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Favicon de meta-llama

Meta: Llama 4 Scout

meta-llama/llama-4-scout

Pesos del modelo
Comparar

Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input (text and image) and multilingual output (text and code) across 12 supported languages. Designed for assistant-style interaction and visual reasoning, Scout uses 16 experts per forward pass and features a context length of 10 million tokens, with a training corpus of ~40 trillion tokens.

Built for high efficiency and local or commercial deployment, Llama 4 Scout incorporates early fusion for seamless modality integration. It is instruction-tuned for use in multilingual chat, captioning, and image understanding tasks. Released under the Llama 4 Community License, it was last trained on data up to August 2024 and launched publicly on April 5, 2025.

Modalidades
Precio de entrada / salida
0,10 US$ / 0,30 US$por 1M
Contexto
1,3M
Publicado
5 abr 2025
Fecha de corte del conocimiento
ago 2024
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 Meta: Llama 4 Scout (Artificial Analysis y Design Arena)
FuentePrueba de rendimientoPuntuación
Artificial AnalysisLlama 4 Scout Coding Index8,2
Artificial AnalysisLlama 4 Scout Agentic Index0,5
Artificial AnalysisLlama 4 Scout GPQA Diamond58.7%
Artificial AnalysisLlama 4 Scout HLE3.8%
Artificial AnalysisLlama 4 Scout IFBench39.5%
Artificial AnalysisLlama 4 Scout τ²-Bench Telecom15.5%
Artificial AnalysisLlama 4 Scout AA-LCR27.7%
Artificial AnalysisLlama 4 Scout τ-Bench Banking3.3%
Artificial AnalysisLlama 4 Scout GDPval-AA0.0%
Artificial AnalysisLlama 4 Scout CritPt0.0%
Artificial AnalysisLlama 4 Scout LiveCodeBench29.9%
Artificial AnalysisLlama 4 Scout SciCode21.3%
Artificial AnalysisLlama 4 Scout Terminal-Bench Hard1.5%
Artificial AnalysisLlama 4 Scout Terminal-Bench 4.00.0%
Artificial AnalysisLlama 4 Scout Terminal-Bench 2.13.7%
Artificial AnalysisLlama 4 Scout MMLU-Pro75.2%
Artificial AnalysisLlama 4 Scout AIME 202514.0%
Artificial AnalysisLlama 4 Scout AIME28.3%
Artificial AnalysisLlama 4 Scout MATH-50084.4%
Design ArenaLlama 4 Scout Models Arena Code Categories Elo788
Design ArenaLlama 4 Scout Models Arena Data Visualization Elo896
Design ArenaLlama 4 Scout Models Arena Game Development Elo773
Design ArenaLlama 4 Scout Models Arena UI Component Elo762
Design ArenaLlama 4 Scout Models Arena Website Elo750

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
Latencia / rendimiento
0,25 US$0,70 US$----
--
0,10 US$0,30 US$1,44s31 tps
100,00 %
Priority
0,15 US$0,45 US$2,94s9 tps
81,17 %
No se utiliza en el enrutamiento Standard:Por qué no se usan estos endpoints
Flex
0,08 US$0,24 US$----
14,19 %
Rendimiento
31tok/s

P50, el mejor entre los proveedores

Latencia
1,44s

P50, mejor proveedor

Tiempo de actividad (3 d)El modelo estaba accesible. Solicitud enrutada a un proveedor.
100,00 %
Disponibilidad (3 d)El modelo devolvió una inferencia de cualquier proveedor. Los errores y las respuestas vacías cuentan en su contra.
99,95 %

Disponibilidad durante los últimos 3 días

Últimas 72 horas
Disponibilidad 99,95 %
Hace 3 díasHace 2 díasAyerAhora

Disponibilidad en las últimas 24 horas

OpenRouter Availability
99,95 %
Without Routing
84,11 %

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.

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websim
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495Mtókenes
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G0DM0D3-pliny
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241Mtókenes
4.
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VerifierContractBench
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5.
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DA KINGZ FendiAI
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186Mtókenes

Preguntas frecuentes

Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input (text and image) and multilingual output (text and code) across 12 supported languages.

Llama 4 Scout cuesta 0,10 US$/M de tokens de entrada y 0,30 US$/M de tókenes de salida.

Llama 4 Scout tiene una ventana de contexto de 1.310.720 tókenes. Admite hasta 16.384 tókenes de respuesta.

Sí. Llama 4 Scout admite tools y tool_choice para llamadas a funciones. También admite salidas estructuradas mediante un esquema JSON en response_format.

Llama 4 Scout acepta texto e imágenes como entrada y devuelve texto.

Llama 4 Scout está disponible a través de 2 proveedores en OpenRouter: DeepInfra y Google Vertex. Las solicitudes se enrutan al mejor proveedor disponible, con conmutación automática a los demás en caso de fallo, y puede fijar o excluir proveedores mediante el enrutamiento de proveedores.

Llama 4 Scout se lanzó el 5 de abril de 2025. Su fecha límite de conocimientos es 31 de agosto de 2024.

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Llama 4 Scout cuesta 0,10 US/Mdetokensdeentraday0,30 US/M de tokens de entrada y 0,30 US/Mdetokensdeentraday0,30 US/M de tókenes de salida.