{"id":6216,"date":"2025-04-15T13:04:53","date_gmt":"2025-04-15T13:04:53","guid":{"rendered":"https:\/\/www.myshirtai.com\/?p=6216"},"modified":"2025-04-15T13:04:53","modified_gmt":"2025-04-15T13:04:53","slug":"openai-%e5%8f%91%e5%b8%83-gpt-4-1-%e7%b3%bb%e5%88%97%ef%bc%9a%e7%bc%96%e7%a0%81%e3%80%81%e6%8c%87%e4%bb%a4%e6%89%a7%e8%a1%8c%e4%b8%8e%e9%95%bf%e4%b8%8a%e4%b8%8b%e6%96%87%e8%83%bd%e5%8a%9b%e5%a4%a7","status":"publish","type":"post","link":"https:\/\/www.myshirtai.com\/es\/archives\/6216","title":{"rendered":"OpenAI lanza la serie GPT-4.1: mejoras espectaculares en la codificaci\u00f3n, la ejecuci\u00f3n de instrucciones y los contextos largos"},"content":{"rendered":"

I. Introducci\u00f3n<\/h3>\n
El 15 de abril de 2025, OpenAI lanz\u00f3 oficialmente la nueva serie de modelos GPT-4.1, que incluye GPT-4.1, GPT-4.1 mini y GPT-4.1 nano. Este lanzamiento supone otro gran avance en el rendimiento, la rentabilidad y las capacidades de aplicaci\u00f3n en el mundo real de los modelos de OpenAI, especialmente en las \u00e1reas de tareas de codificaci\u00f3n, cumplimiento de instrucciones y procesamiento de contextos largos, al tiempo que proporciona a los desarrolladores mejores opciones a precios y latencias m\u00e1s bajos.<\/div>\n

GPT-4.1 mini ya est\u00e1 disponible en ShirtAI para su uso gratuito e ilimitado, a un solo clic del sitio web oficial:www.lsshirtai.com<\/a> <\/span><\/strong><\/p>\n

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Si desea llamar como API<\/strong><\/strong> GPT-4.1<\/strong> consulte el sitio web:https:\/\/coultra.blueshirtmap.com\/<\/a><\/span><\/strong><\/p>\n

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II. Salto de nivel en las destrezas de codificaci\u00f3n: reforzar toda la dimensi\u00f3n desde la generaci\u00f3n de c\u00f3digo hasta la pr\u00e1ctica de la ingenier\u00eda<\/h3>\n
En el campo de batalla central del desarrollo de software, la serie GPT-4.1 muestra un cambio cualitativo de la \"generaci\u00f3n de fragmentos de c\u00f3digo\" al \"procesamiento de ingenier\u00eda compleja\". En respuesta a las necesidades de la ingenier\u00eda de software del mundo real, el modelo alcanza una tasa de finalizaci\u00f3n de tareas de 54,6% en la prueba SWE-bench Verified, lo que supone 21% m\u00e1s que su predecesor GPT-4o, e incluso supera en 26,6 puntos porcentuales a la versi\u00f3n preliminar de GPT-4.5, a\u00fan pendiente de lanzamiento. Este avance no s\u00f3lo se refleja en la precisi\u00f3n de la l\u00f3gica del c\u00f3digo, sino tambi\u00e9n en la comprensi\u00f3n en profundidad de la base de c\u00f3digo multiling\u00fce: en la prueba de referencia de diferencias multiling\u00fces de Aider, GPT-4.1 obtuvo el doble de puntos que GPT-4o, y puede seguir con precisi\u00f3n el formato de diferencias para mostrar s\u00f3lo las l\u00edneas modificadas, y controlar de forma estable el l\u00edmite superior de los tokens de salida en 32.768, lo que reduce enormemente el n\u00famero de desarrolladores. Puede seguir con precisi\u00f3n el formato diff para dar salida s\u00f3lo a las l\u00edneas modificadas, y controlar de forma estable el l\u00edmite de tokens de salida en 32.768, lo que reduce significativamente el coste de depuraci\u00f3n de los desarrolladores. En el escenario de desarrollo front-end, la puntuaci\u00f3n manual muestra que la probabilidad de que la aplicaci\u00f3n web generada se vea favorecida en t\u00e9rminos de funcionalidad y est\u00e9tica alcanza los 80%, y la capacidad de desarrollo full-stack supera por primera vez a la mayor\u00eda de modelos de c\u00f3digo dedicados.<\/div>\n
Comparaci\u00f3n de indicadores b\u00e1sicos:<\/div>\n
\n\n\n\n\n\n\n\n\n
modelizaci\u00f3n<\/th>\nSWE-bench Verificado<\/th>\nAider Multilingual Benchmarks<\/th>\nPuntuaci\u00f3n manual de desarrollo front-end<\/th>\nL\u00edmite superior del testigo de salida<\/th>\nC\u00f3digo dif Precisi\u00f3n<\/th>\n<\/tr>\n<\/thead>\n
GPT-4.1<\/td>\n54.6%<\/td>\n11.2<\/td>\n80%<\/td>\n32768<\/td>\n53%<\/td>\n<\/tr>\n
Vista previa de GPT-4.5<\/td>\n38.0%<\/td>\n7.4<\/td>\n52%<\/td>\n16384<\/td>\n45%<\/td>\n<\/tr>\n
o3-mini-alto<\/td>\n49.3%<\/td>\n9.8<\/td>\n65%<\/td>\n16384<\/td>\n60%<\/td>\n<\/tr>\n
o1<\/td>\n41.2%<\/td>\n6.1<\/td>\n48%<\/td>\n128000<\/td>\n62%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

