大規模オープンウェイトAIモデルの比較（>150B）

Name: オープン性
Creator: Artificial Analysis
License: https://artificialanalysis.ai/docs/legal/Terms-of-Use.pdf

パラメーター数が150Bを超えるオープンウェイト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。

MiMo-V2.6-Proと

DeepSeek V4.1 Flash 1017 (max)はパラメーター数>150Bと定義される大規模オープンウェイトモデルの中で知能が最も高く、

GLM-5.3 (max)と

Step 5 Previewが続きます。

オープン性

Artificial Analysis Openness Index · Higher is better

知能

Artificial Analysis Intelligence Index · Higher is better

オープン性

Artificial Analysis Openness Index：スコア

Openness Indexはモデルのオープン性を0～100の正規化尺度で評価します（高いほどオープン）

知能

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

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

EnterpriseOps-Gym-AA

Agentic business operations

AA-3DNew

Agentic interactive 3D experiences, Elo

Code2Video-AANew

Product and brand videos designed in code, Elo

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.

規模

モデルサイズ：総パラメータ数とアクティブパラメータ数

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と有効パラメーター数

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

Most attractive quadrant

Pareto line

Intelligence Index と総パラメータ数

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.

コンテキストウィンドウ

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).

詳細

							ウェイト		プロバイダーのベンチマーク
MiMo-V2.6-Pro	Xiaomi	46	1.0T 推論時に42Bが有効	1M	$0.2	56	🤗		表示
DeepSeek V4.1 Flash 1017 (Max)	DeepSeek	45	552B 推論時に16Bが有効	1M	$0.2	169	🤗	+19	表示
GLM-5.3 (Max)	Z AI	45	753B 推論時に40Bが有効	1M	$0.9	94	🤗	+24	表示
Step 5 Preview	StepFun	44	600B 推論時に27Bが有効	1M	$0.5	198	🤗		表示
GLM-5.3-Flash	Z AI	42	320B 推論時に18Bが有効	1M	$0.1	73	🤗	+23	表示
Ling 3.1 Flash	InclusionAI	41	560B 推論時に25Bが有効	1M	$0.2	213	🤗		表示
Qwen3.8 2.4T A95B	Alibaba	40	2.4T 推論時に95Bが有効	262k	$1.2	41	🤗	+3	表示
Qwen3.8-Flash-Next	Alibaba	40	180B 推論時に6Bが有効	256k	$0.1	53	🤗		表示
Muse Spark Open (Xhigh)	Meta	39	816B 推論時に42Bが有効	1M	$0.8	204	🤗	+3	表示
MiMo-V2.6-Flash	Xiaomi	38	309B 推論時に15Bが有効	1M	$0.1	55	🤗		表示
DeepSeek V4 Pro 0813 (Max)	DeepSeek	36	1.6T 推論時に49Bが有効	1M	$0.7	98	🤗	+7	表示
GLM-5.3 (Low)	Z AI	34	753B 推論時に40Bが有効	1M	$0.9	89	🤗	+5	表示

大規模オープンウェイトAIモデルの比較（>150B）

オープン性

知能

総パラメーター数

オープン性

Artificial Analysis Openness Index：スコア

知能

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index

Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

規模

モデルサイズ：総パラメータ数とアクティブパラメータ数

Total Parameters

Active Parameters at Inference Time

Intelligence Indexと有効パラメーター数

Artificial Analysis Intelligence Index

Active Parameters at Inference Time

Intelligence Index と総パラメータ数

Artificial Analysis Intelligence Index

Total Parameters

コンテキストウィンドウ

コンテキストウィンドウ

Context Window for RAG

Context Window

詳細