Comparación de modelos de IA muy pequeños con pesos abiertos (≤4B)

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

Modelos de IA con pesos abiertos que tienen 4B parámetros o menos. Suelen ser los modelos más pequeños en demanda de recursos.

Consideramos que un modelo tiene pesos abiertos (también denominado comúnmente «open source») cuando sus pesos están disponibles para descargar. Esto permite alojarlo en infraestructura propia y personalizarlo, por ejemplo, mediante ajuste fino.

Para más detalles sobre nuestra metodología, consulta nuestras FAQs.

G9v3-3B y

Qwen3 4B 2507 son los modelos muy pequeños con pesos abiertos, definidos como aquellos con ≤4B parámetros de mayor inteligencia, seguidos por

MiniCPM5-1B y

MiniCPM5-1B.

Aspectos destacados

Openness

Artificial Analysis Openness Index · Higher is better

Inteligencia

Artificial Analysis Intelligence Index · Higher is better

Parámetros totales

Parámetros entrenables en miles de millones

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

Inteligencia

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-Briefcase

Agentic knowledge work, Elo

No data available

AutomationBench-AA

Agentic SaaS workflows

No data available

Harvey LAB-AA

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.

Tamaño

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

Pareto line

Reasoning models are indicated by a lightbulb icon

Intelligence Index vs. Total Parameters

Artificial Analysis Intelligence Index · Size in parameters (billions)

Most attractive quadrant

Pareto line

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.

Ventana de contexto

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).

Más detalles

							Pesos		Benchmarks de proveedores
G9v3-3B	AI9Stars	16	3B	131k	-	-	🤗		Ver
MiniCPM5-1B (Reasoning)	OpenBMB	12	1B	128k	-	-	🤗	-	Ver
MiniCPM5-1B (Non-reasoning)	OpenBMB	12	1B	128k	-	-	🤗	-	Ver
Nanbeige4.1-3B	Nanbeige	11	3.9B	256k	-	-	🤗	-	Ver
NVIDIA Nemotron 3 Nano 4B	NVIDIA	9	4.0B	262k	-	-	🤗	-	Ver
Qwen3.5 2B (Reasoning)	Alibaba	7	2.3B	262k	-	-	🤗	-	Ver
Ministral 3 3B	Mistral	6	3B	256k	$0.1	247	🤗		Ver
Phi-4 Mini Instruct	Microsoft	6	3.8B	128k	-	45	🤗		Ver
Qwen3.5 2B (Non-reasoning)	Alibaba	6	2.3B	262k	-	-	🤗	-	Ver
Qwen3.5 0.8B (Reasoning)	Alibaba	5	0.9B	262k	-	-	🤗	-	Ver
Granite 4.1 3B	IBM	5	3B	131k	-	-	🤗	-	Ver
MiniCPM-V 4.6 1.3B	OpenBMB	4	1.3B	262k	-	-	🤗	-	Ver

Comparación de modelos de IA muy pequeños con pesos abiertos (≤4B)

Openness

Inteligencia

Parámetros totales

Openness

Artificial Analysis Openness Index: Score

Inteligencia

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index

Intelligence Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

Tamaño

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

Ventana de contexto

Context Window

Context Window for RAG

Context Window

Más detalles