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Favicon de google

Google: Gemini 2.5 Pro

google/gemini-2.5-pro

Comparar

Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy and nuanced context handling. Gemini 2.5 Pro achieves top-tier performance on multiple benchmarks, including first-place positioning on the LMArena leaderboard, reflecting superior human-preference alignment and complex problem-solving abilities.

Modalidades
Precio de entrada / salida
1,25 US$ / 10 US$por 1M
Contexto
1,0M
Publicado
17 jun 2025
Fecha de corte del conocimiento
ene 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 Google: Gemini 2.5 Pro (Artificial Analysis, Design Arena y Vals AI)
FuentePrueba de rendimientoPuntuación
Artificial AnalysisGemini 2.5 Pro Preview (May' 25) GPQA Diamond82.2%
Artificial AnalysisGemini 2.5 Pro Preview (May' 25) HLE19.9%
Artificial AnalysisGemini 2.5 Pro Preview (May' 25) LiveCodeBench77.0%
Artificial AnalysisGemini 2.5 Pro Preview (May' 25) MMLU-Pro83.7%
Artificial AnalysisGemini 2.5 Pro Preview (May' 25) AIME84.3%
Artificial AnalysisGemini 2.5 Pro Preview (May' 25) MATH-50098.6%
Artificial AnalysisGemini 2.5 Pro Coding Index33,3
Artificial AnalysisGemini 2.5 Pro Agentic Index1,6
Artificial AnalysisGemini 2.5 Pro GPQA Diamond84.4%
Artificial AnalysisGemini 2.5 Pro HLE22.5%
Artificial AnalysisGemini 2.5 Pro IFBench48.7%
Artificial AnalysisGemini 2.5 Pro τ²-Bench Telecom54.1%
Artificial AnalysisGemini 2.5 Pro AA-LCR69.0%
Artificial AnalysisGemini 2.5 Pro τ-Bench Banking9.7%
Artificial AnalysisGemini 2.5 Pro GDPval-AA0.0%
Artificial AnalysisGemini 2.5 Pro CritPt2.6%
Artificial AnalysisGemini 2.5 Pro LiveCodeBench80.1%
Artificial AnalysisGemini 2.5 Pro SciCode46.3%
Artificial AnalysisGemini 2.5 Pro Terminal-Bench Hard26.5%
Artificial AnalysisGemini 2.5 Pro Terminal-Bench 4.00.0%
Artificial AnalysisGemini 2.5 Pro Terminal-Bench 2.128.5%
Artificial AnalysisGemini 2.5 Pro MMLU-Pro86.2%
Artificial AnalysisGemini 2.5 Pro AIME 202587.7%
Artificial AnalysisGemini 2.5 Pro AIME88.7%
Artificial AnalysisGemini 2.5 Pro MATH-50096.7%
Design ArenaGemini 2.5 Pro Models Arena 3D Elo1078
Design ArenaGemini 2.5 Pro Models Arena Code Categories Elo1150
Design ArenaGemini 2.5 Pro Models Arena Data Visualization Elo1222
Design ArenaGemini 2.5 Pro Models Arena Game Development Elo1111
Design ArenaGemini 2.5 Pro Models Arena UI Component Elo1134
Design ArenaGemini 2.5 Pro Models Arena Website Elo1165
Vals AIGemini 2.5 Pro CaseLaw v263.9%
Vals AIGemini 2.5 Pro CorpFin v260.8%
Vals AIGemini 2.5 Pro Finance Agent v1.141.6%
Vals AIGemini 2.5 Pro IOI v117.1%
Vals AIGemini 2.5 Pro MedCode50.6%
Vals AIGemini 2.5 Pro MedScribe73.6%
Vals AIGemini 2.5 Pro MortgageTax68.9%
Vals AIGemini 2.5 Pro SAGE v141.9%
Vals AIGemini 2.5 Pro SWE-bench54.4%
Vals AIGemini 2.5 Pro Terminal-Bench 2.030.3%
Vals AIGemini 2.5 Pro Vals Index39.3%
Vals AIGemini 2.5 Pro Vals Multimodal Index42.4%
Vals AIGemini 2.5 Pro Vibe Code Bench v1.10.4%

