中規模オープンウェイトAIモデルの比較（40B～150B）

パラメーター数が40B～150B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。

InclusionAIのロゴLing 3.0 FlashとAlibabaのロゴQwen3.5 122B A10Bはパラメーター数40B～150Bと定義される中規模オープンウェイトモデルの中で知能が最も高く、MistralのロゴMistral Medium 3.5とAlibabaのロゴQwen3.5 122B A10B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 v4.1.1 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 v4.1.1 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
See more
GDPval-AA v2

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

𝜏³-BankingUpdated

Agentic tool use

Terminal-Bench v2.1

Agentic coding & terminal use

SciCode

Coding

Humanity's Last ExamUpdated

Reasoning & knowledge

GPQA Diamond

Scientific reasoning

CritPt

Physics reasoning

AA-Omniscience AccuracyUpdated

Knowledge

AA-Omniscience Non-Hallucination RateUpdated

1 - hallucination rate

AA-LCRUpdated

Long context reasoning

AA-Briefcase

Agentic knowledge work, Elo

AutomationBench-AA

Agentic SaaS workflows

Harvey LAB-AA

Legal agentic work, criterion pass rate

EnterpriseOps-Gym-AA

Agentic business operations

AA-AnalystAgentNew

Quantitative analysis on spreadsheets & documents

IFBench

Instruction following

APEX-Agents-AA

Long-horizon agentic tasks

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 v4.1.1 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 v4.1.1 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 v4.1.1 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
Ling 3.0 Flash
InclusionAIのロゴInclusionAI
38
124B
推論時に5.1Bが有効
262k
$0.0
387
利用不可
DeepInfraInclusionAI
表示
Qwen3.5 122B A10B (Reasoning)
AlibabaのロゴAlibaba
33
125B
推論時に10Bが有効
262k
$0.7
131
🤗
DeepInfraSiliconFlowNovita
+2
表示
Mistral Medium 3.5
MistralのロゴMistral
30
128B
256k
$1.2
144
🤗
Self-hostedMistral
表示
Qwen3.5 122B A10B (Non-reasoning)
AlibabaのロゴAlibaba
28
125B
推論時に10Bが有効
262k
$0.7
146
🤗
DeepInfraAlibaba Cloud
表示
Nemotron 3 Super 120B A12B (Reasoning)
NVIDIAのロゴNVIDIA
26
120.6B
推論時に12.7Bが有効
1M
$0.3
142
🤗
CoreWeaveNebiusDeepInfra
表示
gpt-oss-120b (high)
OpenAIのロゴOpenAI
24
117B
推論時に5.1Bが有効
131k
$0.2
177
🤗
ScalewayBasetenTogether AI
+16
表示
Qwen3 Coder Next
AlibabaのロゴAlibaba
21
79.7B
推論時に3Bが有効
256k
$0.4
121
🤗
Amazon BedrockNovitaTogether AIParasail
表示
Mistral Small 4 (Reasoning)
MistralのロゴMistral
20
119B
推論時に6.5Bが有効
256k
$0.2
174
🤗
Mistral
表示
Devstral 2
MistralのロゴMistral
19
125B
256k
-
49
🤗
Mistral
表示
HyperNova 60B 2605
Multiverse ComputingのロゴMultiverse Computing
18
58.7B
推論時に4.8Bが有効
131k
$0.1
366
🤗
Multiverse Computing
表示
K2 Think V2
MBZUAI Institute of Foundation ModelsのロゴMBZUAI Institute of Foundation Models
17
70B
262k
-
-
🤗
-
表示
LongCat Flash Lite
LongCatのロゴLongCat
17
68.5B
推論時に3Bが有効
256k
-
-
🤗
-
表示