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 <\/p>\n

III. Avance en la ejecuci\u00f3n de \u00f3rdenes: precisi\u00f3n y fiabilidad en el procesamiento de tareas complejas<\/h3>\n
Al enfrentarse a instrucciones complejas con m\u00faltiples pasos y restricciones, GPT-4.1 ha logrado el salto de la \"coincidencia difusa\" a la \"ejecuci\u00f3n precisa\". En la prueba de referencia MultiChallenge de Scale, su puntuaci\u00f3n de cumplimiento de instrucciones alcanza los 38,3%, 10,5% m\u00e1s que la de GPT-4o; y su puntuaci\u00f3n de referencia IFEval es de 87,4%, superando con creces los 81,0% de su predecesor. El modelo refuerza especialmente las tres dificultades principales de cumplimiento de formatos (por ejemplo, estructuras anidadas XML\/YAML), instrucciones negativas (rechazo expl\u00edcito de solicitudes delicadas) y tareas ordenadas (ejecuci\u00f3n de flujos de trabajo paso a paso), y la frecuencia de ediciones no v\u00e1lidas en los escenarios dif\u00edciles de cueing cae en picado hasta los 2% desde los 9% de GPT-4o en una evaluaci\u00f3n interna de OpenAI. En m\u00faltiples rondas de di\u00e1logo, su coherencia contextual alcanza los 92%, rastreando con precisi\u00f3n los detalles requeridos en las instrucciones hist\u00f3ricas, proporcionando una fiabilidad de nivel industrial para el servicio inteligente al cliente, el flujo de trabajo automatizado y otros escenarios.<\/div>\n
Comparaci\u00f3n de indicadores b\u00e1sicos:<\/div>\n
\n\n\n\n\n\n\n\n\n
modelizaci\u00f3n<\/th>\nMultiReto<\/th>\nIFEval<\/th>\nCoherencia del di\u00e1logo a varias bandas<\/th>\nSe siguen las directrices negativas<\/th>\nPorcentaje de cumplimiento del mandato<\/th>\n<\/tr>\n<\/thead>\n
GPT-4.1<\/td>\n38.3%<\/td>\n87.4%<\/td>\n92%<\/td>\n98%<\/td>\n95%<\/td>\n<\/tr>\n
Vista previa de GPT-4.5<\/td>\n44.2%<\/td>\n81.0%<\/td>\n78%<\/td>\n89%<\/td>\n82%<\/td>\n<\/tr>\n
o3-mini-alto<\/td>\n40.1%<\/td>\n85.2%<\/td>\n88%<\/td>\n96%<\/td>\n91%<\/td>\n<\/tr>\n
o1<\/td>\n45.1%<\/td>\n87.1%<\/td>\n89%<\/td>\n97%<\/td>\n94%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

