GPT-6 Luna vs. Qwen3.8 27B：发布比较

GPT-6 Luna 与 Qwen3.8 27B 发布比较：分别包含 6 个和 4 个模型，各自具有不同的智能、性能与价格特征。下方对全部 10 个模型的关键指标进行了比较。

智能方面，GPT-6 Luna 得分最高：GPT-6 Luna (Max)（28），Qwen3.8 27B 为 Qwen3.8 27B (Xhigh)（20）。
输出速度方面，GPT-6 Luna 最快：GPT-6 Luna (Xhigh)（127 token/秒），Qwen3.8 27B 为 Qwen3.8 27B (Medium)（51 token/秒）。
单任务成本方面，GPT-6 Luna 最低：GPT-6 Luna (Low)（$0.01），Qwen3.8 27B 为 Qwen3.8 27B (Low)（$1.91）。

比较的发布


	GPT-6 Luna (max)	GPT-6 Luna (xhigh)	GPT-6 Luna (high)	Qwen3.8 27B (xhigh)	GPT-6 Luna (medium)	Qwen3.8 27B (medium)	Qwen3.8 27B (low)	Qwen3.8 27B (non-reasoning)	GPT-6 Luna (low)	GPT-6 Luna (non-reasoning)
Intelligence Index	28	23	21	20	18	17	16	10	10	9
财务与会计	34	31	29	36	26	29	28	22	17	19
战略与运营	43	39	38	39	34	34	29	16	17	17
法律	32	28	27	33	24	26	27	22	16	17
医疗与健康	35	30	29	35	27	24	23	19	16	16
工程	33	29	27	28	25	22	21	17	19	17
经济学	35	32	30	35	28	29	28	22	19	20
AA-Briefcase v1.1	1337	1235	1187	1393	1096	1373	1270	901	740	902
AutomationBench-AA	53.2%	47.8%	47.8%	48.2%	40.5%	40.3%	30.1%	12.4%	12.0%	9.1%
Terminal-Bench 4.0	12.6%	8.1%	4.5%	5.6%	2.5%	5.1%	2.5%	0.0%	0.0%	1.5%
FrontierCode 2.0	25.2%	18.4%	18.6%	14.9%	12.8%	11.5%	12.4%	3.7%	2.2%	1.6%
AA-Omniscience v1.1	−8	−13	−13	−6	−14	−36	−21	−7	−18	−26
GDP.pdf	22.8%	18.6%	18.2%	16.4%	13.8%	12.8%	10.4%	5.2%	9.0%	5.8%
HLE-Diamond	22.2%	18.9%	15.6%	15.3%	15.1%	6.7%	6.8%	4.2%	10.2%	6.3%
CritPt v2 (Hard Subset)	41.5%	35.5%	30.5%	4.0%	26.0%	16.5%	5.5%	2.0%	9.5%	4.5%
Terminal-Bench-Science 0.1	8.6%	5.2%	2.9%	0.5%	1.4%	1.0%	0.5%	0.0%	1.0%	1.0%

每任务成本美元	$0.15	$0.10	$0.07	$3.53	$0.04	$2.16	$1.91	$3.77	$0.01	$0.07
输入价格美元/100 万 Token	$0.10	$0.10	$0.10	$0.50	$0.10	$0.50	$0.50	$0.50	$0.10	$0.10
输出价格美元/100 万 Token	$0.50	$0.50	$0.50	$3.00	$0.50	$3.00	$3.00	$3.00	$0.50	$0.50
缓存命中价格美元/100 万 Token	$0.01	$0.01	$0.01	$0.10	$0.01	$0.10	$0.10	$0.10	$0.01	$0.01
缓存写入价格美元/100 万 Token	$0.125	$0.125	$0.125	--	$0.125	--	--	--	$0.125	$0.125

输出速度Token/秒	118	127	120	41	--	51	48	46	127	118
首 Token 延迟秒	112.81	40.38	29.02	4.01	--	4.09	3.93	4.02	2.81	0.81
首个回答 Token 时间秒	112.81	40.38	29.02	52.66	--	43.23	45.40	4.02	2.81	0.81
端到端响应时间秒	117.04	44.30	33.19	64.83	--	53.02	55.77	14.99	6.74	5.05
每任务耗时秒	630.00	345.72	275.42	1,842.60	--	1,579.30	1,470.44	902.48	25.97	85.20

上下文窗口	1M	1M	1M	256k	1M	256k	256k	256k	1M	1M
开发商	OpenAI	OpenAI	OpenAI	Alibaba	OpenAI	Alibaba	Alibaba	Alibaba	OpenAI	OpenAI
许可证	专有	专有	专有	开放	专有	开放	开放	开放	专有	专有
输入模态	支持：文本和图像	支持：文本和图像	支持：文本和图像	支持：文本、图像和视频	支持：文本和图像	支持：文本、图像和视频	支持：文本、图像和视频	支持：文本、图像和视频	支持：文本和图像	支持：文本和图像
输出模态	支持：文本	支持：文本	支持：文本	支持：文本	支持：文本	支持：文本	支持：文本	支持：文本	支持：文本	支持：文本
服务商				+7				+4

Updated

智能

Artificial Analysis Intelligence Index · Higher is better

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

智能Updated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v5.0 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

Artificial Analysis Intelligence Index v5.0 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.

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

GPT-6 Luna

Qwen3.8 27B

Pareto line

AA-Briefcase v1.1

AutomationBench-AA

单任务成本（美元，对数刻度）

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.

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

速度与延迟

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.

能力得分

能力指数

衡量模型在特定能力与行业中的表现

Finance & Accounting Index

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

Strategy & Ops Index

Incorporates 4 evaluations: AutomationBench-AA, AA-Briefcase v1.1, AA-Omniscience v1.1, GDP.pdf · Higher is better

Legal Index

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

Healthcare & Medical Index

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

Engineering Index

Incorporates 7 evaluations: AA-Omniscience v1.1, CritPt v2 (Hard Subset), Terminal-Bench-Science 0.1, HLE-Diamond, Terminal-Bench 4.0, FrontierCode 2.0, AA-Briefcase v1.1 · Higher is better

Economics Index

Incorporates 4 evaluations: AA-Briefcase v1.1, AA-Omniscience v1.1, HLE-Diamond, GDP.pdf · Higher is better

基准测试

AA-PersonalityBenchNew

模型 Avs模型 B

AA-PersonalityBench：Expressiveness 指数，GPT-6 Luna (max) vs Qwen3.8 27B (xhigh)

根据两者的 Elo 差距，每个模型的回答在各特质上被判定为更显著的概率 · 50% 表示势均力敌

GPT-6 Luna (max)

Qwen3.8 27B (xhigh)

Natural Sounding

93%

Lexically Varied

90%

10%

Literate

85%

15%

Metaphorical

16%

84%

Eloquent

40%

60%

GPT-6 Luna vs. Qwen3.8 27B：发布比较

智能

速度

每任务成本

智能Updated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index

Intelligence Index 与每项任务的成本

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

成本

每项 Intelligence Index 任务的成本

Cost per Intelligence Index Task

速度与延迟

输出速度

Output Speed

Model Performance Representation

能力得分

能力指数

基准测试

AA-PersonalityBenchNew

AA-PersonalityBench：Expressiveness 指数，GPT-6 Luna (max) vs Qwen3.8 27B (xhigh)