Comparing LLMs on Real Code Generation
The article compares the performance of five AI models on a 16-step code generation task, with Claude Sonnet 4.6 as the assumed gold standard.
Why it matters
This comparison of LLMs on a real-world code generation task provides insights into the current capabilities and limitations of these models for practical application.
Key Points
- 1Five AI models were tested on the same 16-action code generation pipeline
- 2Claude Sonnet 4.6 scored the highest at 93.4% of the maximum possible score
- 3Kimi K2.5, Claude Haiku 4.5, and DeepSeek V3.2 scored 67-68% of the maximum
- 4DeepSeek R1 scored the lowest at 26.2% of the maximum
- 5The results have some methodological caveats, and the authors plan to conduct a larger-scale study
Details
The article compares the performance of five AI models on a 16-step code generation task, where each model was given the same template and business requirements and asked to complete a series of actions like applying colors, updating content, and adding technical features. The models spanned a 15x cost range, with Claude Sonnet 4.6 as the assumed gold standard. Sonnet scored the highest at 93.4% of the maximum possible score, while the other models (Kimi K2.5, Claude Haiku 4.5, DeepSeek V3.2, and DeepSeek R1) scored between 26.2% and 68% of the maximum. However, the authors note that the Sonnet score was measured differently, and the rankings of the other models are statistically noisy due to small sample sizes. They plan to conduct a larger-scale study to get more robust results.
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