Artificial Analysis AI 趋势

Name: 主要科技公司的资本支出变化
Creator: Artificial Analysis
License: https://artificialanalysis.ai/docs/legal/Terms-of-Use.pdf

分析 AI 的当前状况和推动其进步的主要趋势，包括模型智能、效率、架构、推理速度与成本，以及训练趋势。

AI 现状（重点摘要）——2025 年年终报告

分析 AI 格局以及塑造 AI 的主要趋势

查看报告

AI 进展

追踪 AI 的持续进步以及各家领先 AI 公司的相对位置。

前沿语言模型智能水平趋势

Artificial Analysis Intelligence Index v4.3.2 incorporates 10 evaluations: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1

Artificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

主要科技公司的资本支出变化

各季度资本支出（十亿美元）

Represents major investments by tech companies in infrastructure, including AI hardware (like GPUs and data centers). Capex is a strong indicator of a company's commitment to AI development, as training and running frontier models requires significant computing resources.

Note: Capex data is sourced from publicly available financial reports, news articles, and primarily from the SEC.

Intelligence Index 与发布日期

Artificial Analysis Intelligence Index · Release date

Most attractive region

各 AI 实验室的领先模型

各 AI 实验室达到的最高 Artificial Analysis Intelligence Index

No publicly available API

效率

分析 AI 效率的发展，包括达到不同智能水平的成本以及获取这种智能的速度如何变化。

按 Intelligence Index 阈值划分的语言模型 Intelligence Index 每项任务成本变化

Intelligence Index 每项任务的成本（美元，按各评测在指数中的权重加权平均）。阈值采用 Artificial Analysis Intelligence Index v4.3.2。

按 Intelligence Index 阈值划分的语言模型输出速度变化

每秒输出 token 数。阈值采用 Artificial Analysis Intelligence Index v4.3.2。

国家分析

AI 是一种全球现象。我们分享 AI 进展发生在哪里，以及各国领先模型如何比较的观点，并深入分析美国和中国这两个主要的 AI 发展中心。

各国前沿语言模型的智能变化

开放权重：各国前沿语言模型的智能变化

按国家/地区划分的领先模型

Artificial Analysis Intelligence Index · Leading models

No publicly available API

开放权重模型

开放权重模型具备灵活部署的优势，并可针对特定使用场景进行微调。我们分析领先的开放权重模型，以及它们的智能与专有模型相比如何。

开放权重与专有模型的智能水平进展

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if commercial use is limited by conditions, and as 'Non-commercial' if the license prohibits commercial use.

Artificial Analysis Intelligence Index：开放权重与专有模型

No publicly available API

模型架构

AI 模型的架构会影响其性能和效率。本部分研究模型架构的趋势，例如混合专家（MoE）架构日益普及，以及这些趋势与模型能力之间的关系。

按模型架构划分的 Intelligence Index 与发布日期

Artificial Analysis Intelligence Index，仅限开放权重模型

Most attractive region

A model where only a subset of parameters ("experts") are active per input. Routing mechanisms select a few experts per forward pass, reducing computation while allowing the model to scale to many more parameters overall.

A model where all parameters are active for every input. Every forward pass involves the full network, making it computationally intensive but straightforward to train and deploy.

模型规模：总参数量与活跃参数量

Comparison between total model parameters and parameters active during inference (billions)

The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses.

The number of parameters actually executed during each inference forward pass, expressed in billions. For Mixture of Experts (MoE) models, a routing mechanism selects a subset of experts per token, resulting in fewer active than total parameters. Dense models use all parameters, so active equals total.

Intelligence Index 与活跃参数

Artificial Analysis Intelligence Index · Active parameters at inference time (billions)

Most attractive quadrant

Pareto line

Intelligence Index 与总参数量

Artificial Analysis Intelligence Index · Size in parameters (billions)

Most attractive quadrant

Pareto line

The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses.

上下文长度（Token）：各季度中位数

上下文长度中位数（千 token）

Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

训练分析

我们对 AI 模型训练趋势的分析，既考察训练运行规模的变化，也考察训练运行规模与模型智能之间的关系。

各模型的训练 Token 数

训练 token 数（万亿）

No data available

The number of tokens used to train the model, represented in trillions.

Intelligence Index 与训练 Token 数

Most attractive quadrant

No data available

The number of tokens used to train the model, represented in trillions.

Artificial Analysis AI 趋势

AI 现状（重点摘要）——2025 年年终报告

AI 进展

前沿语言模型智能水平趋势

Artificial Analysis Intelligence Index

主要科技公司的资本支出变化

Capital Expenditure (Capex)

Capex Data Sources Note

Intelligence Index 与发布日期

Artificial Analysis Intelligence Index

各 AI 实验室的领先模型

Artificial Analysis Intelligence Index

效率

按 Intelligence Index 阈值划分的语言模型 Intelligence Index 每项任务成本变化

Artificial Analysis Intelligence Index

按 Intelligence Index 阈值划分的语言模型输出速度变化

Artificial Analysis Intelligence Index

国家分析

各国前沿语言模型的智能变化

Artificial Analysis Intelligence Index

开放权重：各国前沿语言模型的智能变化

Artificial Analysis Intelligence Index

按国家/地区划分的领先模型

Artificial Analysis Intelligence Index

开放权重模型

开放权重与专有模型的智能水平进展

Artificial Analysis Intelligence Index

Open Weights

Artificial Analysis Intelligence Index：开放权重与专有模型

Artificial Analysis Intelligence Index

Open Weights

模型架构

按模型架构划分的 Intelligence Index 与发布日期

MoE Models

Dense Models

模型规模：总参数量与活跃参数量

Total Parameters

Active Parameters at Inference Time

Intelligence Index 与活跃参数

Artificial Analysis Intelligence Index

Active Parameters at Inference Time

Intelligence Index 与总参数量

Artificial Analysis Intelligence Index

Total Parameters

上下文长度（Token）：各季度中位数

Context Window for RAG

Context Window

训练分析

各模型的训练 Token 数

Training Tokens

Intelligence Index 与训练 Token 数

Artificial Analysis Intelligence Index

Training Tokens