GPT-6 Luna (Max) 在 Artificial Analysis Intelligence Index 上的得分为 28，在同类模型中高于平均水平（中位数：22）。在 Intelligence Index 评测中，它生成了 110M 个 token；与 80.7M 的中位数相比，其回答有些冗长。

GPT-6 Luna (Max) 每 100 万输入 token 的价格为 $0.10（很有竞争力，中位数：$0.24），每 100 万输出 token 的价格为 $0.50（很有竞争力，中位数：$0.87）。在 Intelligence Index 上评测 GPT-6 Luna (Max) 的平均每项任务成本为 $0.15。

GPT-6 Luna (Max) 完成一项 Intelligence Index 任务需 12.0 分钟，与同类模型 6.9 分钟的中位数相比高于平均水平（越低越好）。

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

No publicly available API

每任务耗时

Inference time (minutes) per Intelligence Index task · Lower is better

No publicly available API

每任务成本

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

No publicly available API

智能Updated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v5.0.1 incorporates 9 evaluations: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), and Terminal-Bench-Science 0.1

No publicly available API

Artificial Analysis Intelligence Index v5.0.1 includes: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), Terminal-Bench-Science 0.1. 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

No publicly available API

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.

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.

能力指数

衡量模型在特定能力和行业中的表现

Artificial Analysis Finance & Accounting Index

Incorporates 5 evaluations: AA-Briefcase v1.1, AA-Omniscience v1.1, HLE-Diamond, AutomationBench-AA, GDP.pdf · Higher is better

No publicly available API

基准测试

Evaluations

Evaluations measured independently by Artificial Analysis · AA-PersonalityBench scores are percentile ranks from 1 to 100

No publicly available API

AA-Briefcase v1.1

Agentic knowledge work, Elo

AA-Omniscience v1.1 AccuracyUpdated

Knowledge

AA-Omniscience v1.1 Non-Hallucination RateUpdated

1 - hallucination rate

AutomationBench-AA

Agentic SaaS workflows

Terminal-Bench-Science 0.1New

Agentic scientific research workflows in a terminal

FrontierCode 2.0New

Agentic software engineering in open-source repositories

HLE-DiamondNew

Reasoning & knowledge

GDP.pdf

Professional document reasoning, All-pass

Terminal-Bench 4.0

Agentic coding & terminal use

CritPt v2 (Hard Subset)

Physics reasoning

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

Harvey LAB-AA v1.1Updated

Legal agentic work, Hallucination-Gated All-Pass Rate

EnterpriseOps-Gym-AA

Agentic business operations

GDPval-AA v2.1

Agentic real-world work tasks, Elo

AA-3DNew

Agentic interactive 3D experiences, Elo

Code2Video-AANew

Product and brand videos designed in code, Elo

ITBench-AA

Kubernetes incident root-cause analysis

MLCR-AAUpdated

Medical long context reasoning

MMMU-Pro

Visual reasoning

AA-PersonalityBench: Expressiveness ScoreNew

Rich, varied, well-crafted language

AA-PersonalityBench: Organization ScoreNew

Well-structured, scannable, answer-first

AA-PersonalityBench: Transparency ScoreNew

Truthful, acknowledges uncertainty, non-sycophantic

AA-PersonalityBench: Slop ScoreNew

Use of filler and generic language

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

AA-Briefcase v1.1

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

No publicly available API

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 v1.1

AA-Omniscience 净得分

AA-Omniscience v1.1 Net Score (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.

No publicly available API

AA-PersonalityBenchNew

性格指数

每个轴对应 8 个性格指数之一：其特质 Elo z 分数的平均值，在基准测试中所有已评估模型之间换算为 1 到 100 的百分位排名

Token 使用量

Intelligence Index 每项任务的输出 Token 数

运行 Artificial Analysis Intelligence Index 单项任务所用输出 token 数的加权平均值

No publicly available API

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 Intelligence Index task · Segmented by token type · Lower is better

No publicly available API

运行 Artificial Analysis Intelligence Index 的成本

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

No publicly available API

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)

No publicly available API

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

No publicly available API

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).

性能

按每任务耗时（分钟）、输出速度（每秒 token 数）、首个答案 Token 延迟和首 Token 延迟（秒）衡量

Intelligence Index 每项任务耗时

Output tokens divided by end-to-end output speed (minutes per Intelligence Index task) · Speed at the model's own endpoint or the median across providers · Lower is better

No publicly available API

The weighted average time (minutes) per Artificial Analysis Intelligence Index task: output tokens per task divided by output speed, weighted by each benchmark's weight in the Intelligence Index.

