小規模オープンウェイトAIモデルの比較（4B～40B）

パラメーター数が4B～40BのオープンウェイトAI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。

AlibabaのロゴQwen3.6 27BとAlibabaのロゴQwen3.5 27Bはパラメーター数4B～40Bと定義される小規模オープンウェイトモデルの中で知能が最も高く、AlibabaのロゴQwen3.6 35B A3BとAI9StarsのロゴG9v3-39A5B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

AutomationBench-AA

Agentic SaaS workflows

No data available
Harvey LAB-AA

Legal agentic work, criterion pass rate

EnterpriseOps-Gym-AANew

Agentic business operations

IFBench

Instruction following

APEX-Agents-AA

Long-horizon agentic tasks

No data available
ITBench-AA

Kubernetes incident root-cause analysis

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
Qwen3.6 27B (Reasoning)
AlibabaのロゴAlibaba
37
27.8B
262k
$0.9
59
🤗
Alibaba CloudSiliconFlowDeepInfra
+2
表示
Qwen3.6 35B A3B (Reasoning)
AlibabaのロゴAlibaba
32
36B
推論時に3Bが有効
262k
$0.4
140
🤗
NovitaGMISiliconFlow
+5
表示
G9v3-39A5B
AI9StarsのロゴAI9Stars
31
39B
推論時に5Bが有効
131k
-
-
🤗
AI9Stars
表示
Qwen3.6 27B (Non-reasoning)
AlibabaのロゴAlibaba
30
27.8B
262k
$0.9
57
🤗
DeepInfraAlibaba CloudNovitaGroq
表示
Gemma 4 31B (Reasoning)
GoogleのロゴGoogle
29
30.7B
256k
-
35
🤗
CoreWeaveCerebrasLightning AI
+10
表示
Gemma 4 26B A4B (Reasoning)
GoogleのロゴGoogle
26
25.2B
推論時に3.8Bが有効
256k
$0.1
-
🤗
GoogleCloudflareParasail
+5
表示
Qwen3.6 35B A3B (Non-reasoning)
AlibabaのロゴAlibaba
24
36B
推論時に3Bが有効
262k
$0.6
163
🤗
NovitaDeepInfraScaleway
+4
表示
Qwen3.5 35B A3B (Non-reasoning)
AlibabaのロゴAlibaba
24
36B
推論時に3Bが有効
262k
$0.4
170
🤗
DeepInfraAlibaba Cloud
表示
Gemma 4 12B (Reasoning)
GoogleのロゴGoogle
22
12B
256k
$0.1
111
🤗
SiliconFlow
表示
Gemma 4 31B (Non-reasoning)
GoogleのロゴGoogle
22
30.7B
256k
$0.2
68
🤗
ParasailCerebrasSambaNova
+5
表示
Qwen3.5 9B (Reasoning)
AlibabaのロゴAlibaba
21
9.7B
262k
$0.1
74
🤗
SiliconFlowTogether AI
表示
Apriel-v1.6-15B-Thinker
ServiceNowのロゴServiceNow
21
15B
128k
-
-
🤗
Together AI
表示