Comparación de modelos: análisis de inteligencia, rendimiento y precio

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

Probar

Haz clic en cualquier modelo para ver métricas detalladas. Para obtener más información, incluida nuestra metodología, consulta nuestras preguntas frecuentes.

Inteligencia

Updated

Claude Opus 5.5 (max with fallback) y

Claude Opus 5.5 (xhigh with fallback) son los modelos con mayor inteligencia, seguidos de

Claude Opus 5.5 (high with fallback) y

Claude Fable 5.1 (max with fallback).

Velocidad de salida (tokens/s)

Celeris-1 (1628 t/s) y

Mercury 2.5 (843 t/s) son los modelos más rápidos, seguidos de

Mercury 2 y

Gemini 2.5 Flash-Lite.

Latencia (segundos)

Gemini 2.5 Flash-Lite (Non-reasoning) (0.30s) y

North Mini Code (0.39s) son los modelos con menor latencia, seguidos de

Command A+ y

Gemini 2.5 Flash (Non-reasoning).

Precio ($ por millón de tokens)

North Mini Code ($0.00) y

Gemma 3 4B ($0.00) son los modelos más económicos, seguidos de

Gemma 3 27B y

Phi-4 Multimodal.

Ventana de contexto

Llama 4 Scout (10M) y

Grok 4.20 0309 (2M) son los modelos con las ventanas de contexto más grandes, seguidos de

Gemini 1.5 Pro (May) y

Grok 4.1 Fast.

Aspectos destacados

Updated

Inteligencia

Artificial Analysis Intelligence Index · Higher is better

Velocidad

Output tokens per second · Higher is better

Costo por tarea

Weighted average cost (USD) per Intelligence Index task · Lower is better

InteligenciaUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.3.2 incorporates 10 evaluations: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1

Artificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, 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.

Artificial Analysis Intelligence Index by Open Weights / Proprietary

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.

Índices de capacidades

Miden el rendimiento de modelos en capacidades e industrias específicas

Artificial Analysis Finance & Accounting Index

Incorporates 7 evaluations: AA-Omniscience, GDPval-AA v2.1, AA-Briefcase v1.1, Humanity's Last Exam, AutomationBench-AA, AA-LCR v1.1, GDP.pdf · Higher is better

Benchmarks

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better

AA-Briefcase v1.1Updated

Agentic knowledge work, (Elo-500)/2000

GDPval-AA v2.1Updated

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

AutomationBench-AAUpdated

Agentic SaaS workflows

Terminal-Bench 4.0New

Agentic coding & terminal use

SciCode

Coding

Humanity's Last Exam

Reasoning & knowledge

GDP.pdfNew

Professional document reasoning, All-pass

CritPtUnder review

Physics reasoning

AA-Omniscience Accuracy

Knowledge

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

AA-LCR v1.1

Long context reasoning

Harvey LAB-AA

Legal agentic work, criterion pass rate

EnterpriseOps-Gym-AA

Agentic business operations

Terminal-Bench-Science 0.1New

Agentic scientific research workflows in a terminal

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

𝜏³-Banking

Agentic tool use

ITBench-AA

Kubernetes incident root-cause analysis

MMMU-Pro

Visual reasoning

MLCR-AANew

Medical long context reasoning

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

AA-Briefcase v1.1Updated

AA-Briefcase Elo

AA-Briefcase v1.1 is an agentic knowledge work benchmark developed by Artificial Analysis. AA-Briefcase Elo is a combined metric that aggregates rubric pass rate, analytical quality Elo and presentation Elo · Higher is better

AA-Briefcase Elo is a combined metric that aggregates analytical quality Elo, presentation Elo, and rubric pass rate, with rubric performance converted into Elo via synthetic head-to-head matches. Elo and 95% confidence interval bounds are clamped at 0.

AA-Omniscience

AA-Omniscience Index

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

Índice de apertura

Artificial Analysis Openness Index: Score

Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)

Comparaciones del Índice de Inteligencia

Intelligence Index vs. Cost per Intelligence Index Task

Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task

Most attractive quadrant

Pareto line

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

Uso de tokens

Output Tokens per Intelligence Index Task

Weighted average number of output tokens used to run one task in the Artificial Analysis Intelligence Index

The number of tokens required per Intelligence Index task. This is calculated by multiplying the output tokens per eval by the relative weights of each benchmark in the Intelligence Index, then dividing by task count (excluding repeats).

Costo

Cost per Intelligence Index Task

Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better

Cost to Run Artificial Analysis Intelligence Index

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index

The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input, cache hit, cache write, reasoning, and answer token prices and the number of tokens used across evaluations (excluding repeats).

Pricing: Cache Hit, Input, and Output

Price (USD per M Tokens)

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

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).

Velocidad

Medida por la velocidad de salida (tokens por segundo)

Output Speed

Output tokens per second · Higher is better

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Figures represent performance of the model's first-party API or the median across providers where a first-party API is not available.

Time per Intelligence Index Task

Weighted average decode time (minutes) per task; excludes TTFT and overhead time · Lower is better

The weighted average time (seconds) per Artificial Analysis Intelligence Index task. This is calculated by dividing output tokens per task by output speed, weighted by the relative weights of each benchmark in the Intelligence Index.

