Comparisons of Tiny Open Source AI Models (≤4B)

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

Open source AI models with 4B parameters or fewer. These are usually the smallest models in terms of resource demand.

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.

Qwen3 4B 2507 and

MiniCPM5-1B are the highest intelligence Tiny open source models, defined as those with ≤4B parameters, followed by

MiniCPM5-1B &

Nanbeige4.1-3B.

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)

Reasoning models are indicated by a lightbulb icon

Intelligence

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.1 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 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-BriefcaseNew

Agentic knowledge work, Elo

No data available

AutomationBench-AANew

Agentic SaaS workflows

No data available

Harvey LAB-AANew

Legal agentic work, criterion pass rate

No data available

EnterpriseOps-Gym-AANew

Agentic business operations

No data available

IFBench

Instruction following

APEX-Agents-AA

Long-horizon agentic tasks

No data available

ITBench-AA

Kubernetes incident root-cause analysis

No data available

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.

Size

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

Reasoning models are indicated by a lightbulb icon.

Intelligence Index vs. Total Parameters

Artificial Analysis Intelligence Index · Size in parameters (billions)

Most attractive quadrant

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.

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).

Further details

							Weights		Provider Benchmarks
MiniCPM5-1B (Reasoning)	OpenBMB	12	1B	128k	-	-	🤗	-	View
MiniCPM5-1B (Non-reasoning)	OpenBMB	12	1B	128k	-	-	🤗	-	View
Nanbeige4.1-3B	Nanbeige	11	3.9B	256k	-	-	🤗	-	View
NVIDIA Nemotron 3 Nano 4B	NVIDIA	9	4.0B	262k	-	-	🤗	-	View
Qwen3.5 2B (Reasoning)	Alibaba	8	2.3B	262k	-	-	🤗	-	View
Ministral 3 3B	Mistral	7	3B	256k	$0.1	164	🤗		View
Phi-4 Mini Instruct	Microsoft	6	3.8B	128k	-	45	🤗		View
Qwen3.5 2B (Non-reasoning)	Alibaba	6	2.3B	262k	-	-	🤗	-	View
Qwen3.5 0.8B (Reasoning)	Alibaba	5	0.9B	262k	-	-	🤗	-	View
Granite 4.1 3B	IBM	5	3B	131k	-	-	🤗	-	View
MiniCPM-V 4.6 1.3B	OpenBMB	4	1.3B	262k	-	-	🤗	-	View
Jamba Reasoning 3B	AI21 Labs	4	3B	262k	-	-	🤗	-	View

Comparisons of Tiny Open Source AI Models (≤4B)

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