Best LLM Models for Coding Tasks: A Comparative Analysis

说明与用途

自动化脚本比较四个 Oxlo.ai LLM，生成电话规范化函数代码并通过单元测试验证。

问题

Choosing the most reliable LLM for code generation without manual guessing.

结果

All four tested Oxlo.ai models passed all six unit tests for the phone‑normalization function, demonstrating consistent performance across models.

工作流

• Set up OpenAI‑compatible client pointing to Oxlo.ai
• Define system and user prompts for a coding task
• Create a runner that sends requests and records latency
• Extract Python code from markdown fences
• Run predefined unit tests against each model’s output
• Collect and display PASS/FAIL statistics

证据

$ python compare_models.py Running deepseek-v3.2 ... PASS=6 FAIL=0 Running qwen-3-32b ... PASS=6 FAIL=0 Running llama-3.3-70b ... PASS=6 FAIL=0 Running kimi-k2.6 ... PASS=6 FAIL=0

来源