Claude Sonnet 4.6 (Max) 在 Artificial Analysis Intelligence Index 上的得分为 30，在同类模型中高于平均水平（中位数：26）。在 Intelligence Index 评测中，它生成了 230M 个 token；与 81M 的中位数相比，其回答非常冗长。

Claude Sonnet 4.6 (Max) 每 100 万输入 token 的价格为 $3.00（有些昂贵，中位数：$2.00），每 100 万输出 token 的价格为 $15.00（有些昂贵，中位数：$10.00）。在 Intelligence Index 上评测 Claude Sonnet 4.6 (Max) 的平均每项任务成本为 $2.49。

Claude Sonnet 4.6 (Max) 的速度为每秒 59 个 token，低于平均水平（86）。

推理	是此页面展示该模型的推理版本。可能还存在非推理版本。
输入模态	支持：文本和图像
输出模态	支持：文本
上下文窗口	1M 约 1500 页 A4 纸（12 号 Arial 字体）

指标与同类别模型进行比较：

非推理模型 → 仅与其他非推理模型比较
推理模型 → 同时与推理和非推理模型比较
开放权重模型 → 仅与规模类别相同的其他开放权重模型比较：

微型：≤4B 个参数
小型：4B–40B 个参数
中型：40B–150B 个参数
大型：>150B 个参数

专有模型 → 与价格区间相同的专有模型和开放权重模型比较，采用输入/输出价格 3:1 的混合比例：

每 100 万 token <$0.15
每 100 万 token $0.15–$1
每 100 万 token >$1

Updated

智能

Artificial Analysis Intelligence Index · Higher is better

Not publicly available

速度

Output tokens per second · Higher is better

每任务成本

Weighted average cost (USD) per Intelligence Index task · Lower is better

Not publicly available

智能Updated

Artificial Analysis Intelligence Index

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

Not publicly available

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.

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

Not publicly available

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.

能力指数

衡量模型在特定能力和行业中的表现

Artificial Analysis 财务与会计指数

Incorporates 7 evaluations: AA-Omniscience, GDPval-AA v2.1, AA-Briefcase v1.1, Humanity's Last Exam, AutomationBench-AA, AA-LCR v1.1, GDP.pdf · Higher is better

Not publicly available

基准测试

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better

Not publicly available

AA-Briefcase v1.1Updated

Agentic knowledge work, (Elo-500)/2000

GDPval-AA v2.1Updated

Agentic real-world work tasks, (Elo-500)/2000

AutomationBench-AA

Agentic SaaS workflows

Terminal-Bench 4.0

Agentic coding & terminal use

SciCodeUnder review

Coding

Humanity's Last Exam

Reasoning & knowledge

GDP.pdf

Professional document reasoning, All-pass

CritPtUnder review

Physics reasoning

AA-Omniscience Accuracy

Knowledge

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

AA-LCR v1.1

Long context reasoning

Harvey LAB-AA

Legal agentic work, criterion pass rate

EnterpriseOps-Gym-AA

Agentic business operations

Terminal-Bench-Science 0.1New

Agentic scientific research workflows in a terminal

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

ITBench-AA

Kubernetes incident root-cause analysis

MMMU-Pro

Visual reasoning

MLCR-AA

Medical long context reasoning

While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

AA-Briefcase v1.1Updated

AA-Briefcase Elo

AA-Briefcase v1.1 is an agentic knowledge work benchmark developed by Artificial Analysis. AA-Briefcase Elo is a combined metric that aggregates rubric pass rate, analytical quality Elo and presentation Elo · Higher is better

Not publicly available

AA-Briefcase Elo is a combined metric that aggregates analytical quality Elo, presentation Elo, and rubric pass rate, with rubric performance converted into Elo via synthetic head-to-head matches. Elo and 95% confidence interval bounds are clamped at 0.

