 | LLM1 | 83.6 | Strong in security & requirements |
| 2 | LLM4 | 83.1 | Great code quality & documentation |
| 3 | LLM3 | 81.9 | Readability & documentation focused |
| 4 | LLM2 | 75.9 | Traceability but weaker elsewhere |
Notice how close LLM1 and LLM4 are. With a “Performance First” preset, the ranking actually flipped. That’s the power of adjusting weights — you can match model choice to your real priorities.
Why It Matters
Choosing the “best” LLM isn’t about a single score. It’s about finding the right tool for the job.
- If you’re in healthcare or finance, you might weigh security above all else.
- If you’re teaching with LLMs, readability and documentation matter more.
- If you’re building a startup MVP, maybe it’s all about performance and adaptability.
This tool makes those tradeoffs explicit.
Where It’s Headed
The current version is just a starting point. Next, I’d love to see:
- Runtime benchmarking (execution speed, memory usage).
- Unit test integration for correctness.
- Deeper static analysis and style linting.
Try It Yourself
Check out the repo: FirstPassModelCompare.
Clone it, drop in some model outputs, and see how your favorite LLMs stack up. And if you build a new analyzer or preset, send a PR — I’d love to see what others value when comparing code generators.
👉 For me, this project started with a simple question: Which LLM should I use?
Now it’s a way to explore not just which is best overall, but which is best for what you care about most.
Ctrl Alt Dad 
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