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.

Granite 4.2 3B and

Nemotron 3 Nano 4B are the highest intelligence Tiny open source models, defined as those with ≤4B parameters, with Artificial Analysis Intelligence Index scores of 4.7 and 1.8 respectively.

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)

IntelligenceUpdated

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

No data available

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

No data available

MMMU-Pro

Visual reasoning

AA-PersonalityBench: Expressiveness ScoreNew

Rich, varied, well-crafted language

No data available

AA-PersonalityBench: Organization ScoreNew

Well-structured, scannable, answer-first

No data available

AA-PersonalityBench: Transparency ScoreNew

Truthful, acknowledges uncertainty, non-sycophantic

No data available

AA-PersonalityBench: Slop ScoreNew

Use of filler and generic language

No data available

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 (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 vs. Active Parameters

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

Most attractive quadrant

Intelligence Index vs. Total Parameters

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.

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
Granite 4.2 3B	IBM	5	3B	131k	-	-	🤗	-	View
NVIDIA Nemotron 3 Nano 4B	NVIDIA	2	4.0B	262k	-	-	🤗	-	View
Gemma 3 270M	Google	-	0.3B	32k	-	-	🤗	-	View
Qwen3.5 2B (Non-reasoning)	Alibaba	-	2.3B	262k	-	-	🤗	-	View
Granite 4.0 Micro	IBM	-	3B	128k	-	-	🤗	-	View
G9v3-3B	AI9Stars	-	3B	131k	-	-	🤗	-	View
K2 Horizon 3.7B	Institute of Foundation Models	-	3.7B	524k	-	-	🤗	-	View
LFM2.5-VL-1.6B	Liquid AI	-	1.6B	32k	-	-	🤗	-	View
Qwen3.5 0.8B (Non-reasoning)	Alibaba	-	0.9B	262k	-	-	🤗	-	View
MiniCPM-V 4.6 1.3B	OpenBMB	-	1.3B	262k	-	-	🤗	-	View
LFM2.5-1.2B-Instruct	Liquid AI	-	1.2B	32k	-	-	🤗	-	View
Granite 4.0 350M	IBM	-	0.3B	33k	-	-	🤗	-	View

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

Openness

Intelligence

Total Parameters

Openness

Artificial Analysis Openness Index: Score

IntelligenceUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index

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