Latencia

Medida por el tiempo (segundos) hasta el primer token

Latency: Time To First Answer Token

Seconds to first answer token received · Accounts for reasoning model 'thinking' time

Time to first answer token received, in seconds, after API request sent. For reasoning models, this includes the 'thinking' time of the model before providing an answer. For models which do not support streaming, this represents time to receive the completion.

Tiempo de respuesta de extremo a extremo

Seconds to output 500 tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed

End-to-End Response Time

Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better

Seconds to receive a 500 token response. Key components:

Input time: Time to receive the first response token
Thinking time (only for reasoning models): Time reasoning models spend outputting tokens to reason prior to providing an answer. Amount of tokens based on the average reasoning tokens across a diverse set of 60 prompts (methodology details).
Answer time: Time to generate 500 output tokens, based on output speed

Figures represent performance of the model's first-party API or the median across providers where a first-party API is not available.

Tamaño del modelo (solo modelos de pesos abiertos)

Model Size: Total and Active Parameters

Comparison between total model parameters and parameters active during inference (billions)

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.

Preguntas frecuentes

Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) lidera actualmente el Índice de Inteligencia de Artificial Analysis con una puntuación de 58, entre 172 modelos evaluados.

Los mejores modelos de IA según el Índice de Inteligencia son: 1. Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) (58), 2. Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) (56), 3. Claude Opus 5.5 (Adaptive Reasoning, High Effort, Default Fallback) (54), 4. Claude Fable 5.1 (Adaptive Reasoning, Max Effort, Default Fallback) (53) y 5. Claude Fable 5.1 (Adaptive Reasoning, Xhigh Effort, Default Fallback) (53).

Celeris-1 es el más rápido, con 1,628.3 tokens por segundo, seguido de Mercury 2.5 (843.0 t/s) y Mercury 2 (772.3 t/s).

Llama 3.1 Instruct 8B es el más económico, con $0.02 por millón de tokens (precio combinado), seguido de Granite 4.2 3B ($0.02) y Nova Micro ($0.03).

Gemini 2.5 Flash-Lite (Non-reasoning) tiene el menor tiempo hasta el primer token, con 0.30s, seguido de North Mini Code (0.39s) y Command A+ (0.42s).

MiMo-V2.6-Pro es el modelo con pesos abiertos mejor clasificado, con una puntuación de 46 en el Índice de Inteligencia. Hay 70 modelos con pesos abiertos de un total de 172 evaluados.

Los mejores modelos de IA con pesos abiertos según el Índice de Inteligencia son: 1. MiMo-V2.6-Pro (46), 2. GLM-5.3 (max) (45) y 3. Kimi K3 (max) (44).

Claude Opus 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) lidera entre 151 modelos de razonamiento con una puntuación de 58 en el Índice de Inteligencia. Los modelos de razonamiento usan pensamiento extendido para resolver problemas complejos antes de responder.

Los modelos se comparan en múltiples dimensiones, como inteligencia (calidad), precio, velocidad de salida (tokens por segundo), latencia (tiempo hasta el primer token), tiempo de respuesta de extremo a extremo y tamaño de la ventana de contexto. Las métricas de rendimiento se miden directamente mediante prompts estandarizados en 673 modelos.

Haz clic en el nombre o la fila de cualquier modelo en los gráficos para ver su página específica, con métricas detalladas y comparaciones directas con modelos similares. También puedes usar el selector de modelos para personalizar cuáles aparecen en cada gráfico. Ver el ranking

Comparación de modelos: análisis de inteligencia, rendimiento y precio

InteligenciaUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Artificial Analysis Intelligence Index

Open Weights

Intelligence Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

AA-Briefcase v1.1Updated

AA-Briefcase Elo

AA-Briefcase Elo

AA-Omniscience

AA-Omniscience Index

AA-Omniscience Index

Índice de apertura

Artificial Analysis Openness Index: Score

Comparaciones del Índice de Inteligencia

Intelligence Index vs. Cost per Intelligence Index Task

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

Uso de tokens

Output Tokens per Intelligence Index Task

Output Tokens per Intelligence Index Task

Costo

Cost per Intelligence Index Task

Cost per Intelligence Index Task

Cost to Run Artificial Analysis Intelligence Index

Cost to Run Artificial Analysis Intelligence Index

Pricing: Cache Hit, Input, and Output

Cache Hit

Ventana de contexto

Context Window

Context Window for RAG

Context Window

Velocidad

Output Speed

Output Speed

Model Performance Representation

Time per Intelligence Index Task

Time per Intelligence Index Task

Latencia

Latency: Time To First Answer Token

Time to First Answer Token

Tiempo de respuesta de extremo a extremo

End-to-End Response Time

End-to-End Response Time

Model Performance Representation

Tamaño del modelo (solo modelos de pesos abiertos)

Model Size: Total and Active Parameters

Total Parameters

Active Parameters at Inference Time

Preguntas frecuentes

¿Cuál es el modelo de IA más inteligente?

¿Cuáles son los mejores modelos de IA?

¿Cuál es el modelo de IA más rápido?

¿Cuál es el modelo de IA más económico?

¿Qué modelo de IA tiene la menor latencia?

¿Cuál es el mejor modelo de IA con pesos abiertos?

¿Cuáles son los mejores modelos de IA con pesos abiertos?

¿Cuál es el mejor modelo de razonamiento?

¿Cómo se comparan los modelos de IA en Artificial Analysis?

¿Cómo comparo un modelo específico con otros?