Comparação de modelos muito pequenos de IA de pesos abertos (≤4B)

Modelos de IA de pesos abertos com até 4B parâmetros. Geralmente, são os menores modelos em termos de demanda de recursos.

Os modelos são considerados de pesos abertos (também chamados frequentemente de código aberto) quando seus pesos estão disponíveis para download. Isso permite a auto-hospedagem em sua própria infraestrutura e a personalização do modelo, por exemplo, por meio de ajuste fino.

Para mais detalhes sobre nossa metodologia, consulte as perguntas frequentes.

Logo: AI9StarsG9v3-3B e Logo: AlibabaQwen3 4B 2507 são os modelos com maior inteligência entre os modelos muito pequenos de pesos abertos, definidos como aqueles com ≤4B parâmetros, seguidos por Logo: OpenBMBMiniCPM5-1B e Logo: OpenBMBMiniCPM5-1B.

Destaques

Artificial Analysis Openness Index · Higher is better
Artificial Analysis Intelligence Index · Higher is better
Parâmetros treináveis em bilhões

Abertura

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

Inteligência

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

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

Agentic tool use

Agentic coding & terminal use

Coding

Reasoning & knowledge

Scientific reasoning

Physics reasoning

Long context reasoning

Agentic knowledge work, Elo

No data available

Agentic SaaS workflows

No data available

Legal agentic work, criterion pass rate

No data available

Agentic business operations

No data available

Instruction following

Long-horizon agentic tasks

No data available

Kubernetes incident root-cause analysis

No data available

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.

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.

Tamanho

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

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.

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. Total Parameters

Artificial Analysis Intelligence Index · Size in parameters (billions)
Most attractive quadrant
Pareto line
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.

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.

Janela 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).

Mais detalhes

Pesos
Benchmarks de provedores
G9v3-3B
Logo: AI9StarsAI9Stars
16
3B
131k
-
-
AI9Stars
MiniCPM5-1B (Reasoning)
Logo: OpenBMBOpenBMB
12
1B
128k
-
-
-
MiniCPM5-1B (Non-reasoning)
Logo: OpenBMBOpenBMB
12
1B
128k
-
-
-
Nanbeige4.1-3B
Logo: NanbeigeNanbeige
11
3.9B
256k
-
-
-
NVIDIA Nemotron 3 Nano 4B
Logo: NVIDIANVIDIA
9
4.0B
262k
-
-
-
Qwen3.5 2B (Reasoning)
Logo: AlibabaAlibaba
8
2.3B
262k
-
-
-
Ministral 3 3B
Logo: MistralMistral
7
3B
256k
$0.1
206
Amazon BedrockMistral
Phi-4 Mini Instruct
Logo: MicrosoftMicrosoft
6
3.8B
128k
-
43
Microsoft Azure
Qwen3.5 2B (Non-reasoning)
Logo: AlibabaAlibaba
6
2.3B
262k
-
-
-
Qwen3.5 0.8B (Reasoning)
Logo: AlibabaAlibaba
5
0.9B
262k
-
-
-
Granite 4.1 3B
Logo: IBMIBM
5
3B
131k
-
-
-
MiniCPM-V 4.6 1.3B
Logo: OpenBMBOpenBMB
4
1.3B
262k
-
-
-