Author

Tony D

Published

March 18, 2025

An overview of Large Language Models (LLMs) and their performance across various benchmarks, including math, code, English, and science.

This document provides a comprehensive overview of Large Language Models (LLMs) and their performance across a variety of benchmarks. It categorizes the performance of different models by subject area, including mathematics, coding, English language understanding, and science. The document also provides links to resources where you can compare the performance of different LLM models online.

(LLM)Large language model

Code

library(tidyverse)
library(openxlsx)
library(readxl)

Code

data001=read_excel('AI model.xlsx')
head(data001)

LLM model performance

math

AIME

https://en.wikipedia.org/wiki/American_Invitational_Mathematics_Examination

MATH-500

Code

Codeforces

LiveCodeBench

English

MMLU

Measuring Massive Multitask Language Understanding (MMLU)

https://en.wikipedia.org/wiki/MMLU

Science

GPQA-Diamond

Graduate-Level Google-Proof Q&A

Description: GPQA consists of 448 multiple-choice questions meticulously crafted by domain experts in biology, physics, and chemistry. These questions are intentionally designed to be high-quality and extremely difficult.

Expert Accuracy: Even experts who hold or are pursuing PhDs in the corresponding domains achieve only 65% accuracy on these questions (or 74% when excluding clear mistakes identified in retrospect).

Google-Proof: The questions are “Google-proof,” meaning that even with unrestricted access to the web, highly skilled non-expert validators only reach an accuracy of 34% despite spending over 30 minutes searching for answers.

AI Systems Difficulty: State-of-the-art AI systems, including our strongest GPT-4 based baseline, achieve only 39% accuracy on this challenging dataset.

SimpleBench

https://simple-bench.com/

# Compare online

https://lmarena.ai/

---
title: "（LLM）大语言模型"
subtitle: "(LLM)Large language model"
author: "Tony D"

date: "2025-03-18"

categories: 
  - Tool
  - R
  - Python 

  
execute:
  warning: false
  error: false
  eval: false

image: '1734616066-llm-security.webp'
---

An overview of Large Language Models (LLMs) and their performance across various benchmarks, including math, code, English, and science.

This document provides a comprehensive overview of Large Language Models (LLMs) and their performance across a variety of benchmarks. It categorizes the performance of different models by subject area, including mathematics, coding, English language understanding, and science. The document also provides links to resources where you can compare the performance of different LLM models online.

(LLM)Large language model

![](images/my%20screenshots.png){width="800"} 


```{r}
library(tidyverse)
library(openxlsx)
library(readxl)
```

```{r}
data001=read_excel('AI model.xlsx')
head(data001)
```

# LLM model performance

## math

### AIME

https://en.wikipedia.org/wiki/American_Invitational_Mathematics_Examination

### MATH-500

## Code

## Codeforces

## LiveCodeBench

## English

### MMLU

Measuring Massive Multitask Language Understanding (MMLU)

https://en.wikipedia.org/wiki/MMLU

## Science

### GPQA-Diamond

Graduate-Level Google-Proof Q&A

Description: GPQA consists of 448 multiple-choice questions meticulously crafted by domain experts in biology, physics, and chemistry. These questions are intentionally designed to be high-quality and extremely difficult.

Expert Accuracy: Even experts who hold or are pursuing PhDs in the corresponding domains achieve only 65% accuracy on these questions (or 74% when excluding clear mistakes identified in retrospect).

Google-Proof: The questions are "Google-proof," meaning that even with unrestricted access to the web, highly skilled non-expert validators only reach an accuracy of 34% despite spending over 30 minutes searching for answers.

AI Systems Difficulty: State-of-the-art AI systems, including our strongest GPT-4 based baseline, achieve only 39% accuracy on this challenging dataset.

# SimpleBench

https://simple-bench.com/

![](images/clipboard-1311795434.png)
# Compare online


https://lmarena.ai/