This model is deprecated. We only continue performance benchmarking for the default 10k input token workload. Results for other workloads are historical and no longer updated.

OpenAI has launched a newer model, GPT-5.3 Codex (xhigh). We suggest considering it instead.

For more information, see comparison of GPT-5.3 Codex (xhigh) to other models and API provider benchmarks for GPT-5.3 Codex (xhigh).

GPT-5.2 Codex (xhigh) logo
OpenAI
•

Proprietary model

•

Released December 2025

Análisis de inteligencia, rendimiento y precio de GPT-5.2 Codex (xhigh)

Benchmarks de proveedores de API

Resumen del modelo

Inteligencia

40
Índice de Inteligencia de Artificial Analysis
3 de 4 unidades para Inteligencia.

Velocidad

N/D
Tokens de salida por segundo
Valor desconocido de 4 unidades para Velocidad.

Precio

Entrada
1,75 US$
por 1 M de tokens
Salida
14,00 US$
por 1 M de tokens
3 de 4 unidades para Precio.

Precio de acierto de caché

0,175 US$
USD por 1 M de tokens
2 de 4 unidades para Precio de acierto de caché.

Verbosidad

N/D
Tokens de salida del Índice de Inteligencia
Valor desconocido de 4 unidades para Verbosidad.

GPT-5.2 Codex (xhigh) está por encima del promedio en inteligencia, pero tiene un precio algo elevado en comparación con otros modelos de precio similar. El modelo admite entrada de texto e imágenes, genera texto y tiene una ventana de contexto de 400k tokens con conocimientos hasta agosto de 2025.

GPT-5.2 Codex (xhigh) obtiene 40 puntos en el Índice de Inteligencia de Artificial Analysis, lo que lo sitúa por encima del promedio entre modelos comparables (con una mediana de 33).

El precio de GPT-5.2 Codex (xhigh) es de $1.75 por 1 M de tokens de entrada (moderado; mediana: $1.75) y $14.00 por 1 M de tokens de salida (algo elevado; mediana: $10.00).

RazonamientoSí

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

Fecha límite de conocimiento31 ago 2025
Ventana de contexto400k
~600 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

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

Inteligencia

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.

Artificial Analysis Intelligence Index by Open Weights / Proprietary

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.

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if the weights are available but commercial use is limited (typically requires obtaining a paid license).

Benchmarks

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better
GDPval-AA v2

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

𝜏³-Banking

Agentic tool use

Terminal-Bench v2.1

Agentic coding & terminal use

SciCode

Coding

Humanity's Last Exam

Reasoning & knowledge

GPQA Diamond

Scientific reasoning

CritPt

Physics reasoning

AA-Omniscience Accuracy

Knowledge

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

AA-LCR

Long context reasoning

AA-Briefcase

Agentic knowledge work, Elo

AutomationBench-AA

Agentic SaaS workflows

Harvey LAB-AA

Legal agentic work, criterion pass rate

EnterpriseOps-Gym-AANew

Agentic business operations

IFBench

Instruction following

APEX-Agents-AA

Long-horizon agentic tasks

ITBench-AA

Kubernetes incident root-cause analysis

MMMU-Pro

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.

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.
Reasoning models are indicated by a lightbulb icon

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
Reasoning models are indicated by a lightbulb icon

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.

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.

Costo

Cost per Intelligence Index Task

Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better
Reasoning models are indicated by a lightbulb icon

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.

Cost to Run Artificial Analysis Intelligence Index

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index
Reasoning models are indicated by a lightbulb icon

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)
Reasoning models are indicated by a lightbulb icon

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.

Price per token included in the request/message sent to the API, represented as USD per million Tokens.

The blended cache price shown here uses cache hit price only. Other caching costs differ by provider:

  • Anthropic: charges a separate cache write fee, with different rates for 5-minute and 1-hour TTLs (1-hour TTL is more expensive).
  • Google (Vertex/Gemini): charges a per-hour cache storage fee in addition to cache hit pricing. Some providers also use tiered pricing for prompts above 200K tokens.
  • OpenAI, DeepSeek, others: typically charge only cache hit pricing with no write or storage fee.

See Prompt Caching for the full breakdown.

Price per token generated by the model (received from the API), represented as USD per million Tokens.

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Ventana 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).

Preguntas frecuentes

Preguntas comunes sobre GPT-5.2 Codex (xhigh)

GPT-5.2 Codex (xhigh) se lanzó el 11 de diciembre de 2025.

GPT-5.2 Codex (xhigh) fue creado por OpenAI.

GPT-5.2 Codex (xhigh) obtiene 40 puntos (estimados) en el Índice de Inteligencia de Artificial Analysis, lo que lo sitúa por encima del promedio entre otros modelos de razonamiento en un rango de precio similar (mediana: 33).

GPT-5.2 Codex (xhigh) cuesta $1.75 por 1 M de tokens de entrada (mejor que el promedio; mediana: $1.75) y $14.00 por 1 M de tokens de salida (algo mayor que el promedio; mediana: $10.00), según la mediana de los proveedores que sirven el modelo.

GPT-5.2 Codex (xhigh) cuesta $1.75 por 1 M de tokens de entrada y $14.00 por 1 M de tokens de salida (según la mediana de los proveedores que sirven el modelo). Con una tarifa combinada (proporción 7:2:1 entre aciertos de caché, entrada y salida), equivale a $1.87 por 1 M de tokens. El precio puede variar según el proveedor. Comparar precios de proveedores

Sí, GPT-5.2 Codex (xhigh) es un modelo de razonamiento. Usa pensamiento extendido o razonamiento en cadena para resolver problemas complejos antes de dar una respuesta.

GPT-5.2 Codex (xhigh) admite entrada de texto e imágenes.

GPT-5.2 Codex (xhigh) admite salida de texto.

Sí, GPT-5.2 Codex (xhigh) admite imágenes como entrada y puede analizarlas, describirlas y responder preguntas sobre ellas.

Sí, GPT-5.2 Codex (xhigh) es multimodal. Puede procesar entradas de texto e imágenes y generar salidas de texto.

GPT-5.2 Codex (xhigh) tiene una ventana de contexto de 400k tokens. Esto determina cuánto texto e historial de conversación puede procesar el modelo en una sola solicitud.

No, GPT-5.2 Codex (xhigh) es propietario. Los pesos del modelo no están disponibles públicamente.

GPT-5.2 Codex (xhigh) es un modelo propietario y OpenAI no ha revelado su tamaño ni su cantidad de parámetros.

GPT-5.2 Codex (xhigh) obtiene 40 puntos en el Índice de Inteligencia de Artificial Analysis. Este benchmark compuesto evalúa los modelos en razonamiento, conocimiento, matemáticas y programación.

La fecha límite de conocimiento de GPT-5.2 Codex (xhigh) es agosto de 2025. Sus datos de entrenamiento incluyen información hasta esa fecha.

Sí, GPT-5.2 Codex (xhigh) está disponible mediante API a través de 1 proveedor. Comparar proveedores de API

GPT-5.2 Codex (xhigh) está disponible a través de 1 proveedor de API. Comparar proveedores