小型开放权重 AI 模型比较（4B-40B）

Name: 开放性
Creator: Artificial Analysis
License: https://artificialanalysis.ai/docs/legal/Terms-of-Use.pdf

参数量介于 4B 和 40B 之间的开放权重 AI 模型。

如果模型权重可供下载，我们便将其视为开放权重模型。这样用户就可以在自己的基础设施上自行托管，并通过微调等方式定制模型。

如需了解包括方法论在内的更多详情，请参阅常见问题。

Qwen3.8 27B (xhigh) 和

Qwen3.8 27B (medium) 是智能得分最高的小型开放权重模型，定义为参数量介于 4B 和 40B 之间的模型，其次是

Qwen3.8 27B (low) 和

K2 Horizon MoVA 36B A4B。

开放性

Artificial Analysis Openness Index · Higher is better

智能

Artificial Analysis Intelligence Index · Higher is better

开放性

Artificial Analysis Openness Index：得分

Openness Index 以 0 至 100 的标准化评分衡量模型的开放程度（分数越高，越开放）

智能

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

Estimate (independent evaluation forthcoming)

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.

Evaluations

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

AA-Briefcase v1.1

Agentic knowledge work, Elo

AA-Omniscience Accuracy

Knowledge

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

AutomationBench-AA

Agentic SaaS workflows

GDPval-AA v2.1

Agentic real-world work tasks, Elo

Humanity's Last Exam

Reasoning & knowledge

GDP.pdf

Professional document reasoning, All-pass

Terminal-Bench 4.0

Agentic coding & terminal use

AA-LCR v1.1

Long context reasoning

SciCodeUnder review

Coding

CritPt

Physics reasoning

Terminal-Bench-Science 0.1New

Agentic scientific research workflows in a terminal

FrontierCode 1.1New

Agentic software engineering in open-source repositories

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

Harvey LAB-AA v1.1Updated

Legal agentic work, Hallucination-Gated All-Pass Rate

No data available

EnterpriseOps-Gym-AA

Agentic business operations

AA-3DNew

Agentic interactive 3D experiences, Elo

No data available

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.

规模

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.

上下文窗口

Context window: tokens limit · Higher is better

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).

							权重		服务商基准测试
Qwen3.8 27B (Xhigh)	Alibaba	34	27B	256k	$0.5	45	🤗	+9	查看
Qwen3.8 27B (Medium)	Alibaba	28	27B	256k	$0.5	48	🤗		查看
Qwen3.8 27B (Low)	Alibaba	26	27B	256k	$0.5	49	🤗		查看
K2 Horizon MoVA 36B A4B	Institute of Foundation Models	25	36B 推理时启用 4B 个参数	524k	-	-	🤗	-	查看
G9v3-39A5B	AI9Stars	22	39B 推理时启用 5B 个参数	131k	-	-	🤗	-	查看
K2 Horizon 7B	Institute of Foundation Models	21	7B	524k	-	-	🤗	-	查看
Qwen3.8 27B (Non-reasoning)	Alibaba	20	27B	256k	$0.5	50	🤗	+3	查看
Qwen3.6 35B A3B (Reasoning)	Alibaba	18	36B 推理时启用 3B 个参数	262k	$0.6	131	🤗	+5	查看
Muse Glimmer (High)	Meta	17	30B	131k	$0.2	105	🤗		查看
Gemma 4 26B A4B (Reasoning)	Google	17	25.2B 推理时启用 3.8B 个参数	256k	$0.1	91	🤗	+4	查看
Qwen3.6 35B A3B (Non-reasoning)	Alibaba	15	36B 推理时启用 3B 个参数	262k	$0.6	147	🤗	+4	查看
Gemma 4 31B (Reasoning)	Google	15	30.7B	256k	$0	35	🤗	+9	查看

小型开放权重 AI 模型比较（4B-40B）

开放性

智能

总参数量

开放性

Artificial Analysis Openness Index：得分

智能

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index

Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

规模

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

上下文窗口

上下文窗口

Context Window for RAG

Context Window

更多详情