Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) está entre los modelos líderes en inteligencia, pero tiene un precio algo elevado en comparación con otros modelos de precio similar. También es más rápido que el promedio; sin embargo, es algo verboso. El modelo admite entrada de texto e imágenes, genera texto y tiene una ventana de contexto de 1M tokens.

Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) obtiene 56 puntos en el Índice de Inteligencia de Artificial Analysis, lo que lo sitúa muy por encima del promedio entre modelos comparables (con una mediana de 25). Al evaluar el Índice de Inteligencia, generó 100M tokens, lo que es algo verboso frente a la mediana de 88M.

El precio de Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) es de $4.00 por 1 M de tokens de entrada (algo elevado; mediana: $2.00) y $20.00 por 1 M de tokens de salida (algo elevado; mediana: $10.00). En total, evaluar Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) en el Índice de Inteligencia costó $4056.65.

Con 72 tokens por segundo, Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) es más rápido que el promedio (71).

Razonamiento	Sí Esta página muestra la versión con razonamiento de este modelo. También puede existir una variante sin razonamiento.
Modalidad de entrada	Admite: texto e imágenes
Modalidad de salida	Admite: texto
Ventana de contexto	1M ~1500 páginas A4 con fuente Arial de 12 puntos

Las métricas se comparan con modelos de la misma clase:

Modelos sin razonamiento → se comparan solo con otros modelos sin razonamiento
Modelos de razonamiento → se comparan tanto con modelos de razonamiento como sin razonamiento
Modelos de pesos abiertos → se comparan solo con otros modelos de pesos abiertos de la misma categoría de tamaño:

Muy pequeño: ≤4B parámetros
Pequeño: 4B–40B parámetros
Mediano: 40B–150B parámetros
Grande: >150B parámetros

Modelos propietarios → se comparan con modelos propietarios y de pesos abiertos del mismo rango de precio mediante una proporción combinada de precios de entrada/salida de 3:1:

<$0.15 por 1 M de tokens
$0.15–$1 por 1 M de tokens
>$1 por 1 M de tokens

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

CritPt

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

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.

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.

Preguntas frecuentes

Preguntas comunes sobre Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback)

Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) se lanzó el 17 de septiembre de 2026.

Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) fue creado por Anthropic.

Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) obtiene 56 puntos en el Índice de Inteligencia de Artificial Analysis, lo que lo sitúa muy por encima del promedio entre otros modelos de razonamiento en un rango de precio similar (mediana: 25).

Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) genera 72.5 tokens de salida por segundo (según la API de Anthropic), un valor por encima del promedio frente a otros modelos de razonamiento en un rango de precio similar (mediana: 71.0 t/s).

Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) tiene un tiempo hasta el primer token (TTFT) de 165.20 s (según la API de Anthropic), un valor en el extremo superior frente a otros modelos de razonamiento en un rango de precio similar (mediana: 3.86 s).

Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) cuesta $4.00 por 1 M de tokens de entrada (algo mayor que el promedio; mediana: $2.00) y $20.00 por 1 M de tokens de salida (algo mayor que el promedio; mediana: $10.00), según la API de Anthropic.

Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) cuesta $4.00 por 1 M de tokens de entrada y $20.00 por 1 M de tokens de salida (según la API de Anthropic). Con una tarifa combinada (proporción 7:2:1 entre aciertos de caché, entrada y salida), equivale a $2.94 por 1 M de tokens. El precio puede variar según el proveedor. Comparar precios de proveedores

Al evaluarlo en el Índice de Inteligencia, Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) generó 100M tokens de salida, un valor algo mayor que el promedio frente a otros modelos de razonamiento en un rango de precio similar (mediana: 88M).

Sí, Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) es un modelo de razonamiento. Usa pensamiento extendido o razonamiento en cadena para resolver problemas complejos antes de dar una respuesta.

Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) admite entrada de texto e imágenes.

Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) admite salida de texto.

Sí, Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) admite imágenes como entrada y puede analizarlas, describirlas y responder preguntas sobre ellas.

Sí, Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) es multimodal. Puede procesar entradas de texto e imágenes y generar salidas de texto.

Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) tiene una ventana de contexto de 1.0M tokens. Esto determina cuánto texto e historial de conversación puede procesar el modelo en una sola solicitud.

No, Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) es propietario. Los pesos del modelo no están disponibles públicamente.

Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) es un modelo propietario y Anthropic no ha revelado su tamaño ni su cantidad de parámetros.

Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) obtiene 56 puntos en el Índice de Inteligencia de Artificial Analysis. Este benchmark compuesto evalúa los modelos en razonamiento, conocimiento, matemáticas y programación.

Sí, Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) está disponible mediante API a través de 1 proveedor. Comparar proveedores de API

Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) está disponible a través de 1 proveedor de API. Comparar proveedores

Análisis de inteligencia, rendimiento y precio de Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback)

Resumen del modelo

InteligenciaUpdated

Resumen comparativo

Especificaciones técnicas

212 modelos en esta clase

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

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

Preguntas frecuentes

¿Cuándo se lanzó Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback)?

¿Quién creó Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback)?

¿Qué tan inteligente es Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback)?

¿Qué tan rápido es Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback)?

¿Cuál es la latencia de Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback)?

¿Cuánto cuesta Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback)?

¿Cuál es el precio de la API de Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback)?

¿Qué tan verboso es Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback)?

¿Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) es un modelo de razonamiento?

¿Qué modalidades de entrada admite Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback)?

¿Qué modalidades de salida admite Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback)?

¿Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) puede procesar imágenes?

¿Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) es multimodal?

¿Cuál es la ventana de contexto de Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback)?

¿Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) es de código abierto?

¿Cuántos parámetros tiene Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback)?

¿Cómo rinde Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) en los benchmarks?

¿Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) está disponible mediante API?

¿Dónde puedo usar Claude Opus 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback)?