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OpenAI: GPT-5.4 Nano (batch)

openai/gpt-5.4-nano:batch

Comparar

GPT-5.4 nano is the most lightweight and cost-efficient variant of the GPT-5.4 family, optimized for speed-critical and high-volume tasks. It supports text and image inputs and is designed for low-latency use cases such as classification, data extraction, ranking, and sub-agent execution.

The model prioritizes responsiveness and efficiency over deep reasoning, making it ideal for pipelines that require fast, reliable outputs at scale. GPT-5.4 nano is well suited for background tasks, real-time systems, and distributed agent architectures where minimizing cost and latency is essential.

Modalidades
Precio de entrada / salida
0,10 US$ / 0,625 US$por 1M
Contexto
400K
Publicado
17 mar 2026
Fecha de corte del conocimiento
ago 2025
Comparar
ProveedoresPreciosRendimientoTiempo de actividadPruebas de rendimientoAplicacionesActividadPreguntas frecuentesExplorar

Proveedores

Distintas empresas alojan el mismo modelo. OpenRouter dirige su solicitud a una de ellas según el modo de enrutamiento que elija: Equilibrado (precio + velocidad), Nitro (el más rápido) o Floor (el más barato).

Precios

El precio medio que los clientes pagan realmente por este modelo, junto a los precios que publican los proveedores. El uso de caché y los descuentos hacen que el precio realmente pagado a menudo sea muy inferior al publicado.

Rendimiento

La velocidad de generación indica lo rápido que escribe el modelo (tokens por segundo: cuanto mayor, mejor). La latencia es el tiempo total de ida y vuelta (cuanto menor, mejor). TTFT es el tiempo hasta el primer token: cuánto tarda en aparecer algo en pantalla (cuanto menor, mejor).

Tiempo de actividad

El tiempo de actividad es el porcentaje de los últimos 3 días en que al menos un proveedor respondía a las solicitudes. La disponibilidad es el porcentaje de tiempo en que la inferencia se sirvió correctamente. OpenRouter supervisa continuamente y utiliza el siguiente mejor proveedor cuando uno devuelve un error.

Pruebas de rendimiento

Puntuaciones en evaluaciones estandarizadas y respuestas de este modelo a instrucciones compartidas de las pruebas de referencia. Los porcentajes más altos son mejores, y cada posición indica dónde se sitúa este modelo entre los modelos evaluados en esa fuente.

Resumen de puntuaciones en pruebas de rendimiento de OpenAI: GPT-5.4 Nano (batch) (Artificial Analysis y Vals AI)
FuentePrueba de rendimientoPuntuación
Artificial AnalysisGPT-5.4 nano (Medium) GPQA Diamond76.1%
Artificial AnalysisGPT-5.4 nano (Medium) HLE15.9%
Artificial AnalysisGPT-5.4 nano (Medium) IFBench64.4%
Artificial AnalysisGPT-5.4 nano (Medium) τ²-Bench Telecom52.6%
Artificial AnalysisGPT-5.4 nano (Medium) AA-LCR67.3%
Artificial AnalysisGPT-5.4 nano (Medium) CritPt5.1%
Artificial AnalysisGPT-5.4 nano (Medium) Terminal-Bench Hard33.3%
Artificial AnalysisGPT-5.4 nano (Non-reasoning) GPQA Diamond55.8%
Artificial AnalysisGPT-5.4 nano (Non-reasoning) HLE4.1%
Artificial AnalysisGPT-5.4 nano (Non-reasoning) IFBench32.7%
Artificial AnalysisGPT-5.4 nano (Non-reasoning) τ²-Bench Telecom34.8%
Artificial AnalysisGPT-5.4 nano (Non-reasoning) AA-LCR29.7%
Artificial AnalysisGPT-5.4 nano (Non-reasoning) CritPt0.0%
Artificial AnalysisGPT-5.4 nano (Non-reasoning) Terminal-Bench Hard24.2%
Artificial AnalysisGPT-5.4 nano (Xhigh) Coding Index56,1
Artificial AnalysisGPT-5.4 nano (Xhigh) Agentic Index16,0
Artificial AnalysisGPT-5.4 nano (Xhigh) GPQA Diamond81.7%
Artificial AnalysisGPT-5.4 nano (Xhigh) HLE28.3%
Artificial AnalysisGPT-5.4 nano (Xhigh) IFBench75.9%
Artificial AnalysisGPT-5.4 nano (Xhigh) τ²-Bench Telecom76.0%
Artificial AnalysisGPT-5.4 nano (Xhigh) AA-LCR76.7%
Artificial AnalysisGPT-5.4 nano (Xhigh) τ-Bench Banking27.4%
Artificial AnalysisGPT-5.4 nano (Xhigh) GDPval-AA22.7%
Artificial AnalysisGPT-5.4 nano (Xhigh) CritPt9.3%
Artificial AnalysisGPT-5.4 nano (Xhigh) SciCode47.2%
Artificial AnalysisGPT-5.4 nano (Xhigh) Terminal-Bench Hard42.4%
Artificial AnalysisGPT-5.4 nano (Xhigh) Terminal-Bench 4.00.5%
Artificial AnalysisGPT-5.4 nano (Xhigh) Terminal-Bench 2.160.7%
Vals AIGPT 5.4 Nano AIME88.8%
Vals AIGPT 5.4 Nano CaseLaw v251.9%
Vals AIGPT 5.4 Nano Code Migration14.5%
Vals AIGPT 5.4 Nano CorpFin v261.2%
Vals AIGPT 5.4 Nano EMB44.8%
Vals AIGPT 5.4 Nano Finance Agent (v2)38.2%
Vals AIGPT 5.4 Nano GPQA Diamond77.5%
Vals AIGPT 5.4 Nano Harvey's Legal Agent Benchmark v10.0%
Vals AIGPT 5.4 Nano IOI v115.3%
Vals AIGPT 5.4 Nano LiveCodeBench84.0%
Vals AIGPT 5.4 Nano LegalBench77.9%
Vals AIGPT 5.4 Nano Legal Research Bench6.3%
Vals AIGPT 5.4 Nano MedCode41.0%
Vals AIGPT 5.4 Nano MedScribe77.1%
Vals AIGPT 5.4 Nano MMLU Pro77.2%
Vals AIGPT 5.4 Nano MMMU Pro73.6%
Vals AIGPT 5.4 Nano MortgageTax59.1%
Vals AIGPT 5.4 Nano ProofBench v15.0%
Vals AIGPT 5.4 Nano SAGE v138.1%
Vals AIGPT 5.4 Nano SWE-bench69.8%
Vals AIGPT 5.4 Nano Tax Agent Bench5.0%
Vals AIGPT 5.4 Nano TaxEval v267.4%
Vals AIGPT 5.4 Nano Terminal-Bench 2.141.6%
Vals AIGPT 5.4 Nano Vals Index33.0%
Vals AIGPT 5.4 Nano Vals Multimodal Index47.6%
Vals AIGPT 5.4 Nano Vibe Code Bench v1.126.1%