AA-Omniscience

AA-Omniscience Index

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

Not publicly available

Intelligence Index 比较

Intelligence Index 与每项任务的成本

Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task

Most attractive quadrant

Pareto line

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

Token 使用量

智能指数每项任务的输出 Token 数

运行 Artificial Analysis 智能指数单项任务所用输出 token 数的加权平均值

Not publicly available

The number of tokens required per Intelligence Index task. This is calculated by multiplying the output tokens per eval by the relative weights of each benchmark in the Intelligence Index, then dividing by task count (excluding repeats).

成本

每项 Intelligence Index 任务的成本

Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better

Not publicly available

运行 Artificial Analysis Intelligence Index 的成本

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index

Not publicly available

The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input, cache hit, cache write, reasoning, and answer token prices and the number of tokens used across evaluations (excluding repeats).

价格：缓存命中、输入和输出

Price (USD per M Tokens)

Not publicly available

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

上下文窗口

Context window: tokens limit · Higher is better

Not publicly available

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).

速度

按输出速度（每秒 token 数）衡量

输出速度

Output tokens per second · Higher is better

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Figures represent performance of the model's first-party API or the median across providers where a first-party API is not available.

智能指数每项任务耗时

Weighted average decode time (minutes) per task; excludes TTFT and overhead time · Lower is better

The weighted average time (seconds) per Artificial Analysis Intelligence Index task. This is calculated by dividing output tokens per task by output speed, weighted by the relative weights of each benchmark in the Intelligence Index.

延迟

按首 Token 延迟（秒）衡量

延迟: 首个答案 Token 延迟

收到首个回答 token 的秒数 · 包含推理模型的“思考”时间

Time to first answer token received, in seconds, after API request sent. For reasoning models, this includes the 'thinking' time of the model before providing an answer. For models which do not support streaming, this represents time to receive the completion.

端到端响应时间

Seconds to output 500 tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed

端到端响应时间

Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better

Seconds to receive a 500 token response. Key components:

Input time: Time to receive the first response token
Thinking time (only for reasoning models): Time reasoning models spend outputting tokens to reason prior to providing an answer. Amount of tokens based on the average reasoning tokens across a diverse set of 60 prompts (methodology details).
Answer time: Time to generate 500 output tokens, based on output speed

Figures represent performance of the model's first-party API or the median across providers where a first-party API is not available.

常见问题

关于 Claude Sonnet 4.6 (Max) 的常见问题

Claude Sonnet 4.6 (Max) 发布于 2026年2月17日。

Claude Sonnet 4.6 (Max) 由 Anthropic 开发。

Claude Sonnet 4.6 (Max) 在 Artificial Analysis Intelligence Index 上的得分为 30，在其他价格档位相近的推理模型中高于平均水平（中位数：26）。

Claude Sonnet 4.6 (Max) 以每秒 59.1 个 token 的速度生成输出（基于 Anthropic 的 API），与其他价格档位相近的推理模型相比低于平均水平（中位数：86.1 t/s）。

Claude Sonnet 4.6 (Max) 的首 Token 延迟（TTFT）为 126.44 秒（基于 Anthropic 的 API），与其他价格档位相近的推理模型相比处于较高水平（中位数：3.83 秒）。

Claude Sonnet 4.6 (Max) 每 100 万输入 token 的价格为 $3.00（略高于平均水平，中位数：$2.00），每 100 万输出 token 的价格为 $15.00（略高于平均水平，中位数：$10.00）；基于 Anthropic 的 API。

Claude Sonnet 4.6 (Max) 每 100 万输入 token 的价格为 $3.00，每 100 万输出 token 的价格为 $15.00（基于 Anthropic 的 API）。按缓存命中/输入/输出为 7:2:1 的比例计算，混合价格为每 100 万 token $2.31。价格可能因服务商而异。比较服务商价格

在 Intelligence Index 评测中，Claude Sonnet 4.6 (Max) 生成了 230M 个输出 token；与其他价格档位相近的推理模型相比处于较高水平（中位数：81M）。

