Comparação de modelos de pesos abertos

Comparação e análise de modelos de IA de pesos abertos em métricas importantes de desempenho, como qualidade, desempenho, velocidade de inferência, janela de contexto, número de parâmetros e detalhes da licença.

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: KimiKimi K3 (max) e Logo: AlibabaQwen3.8 2.4T A95B são os modelos com maior inteligência entre os modelos de pesos abertos, seguidos por Logo: DeepSeekDeepSeek V4 Pro 0813 (max) e Logo: Z AIGLM-5.2 (max).

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

Progresso dos modelos de pesos abertos

Progress in Open Weights vs. Proprietary Intelligence

Artificial Analysis Intelligence Index v4.1.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR
Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.1.1 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.

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if commercial use is limited by conditions, and as 'Non-commercial' if the license prohibits commercial use.

Evolução da inteligência dos modelos de linguagem de pesos abertos por laboratório

Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.1.1 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.

Evolução da inteligência dos modelos de pesos abertos por tamanho

Artificial Analysis Intelligence Index v4.1.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR
Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.1.1 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.

  • Tiny: Less than or equal to 4B parameters. These are usually the smallest models in terms of resource demand.
  • Small: Less than 40B parameters.
  • Medium: Between 40B-150B parameters.
  • Large: Over 150B parameters.

Inteligência

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.1.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR
Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.1.1 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
See more

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

Agentic tool use

Agentic coding & terminal use

Coding

Reasoning & knowledge

Scientific reasoning

Physics reasoning

Knowledge

1 - hallucination rate

AA-LCRUpdated

Long context reasoning

Agentic knowledge work, Elo

Agentic SaaS workflows

Legal agentic work, criterion pass rate

Agentic business operations

Quantitative analysis on spreadsheets & documents

Instruction following

Long-horizon agentic tasks

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 v4.1.1 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

Intelligence Index por tamanho do modelo

Artificial Analysis Intelligence Index v4.1.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR
Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.1.1 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.

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 v4.1.1 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 v4.1.1 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
Kimi K3 (max)
Logo: KimiKimi
60
2.8T
104B ativos durante a inferência
1M
$2.3
36
DatabricksFireworksParasail
+12
Qwen3.8 2.4T A95B
Logo: AlibabaAlibaba
58
2.4T
95B ativos durante a inferência
984k
$1.2
21
DigitalOceanTogether AIBitdeer AI
+3
DeepSeek V4 Pro 0813 (Reasoning, Max Effort)
Logo: DeepSeekDeepSeek
53
1.6T
49B ativos durante a inferência
1M
$0.7
65
NovitaBasetenGMI
+5
Qwen3.8 27B (xhigh)
Logo: AlibabaAlibaba
52
27B
256k
$0.4
53
Self-hostedDeepInfraSelf-hosted
+2
Motif 3
Logo: Motif TechnologiesMotif Technologies
47
314B
13.2B ativos durante a inferência
262k
-
-
Não disponível
-
MiniMax-M3
Logo: MiniMaxMiniMax
45
428B
23B ativos durante a inferência
1M
$0.2
126
Together AIBitdeer AICoreWeave
+9
Inkling (xhigh)
Logo: Thinking MachinesThinking Machines
42
975B
41B ativos durante a inferência
1M
$0.7
50
BasetenTogether AIDeepInfra
+2
Nemotron 3 Ultra 550B A55B (Reasoning)
Logo: NVIDIANVIDIA
38
550B
55B ativos durante a inferência
262k
$0.5
170
Self-hostedDeepInfraTogether AI
+6
Solar Open2 250B
Logo: UpstageUpstage
37
250B
15B ativos durante a inferência
1M
-
-
-
Muse Glimmer (high)
Logo: MetaMeta
35
30B
131k
$0.2
109
DeepInfraTogether AI
A.X-K2
Logo: SK TelecomSK Telecom
35
692B
33B ativos durante a inferência
262k
-
-
-
K-EXAONE 2.0 0803
Logo: LG AI ResearchLG AI Research
31
750B
37B ativos durante a inferência
262k
-
-
Não disponível
-
Mistral Medium 3.5
Logo: MistralMistral
30
128B
256k
$1.2
144
Self-hostedMistral
Nemotron 3 Super 120B A12B (Reasoning)
Logo: NVIDIANVIDIA
26
120.6B
12.7B ativos durante a inferência
1M
$0.3
142
NebiusDeepInfraCoreWeave
gpt-oss-120b (high)
Logo: OpenAIOpenAI
24
117B
5.1B ativos durante a inferência
131k
$0.2
177
NovitaGroqSelf-hosted
+16
Nemotron 3.5 Lightning
Logo: NVIDIANVIDIA
24
31.6B
3.6B ativos durante a inferência
1M
$0.1
311
Não disponível
GMIFireworksDeepInfra
+3
Command A+
Logo: CohereCohere
23
218B
25B ativos durante a inferência
192k
-
259
Cohere