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 <\/p>\n

Innovaci\u00f3n de contexto prolongado: millones de ventanas de fichas abren nuevas posibilidades para aplicaciones multiescenario profundas.<\/h3>\n
GPT-4.1 viene de serie con una ventana de contexto de 1 mill\u00f3n de tokens, que lleva el procesamiento de texto largo a una nueva dimensi\u00f3n: puede contener unas 8 bases de c\u00f3digo React completas o 3.000 p\u00e1ginas de documentos legales, lo que resuelve por completo el punto problem\u00e1tico de \"fuera de contexto\" del modelo anterior. \" punto doloroso de los modelos anteriores. En la tarea de an\u00e1lisis de v\u00eddeo largo sin gui\u00f3n Video-MME, el modelo obtuvo 72%, una mejora de 6,7% con respecto a GPT-4o; las pruebas realizadas en el conjunto de datos de c\u00f3digo abierto Graphwalks mostraron que su precisi\u00f3n de inferencia multisalto a una escala de millones de tokens alcanz\u00f3 61,7%, superando con creces la del modelo o1 que se basa en contextos cortos (48,7%). OpenAI optimiza de forma sincr\u00f3nica la econom\u00eda de las solicitudes de contextos largos: se incluye una ventana de 1 mill\u00f3n de tokens en el precio est\u00e1ndar, se aumenta el descuento de cach\u00e9 de 50% a 75% y se reduce la latencia de respuesta de 128K tokens a 15 segundos, lo que supone 30% m\u00e1s r\u00e1pido que GPT-4.5, proporcionando una soluci\u00f3n t\u00e9cnica fundamentada para escenarios como la revisi\u00f3n legal de contratos y la auditor\u00eda de bases de c\u00f3digo a gran escala.<\/div>\n
Comparaci\u00f3n de indicadores b\u00e1sicos:<\/div>\n
\n\n\n\n\n\n\n\n\n
modelizaci\u00f3n<\/th>\nventana contextual<\/th>\nV\u00eddeo-MME sin subt\u00edtulos<\/th>\nGraphwalks Razonamiento<\/th>\nDescuento de cach\u00e9<\/th>\nRetardo de token 128K<\/th>\n<\/tr>\n<\/thead>\n
GPT-4.1<\/td>\n1,000,000<\/td>\n72.0%<\/td>\n61.7%<\/td>\n75%<\/td>\n15 segundos.<\/td>\n<\/tr>\n
Vista previa de GPT-4.5<\/td>\n128,000<\/td>\n65.3%<\/td>\n42.0%<\/td>\n50%<\/td>\n22 segundos.<\/td>\n<\/tr>\n
o3-mini-alto<\/td>\n256,000<\/td>\n68.5%<\/td>\n55.2%<\/td>\n50%<\/td>\n18 segundos.<\/td>\n<\/tr>\n
o1<\/td>\n128,000<\/td>\n64.1%<\/td>\n48.7%<\/td>\n50%<\/td>\n25 segundos.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

