초소형 오픈 웨이트 AI 모델 비교(≤4B)

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

파라미터 수가 4B개 이하인 오픈 웨이트 AI 모델입니다. 일반적으로 필요한 리소스가 가장 적은 모델입니다.

웨이트를 다운로드할 수 있는 모델을 오픈 웨이트 모델(흔히 오픈 소스라고도 함)로 간주합니다. 자체 인프라에서 호스팅할 수 있으며 미세 조정 등을 통해 모델을 맞춤 설정할 수 있습니다.

방법론을 비롯한 자세한 내용은 FAQ에서 확인하세요.

K2 Horizon 3.7B과

MiniCPM5-2B은 파라미터 수가 ≤4B개인 초소형 오픈 웨이트 모델 중 지능이 가장 높으며

G9v3-3B과

Granite 4.2 3B이 뒤를 잇습니다.

개방성

Artificial Analysis Openness Index · Higher is better

지능

Artificial Analysis Intelligence Index · Higher is better

개방성

Artificial Analysis Openness Index: 점수

Openness Index는 모델의 개방성을 0~100으로 정규화해 평가합니다(높을수록 더 개방적)

지능

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

No data available

FrontierCode 1.1New

Agentic software engineering in open-source repositories

No data available

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

No data available

Harvey LAB-AA v1.1Updated

Legal agentic work, Hallucination-Gated All-Pass Rate

No data available

EnterpriseOps-Gym-AA

Agentic business operations

No data available

AA-3DNew

Agentic interactive 3D experiences, Elo

No data available

ITBench-AA

Kubernetes incident root-cause analysis

No data available

MLCR-AAUpdated

Medical long context reasoning

No data available

MMMU-Pro

Visual reasoning

AA-PersonalityBench: Expressiveness ScoreNew

Rich, varied, well-crafted language

No data available

AA-PersonalityBench: Organization ScoreNew

Well-structured, scannable, answer-first

No data available

AA-PersonalityBench: Transparency ScoreNew

Truthful, acknowledges uncertainty, non-sycophantic

No data available

AA-PersonalityBench: Slop ScoreNew

Use of filler and generic language

No data available

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

크기

모델 크기: 총 매개변수와 활성 매개변수

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 Index와 활성 파라미터

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

Most attractive quadrant

Pareto line

Intelligence Index와 총 매개변수

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.

컨텍스트 창

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).

상세 정보

							웨이트		제공업체 벤치마크
K2 Horizon 3.7B	Institute of Foundation Models	16	3.7B	524k	-	-	🤗	-	보기
MiniCPM5-2B	OpenBMB	12	2.6B	131k	-	-	🤗	-	보기
G9v3-3B	AI9Stars	11	3B	131k	-	-	🤗	-	보기
Granite 4.2 3B	IBM	9	3B	131k	$0.0	51	🤗		보기
MiniCPM5-1B (Reasoning)	OpenBMB	9	1B	128k	-	-	🤗	-	보기
MiniCPM5-1B (Non-reasoning)	OpenBMB	9	1B	128k	-	-	🤗	-	보기
Nanbeige4.1-3B	Nanbeige	8	3.9B	256k	-	-	🤗	-	보기
LFM2.5-2.6B	Liquid AI	8	2.7B	128k	-	-	🤗	-	보기
Qwen3.5 2B (Reasoning)	Alibaba	7	2.3B	262k	-	-	🤗	-	보기
Phi-4 Mini Instruct	Microsoft	6	3.8B	128k	$0.1	46	🤗		보기
Qwen3.5 2B (Non-reasoning)	Alibaba	6	2.3B	262k	-	-	🤗	-	보기
Qwen3.5 0.8B (Reasoning)	Alibaba	6	0.9B	262k	-	-	🤗	-	보기

초소형 오픈 웨이트 AI 모델 비교(≤4B)

개방성

지능

총 파라미터 수

개방성

Artificial Analysis Openness Index: 점수

지능

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index

Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

크기

모델 크기: 총 매개변수와 활성 매개변수

Total Parameters

Active Parameters at Inference Time

Intelligence Index와 활성 파라미터

Artificial Analysis Intelligence Index

Active Parameters at Inference Time

Intelligence Index와 총 매개변수

Artificial Analysis Intelligence Index

Total Parameters

컨텍스트 창

컨텍스트 창

Context Window for RAG

Context Window

상세 정보