LLM Benchmarks Cheat Sheet

Overview

MMLU, HumanEval, MATH benchmark scores for major models. A comprehensive reference guide for cheat sheets practitioners.

Quick Reference

python
from openai import OpenAI
client = OpenAI()
def solve_llm_benchmarks_cheat_sheet(input_text: str) -> str:
    """MMLU, HumanEval, MATH benchmark scores for major models"""
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {"role":"system","content":"You are an expert in cheat sheets. Topic: LLM Benchmarks Cheat Sheet."},
            {"role":"user","content":input_text}
        ],
        temperature=0.3,
        max_tokens=1000
    )
    return response.choices[0].message.content
Usage
result = solve_llm_benchmarks_cheat_sheet("Your llm benchmarks cheat sheet question")
print(result)

Key Concepts

cheat sheet: Core to this approach

Validation: Always validate inputs and outputs

Error handling: Implement robust retry logic

Monitoring: Track performance and costs

Best Practices

Start with the simplest approach

Measure quality, latency, and cost

Optimize based on real usage patterns

Document decisions and tradeoffs

Review security implications

Related Topics

cheat sheet

reference

benchmarks

openai