Speed and latency metrics average each endpoint's runs over the past 5 days, and the error rate pools them. We run the benchmark every 6 hours, and endpoints of deprecated models every 18 hours.

输出速度

Output tokens divided by total request time, including time to first token · Higher is better

No publicly available API

Output tokens, including reasoning tokens, received per second over a request's full duration, from sending the request to receiving the final token. The headline value averages each Performance v3 workload over the past 5 days, then averages the workloads.

首个答案 Token 延迟

收到首个答案 token 的秒数 · 包含推理模型的“思考”时间

No publicly available API

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.

常见问题

关于 GPT-6 Luna (Max) 的常见问题

GPT-6 Luna (Max) 发布于 2026年9月22日。

GPT-6 Luna (Max) 由 OpenAI 开发。

GPT-6 Luna (Max) 在 Artificial Analysis Intelligence Index 上的得分为 28，在其他价格档位相近的模型中高于平均水平（中位数：22）。

GPT-6 Luna (Max) 完成一项 Intelligence Index 任务需 12.0 分钟（基于 OpenAI 的 API），与其他价格档位相近的模型相比慢于平均水平（中位数：6.9 分钟，越低越好）。

GPT-6 Luna (Max) 的首个答案 Token 延迟为 78.26 秒（基于 OpenAI 的 API），与其他价格档位相近的模型相比处于较高水平（中位数：10.18 秒）。

GPT-6 Luna (Max) 每 100 万输入 token 的价格为 $0.10（非常有竞争力，中位数：$0.24），每 100 万输出 token 的价格为 $0.50（非常有竞争力，中位数：$0.87）；基于 OpenAI 的 API。

GPT-6 Luna (Max) 每 100 万输入 token 的价格为 $0.10，每 100 万输出 token 的价格为 $0.50（基于 OpenAI 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 $0.08。价格可能因服务商而异。比较服务商价格

在 Intelligence Index 评测中，GPT-6 Luna (Max) 生成了 110M 个输出 token；与其他价格档位相近的模型相比略高于平均水平（中位数：80.7M）。

是，GPT-6 Luna (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。

GPT-6 Luna (Max) 支持文本和图像输入。

GPT-6 Luna (Max) 支持文本输出。

是，GPT-6 Luna (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，GPT-6 Luna (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。

GPT-6 Luna (Max) 的上下文窗口为 1M 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，GPT-6 Luna (Max) 是专有模型，其模型权重并未公开。

GPT-6 Luna (Max) 是专有模型，OpenAI 尚未披露模型规模或参数量。

GPT-6 Luna (Max) 在 Artificial Analysis Intelligence Index 上的得分为 28。这项综合基准测试评估模型的知识工作、编程、科学和通用知识能力。

是，可通过 8 家服务商的 API 使用 GPT-6 Luna (Max)。比较 API 服务商

可通过 8 家 API 服务商使用 GPT-6 Luna (Max)。比较服务商

GPT-6 Luna (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 167 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 Index 与每项任务的成本

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

AA-Briefcase v1.1

AA-Briefcase Elo

AA-Briefcase Elo

AA-Omniscience v1.1

AA-Omniscience 净得分

AA-Omniscience Net Score

AA-PersonalityBenchNew

性格指数

Token 使用量

Intelligence Index 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

性能

Intelligence Index 每项任务耗时

Time per Intelligence Index Task

Time Period

输出速度

End-to-End Output Speed

首个答案 Token 延迟

Time to First Answer Token

常见问题

GPT-6 Luna (Max) 何时发布？

谁开发了 GPT-6 Luna (Max)？

GPT-6 Luna (Max) 有多智能？

GPT-6 Luna (Max) 有多快？

GPT-6 Luna (Max) 的延迟是多少？

GPT-6 Luna (Max) 的费用是多少？

GPT-6 Luna (Max) API 的价格是多少？

GPT-6 Luna (Max) 的回答有多冗长？

GPT-6 Luna (Max) 是推理模型吗？

GPT-6 Luna (Max) 支持哪些输入模态？

GPT-6 Luna (Max) 支持哪些输出模态？

GPT-6 Luna (Max) 能处理图像吗？

GPT-6 Luna (Max) 是多模态模型吗？

GPT-6 Luna (Max) 的上下文窗口有多大？

GPT-6 Luna (Max) 是开放权重模型吗？

GPT-6 Luna (Max) 有多少个参数？

GPT-6 Luna (Max) 在基准测试中的表现如何？

可以通过 API 使用 GPT-6 Luna (Max) 吗？

可以在哪里使用 GPT-6 Luna (Max)？