Comparisons of Large Open Source AI Models (>150B)

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

Open source AI models with over 150B parameters.

Models are considered open source (also commonly referred to as open weights) where their weights are accessible to download. This allows self-hosting on your own infrastructure and enables customizing the model such as through fine-tuning.

For more details including relating to our methodology, see our FAQs.

Kimi K3 (max) and

Qwen3.8 2.4T A95B are the highest intelligence Large open source models, defined as those with >150B parameters, followed by

GLM-5.3-Flash &

Qwen3.8-Flash-Next.

Highlights

Openness

Artificial Analysis Openness Index · Higher is better

Intelligence

Artificial Analysis Intelligence Index · Higher is better

Total Parameters

Trainable parameters in billions

Openness

Artificial Analysis Openness Index: Score

Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)

Intelligence

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

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

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

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

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

Size

Model Size: Total and Active Parameters

Comparison between total model parameters and parameters active during inference

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

Intelligence Index vs. Total Parameters

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.

Context Window

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).

Further details

							Weights		Provider Benchmarks
Kimi K3 (max)	Kimi	60	2.8T 104B active at inference time	1M	$2.3	36	🤗	+12	View
Qwen3.8 2.4T A95B	Alibaba	58	2.4T 95B active at inference time	984k	$1.2	25	🤗	+3	View
GLM-5.3-Flash	Z AI	57	320B 18B active at inference time	1M	$0.1	50	🤗	+8	View
Qwen3.8-Flash-Next	Alibaba	56	180B 6B active at inference time	256k	$0.1	73	🤗		View
DeepSeek V4 Pro 0813 (Reasoning, Max Effort)	DeepSeek	53	1.6T 49B active at inference time	1M	$0.7	66	🤗	+5	View
GLM-5.2 (max)	Z AI	53	753B 40B active at inference time	1M	$0.9	69	🤗	+20	View
DeepSeek V4 Flash 0731 (Reasoning, Max Effort)	DeepSeek	52	284B 13B active at inference time	1M	$0.2	119	🤗	+13	View
Kimi K3 (low)	Kimi	48	2.8T 104B active at inference time	1M	$2.3	36	🤗		View
Motif 3	Motif Technologies	47	314B 13.2B active at inference time	262k	-	-	Not available	-	View
MiniMax-M3	MiniMax	45	428B 23B active at inference time	1M	$0.2	118	🤗	+9	View
DeepSeek V4 Pro (Reasoning, Max Effort)	DeepSeek	45	1.6T 49B active at inference time	1M	$0.2	66	🤗	+9	View
DeepSeek V4 Pro (Reasoning, High Effort)	DeepSeek	44	1.6T 49B active at inference time	1M	$0.2	63	🤗	+6	View

Comparisons of Large Open Source AI Models (>150B)

Openness

Intelligence

Total Parameters

Openness

Artificial Analysis Openness Index: Score

Intelligence

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index

Intelligence Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

Size

Model Size: Total and Active Parameters

Total Parameters

Active Parameters at Inference Time

Intelligence Index vs. Active Parameters

Artificial Analysis Intelligence Index

Active Parameters at Inference Time

Intelligence Index vs. Total Parameters

Artificial Analysis Intelligence Index

Total Parameters

Context Window

Context Window

Context Window for RAG

Context Window

Further details