Comparación de modelos de IA grandes con pesos abiertos (>150B)

Modelos de IA con pesos abiertos de más de 150B parámetros.

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.

Logo de KimiKimi K3 (max) y Logo de Z AIGLM-5.3 (max) son los modelos grandes con pesos abiertos, definidos como aquellos con >150B parámetros de mayor inteligencia, seguidos por Logo de AlibabaQwen3.8 2.4T A95B y Logo de Z AIGLM-5.3-Flash.

Aspectos destacados

Artificial Analysis Openness Index · Higher is better
Updated
Artificial Analysis Intelligence Index · Higher is better
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)

Inteligencia

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.2 incorporates 10 evaluations: AA-Briefcase, GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1
Estimate (independent evaluation forthcoming)

Artificial Analysis Intelligence Index v4.2 includes: AA-Briefcase, GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. 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 knowledge work, (Elo-500)/2000

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

Agentic tool use

Agentic coding & terminal use

Coding

Reasoning & knowledge

Professional document reasoning, All-pass

Physics reasoning

Long context reasoning

Agentic SaaS workflows

Legal agentic work, criterion pass rate

Agentic business operations

Scientific reasoning

Quantitative analysis on spreadsheets & documents

Instruction following

Long-horizon agentic tasks

Kubernetes incident root-cause analysis

Visual reasoning

While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

Artificial Analysis Intelligence Index v4.2 includes: AA-Briefcase, GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Tamaño

Model Size: Total and Active Parameters

Comparison between total model parameters and parameters active during inference

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

Artificial Analysis Intelligence Index v4.2 includes: AA-Briefcase, GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. 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

Artificial Analysis Intelligence Index v4.2 includes: AA-Briefcase, GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. 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.

Ventana de contexto

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).

Más detalles

Pesos
Benchmarks de proveedores
Kimi K3 (max)
Logo de KimiKimi
50
2.8T
104B activos en inferencia
1M
$2.3
42
FireworksFireworksModal
+13
GLM-5.3 (max)
Logo de Z AIZ AI
49
753B
40B activos en inferencia
1M
$0.9
87
ZaiSelf-hostedDeepInfra
+12
Qwen3.8 2.4T A95B
Logo de AlibabaAlibaba
47
2.4T
95B activos en inferencia
984k
$1.2
40
FireworksTogether AIDeepInfra
+3
GLM-5.3-Flash
Logo de Z AIZ AI
46
320B
18B activos en inferencia
1M
$0.1
48
ZaiBasetenNovita
+15
Qwen3.8-Flash-Next
Logo de AlibabaAlibaba
46
180B
6B activos en inferencia
256k
$0.1
83
Alibaba CloudMakora
GLM-5.2 (max)
Logo de Z AIZ AI
43
753B
40B activos en inferencia
1M
$0.9
76
WaferFriendliAIMakora
+20
DeepSeek V4 Pro 0813 (Reasoning, Max Effort)
Logo de DeepSeekDeepSeek
42
1.6T
49B activos en inferencia
1M
$0.7
71
DeepSeekGMISiliconFlow
+6
DeepSeek V4 Flash 0731 (Reasoning, Max Effort)
Logo de DeepSeekDeepSeek
41
284B
13B activos en inferencia
1M
$0.2
133
CoreWeaveWaferNebius
+14
Kimi K3 (low)
Logo de KimiKimi
39
2.8T
104B activos en inferencia
1M
$2.3
41
KimiBasetenDatabricks
Motif 3
Logo de Motif TechnologiesMotif Technologies
38
314B
13.2B activos en inferencia
262k
-
-
No disponible
-
K2 Horizon 375B A23B
Logo de MBZUAI Institute of Foundation ModelsMBZUAI Institute of Foundation Models
38
375B
23B activos en inferencia
524k
-
-
-
DeepSeek V4 Pro (Reasoning, Max Effort)
Logo de DeepSeekDeepSeek
36
1.6T
49B activos en inferencia
1M
$0.2
73
DeepInfraMicrosoft AzureNovita
+8