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V. Coste y eficacia: una mejora pragm\u00e1tica para los desarrolladores<\/h3>\n
La estrategia de \"precios escalonados + optimizaci\u00f3n del rendimiento\" de OpenAI permite a desarrolladores de todos los tama\u00f1os obtener una opci\u00f3n rentable. El modelo b\u00e1sico, GPT-4.1 nano, reduce el coste de entrada a 2 $\/mill\u00f3n de tokens y el de salida a 8 $\/mill\u00f3n de tokens, al tiempo que mantiene una ventana de un mill\u00f3n de tokens y reduce la latencia en 50% en comparaci\u00f3n con GPT-4o, lo que lo convierte en la opci\u00f3n preferida para tareas de carga ligera, como la categorizaci\u00f3n de texto y el autocompletado; el modelo de gama media, GPT-4.1 mini, supera a GPT-4o en escenarios de carga media, como la generaci\u00f3n de c\u00f3digo y los di\u00e1logos multironda, al tiempo que reduce el coste en 60%. El modelo de gama media, GPT-4.1 mini, supera a GPT-4o en la generaci\u00f3n de c\u00f3digo, los di\u00e1logos multirronda y otros escenarios de carga media, al tiempo que reduce el coste en 60%. En comparaci\u00f3n, el coste de entrada de la vista previa de GPT-4.5 asciende a 75 $\/mill\u00f3n de tokens, lo que supone s\u00f3lo 1\/25 de la relaci\u00f3n precio\/rendimiento de GPT-4.1, que es la principal raz\u00f3n de su abandono en julio de 2025, y el nuevo modelo adopta uniformemente el modelo \"GPT-4o\". Adem\u00e1s, el nuevo modelo adopta una pol\u00edtica uniforme de \"no recargo por contextos largos\", lo que cambia por completo el punto d\u00e9bil de los costes del modelo anterior cuando se trata de textos largos.<\/div>\n
Comparaci\u00f3n de indicadores b\u00e1sicos:<\/div>\n
\n\n\n\n\n\n\n\n\n\n\n
modelizaci\u00f3n<\/th>\nCoste de entrada ($ \/ mill\u00f3n de fichas)<\/th>\nCoste de producci\u00f3n ($\/mill\u00f3n de fichas)<\/th>\nRetraso (128K token)<\/th>\n<\/tr>\n<\/thead>\n
GPT-4.1 nano<\/td>\n0.10<\/td>\n0.40<\/td>\n5 segundos.<\/td>\n<\/tr>\n
GPT-4.1 mini<\/td>\n0.40<\/td>\n1.60<\/td>\n8 segundos.<\/td>\n<\/tr>\n
GPT-4.1<\/td>\n2.00<\/td>\n8.00<\/td>\n15 segundos.<\/td>\n<\/tr>\n
Vista previa de GPT-4.5<\/td>\n75.0<\/td>\n150.0<\/td>\n22 segundos.<\/td>\n<\/tr>\n
o3-mini-alto<\/td>\n1.10<\/td>\n4.40<\/td>\n18 segundos.<\/td>\n<\/tr>\n
o1<\/td>\n15.00<\/td>\n60.00<\/td>\n25 segundos.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
* :: \u00cdndice coste-rendimiento = (capacidad de codificaci\u00f3n + puntuaci\u00f3n del comando + ventana de contexto)\/(coste + latencia), cuanto mayor sea el valor, mejor<\/span><\/div>\n
\n

 <\/p>\n

Si desea utilizar GPT Plus, Claude Pro, Grok Super cuenta exclusiva oficial de pago, puede ponerse en contacto con nuestro equipo profesional (wx: abch891) si no sabe c\u00f3mo recargar su cuenta.<\/strong><\/p>\n\n\n\n\n\n\n\n\n
\n

Para m\u00e1s productos, visite<\/h4>\n<\/th>\n

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M\u00e1s informaci\u00f3n en<\/h4>\n<\/th>\n<\/tr>\n<\/thead>\n

ShirtAI - Inteligencia penetrante<\/a><\/td>\nEl Gran Modelo AIGC: el comienzo de una era de doble revoluci\u00f3n en ingenier\u00eda y ciencia - Inteligencia Penetrante<\/a><\/td>\n<\/tr>\n
Restauraci\u00f3n 1:1 de Claude y GPT Sitio web oficial - AI Cloud Native<\/a><\/td>\nLive Match App Global HD Sports Viewing Player (Recomendada) - Blueshirt Technology<\/a><\/td>\n<\/tr>\n
Servicio de tr\u00e1nsito basado en la API oficial - GPTMeta API<\/a><\/td>\nAyuda, \u00bfalguien de ustedes puede proporcionar algunos consejos sobre c\u00f3mo hacer preguntas en GPT? - Conocimientos<\/a><\/td>\n<\/tr>\n
Tienda digital global de bienes virtuales - Global SmarTone (Feng Ling Ge)<\/a><\/td>\n\u00bfQu\u00e9 tan poderoso es Claude airtfacts caracter\u00edstica que GPT al instante no huele bien? -BeepBeep<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>","protected":false},"excerpt":{"rendered":"

\u4e00\u3001\u5f15\u8a00 2025 \u5e74 4 \u6708 15 \u65e5\uff0cOpenAI \u6b63\u5f0f\u63a8\u51fa\u5168\u65b0\u7684 GPT-4.1 \u7cfb\u5217\u6a21\u578b\uff0c\u5305\u542b GPT […]<\/p>","protected":false},"author":19,"featured_media":6221,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"default","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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