Comparação de modelos pequenos de IA de pesos abertos (4B-40B)

Modelos de IA de pesos abertos com 4B a 40B parâmetros.

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: AlibabaQwen3.6 27B e Logo: AlibabaQwen3.5 27B são os modelos com maior inteligência entre os modelos pequenos de pesos abertos, definidos como aqueles com 4B-40B parâmetros, seguidos por Logo: AlibabaQwen3.6 35B A3B e Logo: AI9StarsG9v3-39A5B.

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

Agentic SaaS workflows

No data available

Legal agentic work, criterion pass rate

Agentic business operations

Instruction following

Long-horizon agentic tasks

No data available

Kubernetes incident root-cause analysis

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
Qwen3.6 27B (Reasoning)
Logo: AlibabaAlibaba
37
27.8B
262k
$0.9
59
DeepInfraAlibaba CloudSiliconFlow
+2
Qwen3.6 35B A3B (Reasoning)
Logo: AlibabaAlibaba
32
36B
3B ativos durante a inferência
262k
$0.4
140
DeepInfraNovitaParasail
+5
G9v3-39A5B
Logo: AI9StarsAI9Stars
31
39B
5B ativos durante a inferência
131k
-
-
AI9Stars
Qwen3.6 27B (Non-reasoning)
Logo: AlibabaAlibaba
30
27.8B
262k
$0.9
57
Alibaba CloudDeepInfraGroqNovita
Gemma 4 31B (Reasoning)
Logo: GoogleGoogle
29
30.7B
256k
-
35
ParasailDeepInfraLightning AI
+10
Gemma 4 26B A4B (Reasoning)
Logo: GoogleGoogle
26
25.2B
3.8B ativos durante a inferência
256k
$0.1
-
MakoraClarifaiGMI
+5
Qwen3.6 35B A3B (Non-reasoning)
Logo: AlibabaAlibaba
24
36B
3B ativos durante a inferência
262k
$0.6
163
DeepInfraGMIScaleway
+4
Qwen3.5 35B A3B (Non-reasoning)
Logo: AlibabaAlibaba
24
36B
3B ativos durante a inferência
262k
$0.4
170
DeepInfraAlibaba Cloud
Gemma 4 12B (Reasoning)
Logo: GoogleGoogle
22
12B
256k
$0.1
111
SiliconFlow
Gemma 4 31B (Non-reasoning)
Logo: GoogleGoogle
22
30.7B
256k
$0.2
68
NovitaCerebrasParasail
+5
Qwen3.5 9B (Reasoning)
Logo: AlibabaAlibaba
21
9.7B
262k
$0.1
74
Together AISiliconFlow
Apriel-v1.6-15B-Thinker
Logo: ServiceNowServiceNow
21
15B
128k
-
-
Together AI