Aplicaciones

Aplicaciones públicas que envían más tráfico a este modelo. Una buena señal de cómo son las cargas de trabajo reales en producción, y una pista sobre los casos de uso para los que este modelo es más adecuado.

Actividad

Volumen de tokens y tráfico de solicitudes a este modelo a lo largo del tiempo.

API

Código listo para usar para llamar a este modelo. La API de OpenRouter es compatible con OpenAI: la mayoría de los SDK funcionan con solo cambiar la URL base. Lo único que cambia entre modelos es el identificador del modelo que aparece a continuación.

Actualmente no está disponible mediante el enrutamiento dentro de la región de la UE. Documentación de enrutamiento dentro de la región

Explorar más modelos

Modelos de IA con visión: LLM multimodales para la comprensión de imágenesColecciónClasificaciones de modelos de IAClasificación
0,10 US$0,625 US$0,01 US$----
--
Pruebas de rendimiento de AutoExacto
GPQA DiamondTAU-BenchOpenAI77,3 %67,3 %

Aún no hay suficientes datos de tiempo de actividad para mostrar.

Cuando se produce un error en un proveedor ascendente, podemos recuperarnos enrutando la solicitud a otro proveedor que funcione correctamente, si los filtros de su solicitud lo permiten. Puede acceder mediante programación a los datos de tiempo de actividad de cada proveedor a través de la API de endpoints. Obtenga más información sobre nuestras opciones de equilibrio de carga y enrutamiento de proveedores.

Aún no hay suficientes datos sobre las aplicaciones que usan este modelo.

Preguntas frecuentes

GPT-5.4 nano is the most lightweight and cost-efficient variant of the GPT-5.4 family, optimized for speed-critical and high-volume tasks. It supports text and image inputs and is designed for low-latency use cases such as classification, data extraction, ranking, and sub-agent execution.

GPT-5.4 Nano (batch) cuesta 0,10 US$/M de tokens de entrada y 0,625 US$/M de tókenes de salida, con tarifas independientes para Cache Read a 0,01 US$/M tokens y Web Search a 10,00 US$/1K calls.

GPT-5.4 Nano (batch) tiene una ventana de contexto de 400.000 tókenes. Admite hasta 128.000 tókenes de respuesta.

Sí. GPT-5.4 Nano (batch) admite tools y tool_choice para llamadas a funciones. También admite salidas estructuradas mediante un esquema JSON en response_format.

GPT-5.4 Nano (batch) acepta archivos como PDF, imágenes y texto como entrada y devuelve texto.

GPT-6.1 Terra Pro, GPT-6.1 Terra, GPT-6 Luna Decisions y 58 más son otros modelos de texto de OpenAI.

GPT-5.4 Nano (batch) se lanzó el 17 de marzo de 2026. Su fecha límite de conocimientos es 31 de agosto de 2025.

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Cost note: pro mode spends far more reasoning tokens per request, so a typical request costs several times more than the same request on GPT-6.1 Terra and takes much longer to complete. It is intended for hard, high-stakes problems where the extra accuracy justifies the cost. For everyday coding, agentic, and chat workloads, use GPT-6.1 Terra instead.

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GPT-6 Sol is the cost-efficient high-end model in OpenAI's GPT-6 series, positioned below the flagship GPT-6 Astra and above the fast GPT-6 Luna tier. It is suited for demanding professional work, agentic coding, business workflow automation, and computer use, and is particularly strong at long-horizon software engineering tasks in real codebases. It approaches Astra-level factual reliability at a much lower cost and shares Astra's clearer, more concise communication style in technical and coding conversations.

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GPT Astra Latest

This model always redirects to the latest model in the GPT Astra family.

TextoContexto de 1,1M
GPT Sol Latest

This model always redirects to the latest model in the GPT Sol family.

TextoContexto de 1,1M
GPT Terra Latest

This model always redirects to the latest model in the GPT Terra family.

TextoContexto de 1,1M

GPT-5.4 Nano (batch) cuesta 0,10 US/Mdetokensdeentraday0,625 US/M de tokens de entrada y 0,625 US/Mdetokensdeentraday0,625 US/M de tókenes de salida, con tarifas independientes para Cache Read a 0,01 US/MtokensyWebSearcha10,00 US/M tokens y Web Search a 10,00 US/MtokensyWebSearcha10,00 US/1K calls.