DRQ Benchmark
Multi-Provider LLM Core War Arena
Inside the project
01 / 01DRQ Benchmark
The brief
A problem worth
building around.
Evaluating LLM code generation requires controlled benchmarks with measurable outcomes. The original DRQ (Digital Red Queen) research showed convergent evolution in LLM-generated programs, but single-provider evaluation limits insights. Building a fair multi-model battle arena requires consistent prompting, parallel generation, and deterministic battle simulation.
Our approach
DRQ Benchmark extends the original research with multi-provider LLM support across leading models. Warriors generated by different models compete in Core War, with parallel generation significantly reducing benchmark time.
The experience
What it lets
people do.
The capabilities that turn the underlying engineering into a usable product.
- 01
Multi-provider LLM battles
- 02
Real-time benchmark monitoring
- 03
Pygame visualizer with color-coded warriors
- 04
Parallel warrior generation
- 05
Warrior code inspection
- 06
Battle history tracking
- 07
Score tracking with win rates
- 08
Docker containerization
Project record
What came out of it.
Multi-provider LLM support across leading models
Real-time web monitoring interface
Pygame battle visualization
Significantly faster with parallel warrior generation
Player vs Player mode (any model combination)
Battle history with localStorage persistence
Under the hood
Multi-provider LLM battle arena for adversarial program evolution research
Multi-provider LLM battle arena for adversarial program evolution research
Facing Similar Challenges?
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