Comparisons of Medium Open Source AI Models (40B-150B)

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

Open source AI models with between 40B to 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.

Ling-3.0-flash-VL and

Ling-3.0-flash-Fin are the highest intelligence Medium open source models, defined as those with 40B-150B parameters, followed by

Ling 3.0 Flash &

Qwen3.5 122B A10B (Non-reasoning).

Highlights

Openness

Artificial Analysis Openness Index · Higher is better

Updated

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 v4.3 incorporates 10 evaluations: AA-Briefcase, GDPval-AA v2, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1

Estimate (independent evaluation forthcoming)

Artificial Analysis Intelligence Index v4.3 includes: AA-Briefcase, GDPval-AA v2, 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.

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better

AA-Briefcase

Agentic knowledge work, (Elo-500)/2000

GDPval-AA v2

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

AutomationBench-AAUpdated

Agentic SaaS workflows

Terminal-Bench 4.0New

Agentic coding & terminal use

SciCode

Coding

Humanity's Last Exam

Reasoning & knowledge

GDP.pdfNew

Professional document reasoning, All-pass

CritPt

Physics reasoning

AA-Omniscience Accuracy

Knowledge

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

AA-LCR v1.1

Long context reasoning

Harvey LAB-AA

Legal agentic work, criterion pass rate

EnterpriseOps-Gym-AA

Agentic business operations

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

𝜏³-Banking

Agentic tool use

ITBench-AA

Kubernetes incident root-cause analysis

MMMU-Pro

Visual reasoning

MLCR-AANew

Medical long context 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
Ling-3.0-flash-VL	InclusionAI	25	124B 5.5B active at inference time	262k	-	144	🤗		View
Ling-3.0-flash-Fin	InclusionAI	23	124B 5.1B active at inference time	262k	-	162	🤗		View
Ling 3.0 Flash	InclusionAI	21	124B 5.1B active at inference time	262k	$0.0	332	Not available		View
Qwen3.5 122B A10B (Non-reasoning)	Alibaba	18	125B 10B active at inference time	262k	$0.7	141	🤗		View
Qwen3.5 122B A10B (Reasoning)	Alibaba	16	125B 10B active at inference time	262k	$0.7	128	🤗	+2	View
Mistral Medium 3.5	Mistral	15	128B	256k	$1.2	145	🤗		View
Nemotron 3 Super 120B A12B (Reasoning)	NVIDIA	14	120.6B 12.7B active at inference time	1M	$0.2	166	🤗		View
gpt-oss-120b (high)	OpenAI	12	117B 5.1B active at inference time	131k	$0.2	198	🤗	+17	View
HyperNova 60B 2605 (high, based on gpt-oss-120b)	Multiverse Computing	12	58.7B 4.8B active at inference time	131k	$0.1	355	🤗		View
K2 Think V2	MBZUAI Institute of Foundation Models	11	70B	262k	-	-	🤗	-	View
LongCat Flash Lite	LongCat	11	68.5B 3B active at inference time	256k	-	-	🤗	-	View
Mistral Small 4 (Reasoning)	Mistral	11	119B 6.5B active at inference time	256k	$0.2	168	🤗		View

Comparisons of Medium Open Source AI Models (40B-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