GPT 4o Mini

GPT-4o mini是OpenAI最新的經濟高效的小型模型，旨在使人工智能更容易獲得和負擔得起。它在文本智能和多模態推理方面表現出色，優於GPT-3.5 Turbo等先前的模型。憑藉128K令牌的上下文窗口以及對文本和視覺的支持，它提供了低成本的實時應用程序，如客戶支持聊天機器人。它的價格為每百萬輸入代幣15美分，每百萬輸出代幣60美分，比其前身便宜得多。通過內置措施和提高對安全威脅的抵抗力來優先考慮安全。

Intelligence(Relatively Weak)

Speed(Relatively Slow)

Input Supported Modalities

Yes

Is Reasoning Model

128,000

Context Window

16,384

Maximum Output Tokens

2023-10-01

Knowledge Cutoff

Pricing

￥1.08 /M tokens

Input

￥4.32 /M tokens

Output

￥1.89 /M tokens

Blended Price

Quick Simple Comparison

GPT-4.1

￥2

GPT-4.1 nano

￥0.1

GPT-4.1 mini

￥0.4

Basic Parameters

GPT-4.1 Technical Parameters

Parameter Count

Not Announced

Context Length

128.00k tokens

Training Data Cutoff

2023-10-01

Open Source Category

Proprietary

Multimodal Support

Text, Image

Throughput

Release Date

2024-07-18

Response Speed

72.6,019 tokens/s

Benchmark Scores

Below is the performance of claude-monet in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.

Intelligence Index

Large Language Model Intelligence Level

3568

Large Language Model Intelligence Level

Coding Index

Indicator of AI model performance on coding tasks

2315

Indicator of AI model performance on coding tasks

Math Index

Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks

MMLU Pro

Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video

64.8

Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video

GPQA

Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions

42.6

Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions

HLE

The model's comprehensive average score on the Hugging Face Open LLM Leaderboard

LiveCodeBench

Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems

23.4

Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems

SciCode

The model's capability in code generation for scientific computing or specific scientific domains

22.9

The model's capability in code generation for scientific computing or specific scientific domains

HumanEval

Score achieved by the AI model on the specific HumanEval benchmark test set

87.6

Score achieved by the AI model on the specific HumanEval benchmark test set

Math 500 Score

Score on the first 500 larger, more well-known mathematical benchmark tests

78.9

Score on the first 500 larger, more well-known mathematical benchmark tests

AIME Score

An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)

11.7

An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)

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