中型开放权重 AI 模型比较（40B-150B）

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

参数量介于 40B 和 150B 之间的开放权重 AI 模型。

如果模型权重可供下载，我们便将其视为开放权重模型。这样用户就可以在自己的基础设施上自行托管，并通过微调等方式定制模型。

如需了解包括方法论在内的更多详情，请参阅常见问题。

Nemotron 3.5 Super VL 是智能得分最高的中型开放权重模型，定义为参数量介于 40B 和 150B 之间的模型，Artificial Analysis Intelligence Index 为 13.6。

开放性

Artificial Analysis Openness Index · Higher is better

Updated

智能

Artificial Analysis Intelligence Index · Higher is better

开放性

Artificial Analysis Openness Index：得分

Openness Index 以 0 至 100 的标准化评分衡量模型的开放程度（分数越高，越开放）

智能Updated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v5.0 incorporates 9 evaluations: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), and Terminal-Bench-Science 0.1

Artificial Analysis Intelligence Index v5.0 includes: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), Terminal-Bench-Science 0.1. 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 v1.1 AccuracyUpdated

Knowledge

AA-Omniscience v1.1 Non-Hallucination RateUpdated

1 - hallucination rate

AutomationBench-AA

Agentic SaaS workflows

Terminal-Bench-Science 0.1New

Agentic scientific research workflows in a terminal

FrontierCode 2.0New

Agentic software engineering in open-source repositories

HLE-DiamondNew

Reasoning & knowledge

GDP.pdf

Professional document reasoning, All-pass

Terminal-Bench 4.0

Agentic coding & terminal use

CritPt v2 (Hard Subset)

Physics reasoning

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

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

GDPval-AA v2.1

Agentic real-world work tasks, Elo

AA-3DNew

Agentic interactive 3D experiences, Elo

No data available

Code2Video-AANew

Product and brand videos designed in code, Elo

No data available

ITBench-AA

Kubernetes incident root-cause analysis

No data available

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.

规模

模型规模：总参数量与活跃参数量

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 与活跃参数

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

Most attractive quadrant

Intelligence Index 与总参数量

Artificial Analysis Intelligence Index · Size in parameters (billions)

Most attractive quadrant

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.

上下文窗口

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).

							权重		服务商基准测试
Nemotron 3.5 Super VL	NVIDIA	14	120B 推理时启用 12B 个参数	1M	$0.1	448	🤗	+5	查看
Qwen3.5 122B A10B (Non-reasoning)	Alibaba	-	125B 推理时启用 10B 个参数	262k	$0.7	146	🤗		查看
Mistral Small 4 (Non-reasoning)	Mistral	-	119B 推理时启用 6.5B 个参数	256k	$0.1	171	🤗		查看
Qwen3.5 122B A10B (Reasoning)	Alibaba	-	125B 推理时启用 10B 个参数	262k	$0.7	132	🤗		查看
Hermes 4 - Llama-3.1 70B (Non-reasoning)	Nous Research	-	70.6B	128k	-	-	🤗	-	查看
Qwen3 Next 80B A3B Instruct	Alibaba	-	80B 推理时启用 3B 个参数	262k	$0.2	136	🤗		查看
HyperNova 60B 2605 (High, Based on gpt-oss-120b)	Multiverse Computing	-	58.7B 推理时启用 4.8B 个参数	131k	-	-	🤗	-	查看
INTELLECT-3 (Based on GLM-4.5-Air)	Prime Intellect	-	107B 推理时启用 12B 个参数	131k	-	-	🤗	-	查看
Command A	Cohere	-	111B	256k	$3.3	18	🤗		查看
Mistral Small 4 (Reasoning)	Mistral	-	119B 推理时启用 6.5B 个参数	256k	$0.1	187	🤗		查看
Ling-3.0-flash-VL	InclusionAI	-	124B 推理时启用 5.5B 个参数	262k	-	-	🤗	-	查看
Sarvam 105B (High)	Sarvam	-	106B 推理时启用 10.3B 个参数	128k	$0.0	195	🤗		查看

中型开放权重 AI 模型比较（40B-150B）

开放性

智能

总参数量

开放性

Artificial Analysis Openness Index：得分

智能Updated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index

Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

规模

模型规模：总参数量与活跃参数量

Total Parameters

Active Parameters at Inference Time

Intelligence Index 与活跃参数

Artificial Analysis Intelligence Index

Active Parameters at Inference Time

Intelligence Index 与总参数量

Artificial Analysis Intelligence Index

Total Parameters

上下文窗口

上下文窗口

Context Window for RAG

Context Window

更多详情