是，Claude Sonnet 4.6 (Max) 是推理模型。它会在给出答案之前，通过扩展思考或思维链推理来解决复杂问题。

Claude Sonnet 4.6 (Max) 支持文本和图像输入。

Claude Sonnet 4.6 (Max) 支持文本输出。

是，Claude Sonnet 4.6 (Max) 支持图像输入，可以分析、描述图像并回答有关图像的问题。

是，Claude Sonnet 4.6 (Max) 是多模态模型，可以处理文本和图像输入并生成文本输出。

Claude Sonnet 4.6 (Max) 的上下文窗口为 1.0M 个 token。这决定了模型在单次请求中可以处理多少文本和对话历史。

否，Claude Sonnet 4.6 (Max) 是专有模型，其模型权重并未公开。

Claude Sonnet 4.6 (Max) 是专有模型，Anthropic 尚未披露模型规模或参数量。

Claude Sonnet 4.6 (Max) 在 Artificial Analysis Intelligence Index 上的得分为 30。这项综合基准测试评估模型的推理、知识、数学和编程能力。

是，可通过 4 家服务商的 API 使用 Claude Sonnet 4.6 (Max)。比较 API 服务商

可通过 4 家 API 服务商使用 Claude Sonnet 4.6 (Max)。比较服务商

Claude Sonnet 4.6 (Max) 智能、性能与价格分析

模型摘要

智能Updated

比较摘要

技术规格

此类别中有 225 个模型

智能Updated

Artificial Analysis Intelligence Index

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

Artificial Analysis Intelligence Index

Open Weights

Intelligence Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

AA-Briefcase v1.1Updated

AA-Briefcase Elo

AA-Briefcase Elo

AA-Omniscience

AA-Omniscience Index

AA-Omniscience Index

Intelligence Index 比较

Intelligence Index 与每项任务的成本

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

Token 使用量

智能指数每项任务的输出 Token 数

Output Tokens per Intelligence Index Task

成本

每项 Intelligence Index 任务的成本

Cost per Intelligence Index Task

运行 Artificial Analysis Intelligence Index 的成本

Cost to Run Artificial Analysis Intelligence Index

价格：缓存命中、输入和输出

Cache Hit

上下文窗口

上下文窗口

Context Window for RAG

Context Window

速度

输出速度

Output Speed

Model Performance Representation

智能指数每项任务耗时

Time per Intelligence Index Task

延迟

延迟: 首个答案 Token 延迟

Time to First Answer Token

端到端响应时间

端到端响应时间

End-to-End Response Time

Model Performance Representation

常见问题

Claude Sonnet 4.6 (Max) 何时发布？

谁开发了 Claude Sonnet 4.6 (Max)？

Claude Sonnet 4.6 (Max) 有多智能？

Claude Sonnet 4.6 (Max) 有多快？

Claude Sonnet 4.6 (Max) 的延迟是多少？

Claude Sonnet 4.6 (Max) 的费用是多少？

Claude Sonnet 4.6 (Max) API 的价格是多少？

Claude Sonnet 4.6 (Max) 的回答有多冗长？

Claude Sonnet 4.6 (Max) 是推理模型吗？

Claude Sonnet 4.6 (Max) 支持哪些输入模态？

Claude Sonnet 4.6 (Max) 支持哪些输出模态？

Claude Sonnet 4.6 (Max) 能处理图像吗？

Claude Sonnet 4.6 (Max) 是多模态模型吗？

Claude Sonnet 4.6 (Max) 的上下文窗口有多大？

Claude Sonnet 4.6 (Max) 是开放权重模型吗？

Claude Sonnet 4.6 (Max) 有多少个参数？

Claude Sonnet 4.6 (Max) 在基准测试中的表现如何？

可以通过 API 使用 Claude Sonnet 4.6 (Max) 吗？

可以在哪里使用 Claude Sonnet 4.6 (Max)？