極小オープンウェイトAIモデルの比較（≤4B）

パラメーター数が4B以下のオープンウェイトAIモデルです。通常、必要なリソースが最も少ないモデルです。

ウェイトをダウンロードできるモデルをオープンウェイト（一般にオープンソースとも呼ばれます）とみなします。独自のインフラストラクチャでセルフホストでき、ファインチューニングなどによるモデルのカスタマイズも可能です。

方法論などの詳細は、よくある質問をご覧ください。

AI9StarsのロゴG9v3-3BとAlibabaのロゴQwen3 4B 2507はパラメーター数≤4Bと定義される極小オープンウェイトモデルの中で知能が最も高く、OpenBMBのロゴMiniCPM5-1BとOpenBMBのロゴMiniCPM5-1Bが続きます。

ハイライト

オープン性

Artificial Analysis Openness Index · Higher is better

知能

Artificial Analysis Intelligence Index · Higher is better

総パラメーター数

学習可能なパラメーター数（十億単位）

オープン性

Artificial Analysis Openness Index: Score

Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)
Reasoning models are indicated by a lightbulb icon

知能

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR
Estimate (independent evaluation forthcoming)
Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better
GDPval-AA v2

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

𝜏³-Banking

Agentic tool use

Terminal-Bench v2.1

Agentic coding & terminal use

SciCode

Coding

Humanity's Last Exam

Reasoning & knowledge

GPQA Diamond

Scientific reasoning

CritPt

Physics reasoning

AA-Omniscience Accuracy

Knowledge

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

AA-LCR

Long context reasoning

AA-Briefcase

Agentic knowledge work, Elo

No data available
AutomationBench-AA

Agentic SaaS workflows

No data available
Harvey LAB-AA

Legal agentic work, criterion pass rate

No data available
EnterpriseOps-Gym-AANew

Agentic business operations

No data available
IFBench

Instruction following

APEX-Agents-AA

Long-horizon agentic tasks

No data available
ITBench-AA

Kubernetes incident root-cause analysis

No data available
MMMU-Pro

Visual reasoning

Reasoning models are indicated by a lightbulb icon

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

Artificial Analysis Intelligence Index v4.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

規模

Model Size: Total and Active Parameters

Comparison between total model parameters and parameters active during inference
Reasoning models are indicated by a lightbulb icon

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 vs. Active Parameters

Artificial Analysis Intelligence Index · Active parameters at inference time
Most attractive quadrant
Pareto line
Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

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 vs. Total Parameters

Artificial Analysis Intelligence Index · Size in parameters (billions)
Most attractive quadrant
Pareto line
Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

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.

コンテキストウィンドウ

Context Window

Context window: tokens limit · Higher is better
Reasoning models are indicated by a lightbulb icon

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).

詳細

ウェイト
プロバイダーのベンチマーク
G9v3-3B
AI9StarsのロゴAI9Stars
16
3B
131k
-
-
🤗
AI9Stars
表示
MiniCPM5-1B (Reasoning)
OpenBMBのロゴOpenBMB
12
1B
128k
-
-
🤗
-
表示
MiniCPM5-1B (Non-reasoning)
OpenBMBのロゴOpenBMB
12
1B
128k
-
-
🤗
-
表示
Nanbeige4.1-3B
NanbeigeのロゴNanbeige
11
3.9B
256k
-
-
🤗
-
表示
NVIDIA Nemotron 3 Nano 4B
NVIDIAのロゴNVIDIA
9
4.0B
262k
-
-
🤗
-
表示
Qwen3.5 2B (Reasoning)
AlibabaのロゴAlibaba
8
2.3B
262k
-
-
🤗
-
表示
Ministral 3 3B
MistralのロゴMistral
7
3B
256k
$0.1
206
🤗
Amazon BedrockMistral
表示
Phi-4 Mini Instruct
MicrosoftのロゴMicrosoft
6
3.8B
128k
-
43
🤗
Microsoft Azure
表示
Qwen3.5 2B (Non-reasoning)
AlibabaのロゴAlibaba
6
2.3B
262k
-
-
🤗
-
表示
Qwen3.5 0.8B (Reasoning)
AlibabaのロゴAlibaba
5
0.9B
262k
-
-
🤗
-
表示
Granite 4.1 3B
IBMのロゴIBM
5
3B
131k
-
-
🤗
-
表示
MiniCPM-V 4.6 1.3B
OpenBMBのロゴOpenBMB
4
1.3B
262k
-
-
🤗
-
表示