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
1,250 US$10,00 US$0,1250 US$0,1250 US$3,20s79 tps
100,00 %
No se utiliza en el enrutamiento Standard:Por qué no se usan estos endpoints
Flex
0,625 US$5,00 US$0,0625 US$0,0625 US$3,08s53 tps
100,00 %
Priority
2,250 US$18,00 US$0,2250 US$0,2250 US$3,24s58 tps
100,00 %
Rendimiento
79tok/s

P50, el mejor entre los proveedores

Latencia
3,08s

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.
99,88 %

Disponibilidad durante los últimos 3 días

Últimas 72 horas
Disponibilidad 99,88 %
Hace 3 díasHace 2 díasAyerAhora

Disponibilidad en las últimas 24 horas

OpenRouter Availability
99,87 %

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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Preguntas frecuentes

Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy and nuanced context handling.

Gemini 2.5 Pro cuesta 1,25 US$/M de tokens de entrada y 10,00 US$/M de tókenes de salida, con tarifas independientes para Cache Read a 0,125 US$/M tokens, Cache Write a 0,375 US$/M tokens, Image Input a 1,25 US$/M tokens, Input Audio a 1,25 US$/M tokens e Input Audio Cache a 0,125 US$/M tokens.

Gemini 2.5 Pro tiene una ventana de contexto de 1.048.576 tókenes. Admite hasta 65.536 tókenes de respuesta.

Sí. Gemini 2.5 Pro admite tools y tool_choice para llamadas a funciones. También admite salidas estructuradas mediante un esquema JSON en response_format.

Gemini 2.5 Pro acepta texto, imágenes, archivos como PDF, audio y vídeo como entrada y devuelve texto.

Gemini 4 Argon, Nano Banana 2.1, Gemini 3.8 Flash y 26 más son otros modelos de texto de Google.

Gemini 2.5 Pro se lanzó el 17 de junio de 2025. Su fecha límite de conocimientos es 31 de enero de 2025.

Más modelos de Google

Gemini 4 Argon

Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response.

TextoContexto de 1,0M2 US$ / 10 US$
Gemini 4 Argon (batch)

Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response.

TextoContexto de 1,0M1 US$ / 5 US$
Nano Banana 2.1

Nano Banana 2.1 (Gemini Nano Banana 2.1) is Google's image generation and editing model on the Flash tier, succeeding Nano Banana 2 and Nano Banana Pro. It improves product recontextualization, mask- and ink-based editing, and factual accuracy, and renders photorealistic skin tones, detailed materials, lighting, and coherent backgrounds. It accepts text and image inputs, returns images with optional text, and supports 1K, 2K, and 4K output plus extended aspect ratios via the image_config API Parameter.

ImagenContexto de 66K1,50 US$ / 30 US$
Gemini 3.5 Transcribe

Gemini 3.5 Transcribe is a speech-to-text model from Google. It is suited for synchronous transcription that needs word-level timestamps or speaker diarization, with support for up to eight speakers. Audio can be up to one hour, or 30 minutes when timestamps or diarization are enabled.

Transcripción2 US$ / 12 US$
Gemini 3.8 Flash Lite TTS

Gemini 3.8 Flash Lite TTS is a text-to-speech model from Google and the fast, high-throughput member of the 3.8 TTS family alongside Gemini 3.8 Flash TTS. It is suited for conversational voice-agent pipelines, high-volume read-aloud, and voice replication workloads where latency and cost matter more than maximum expressiveness.

It accepts the same speech configuration as Gemini 3.1 Flash TTS Preview, including the 30 prebuilt studio voices, and supports Google's Voice Design and Voice Replication through voice_... identifiers.

Voz0,50 US$ / 6 US$
Gemini 3.8 Flash TTS

Gemini 3.8 Flash TTS is a text-to-speech model from Google and the successor to Gemini 3.1 Flash TTS Preview. It is the creative tier of the 3.8 TTS family, suited for narration, character work, and multi-speaker dialogue where voice fidelity, acting nuance, and regional dialect coverage matter more than latency.

It accepts the same speech configuration as its predecessor, including the 30 prebuilt studio voices, and supports Google's Voice Design (custom voices from a text description) and Voice Replication (voices cloned from a short sample) through voice_... identifiers.

Voz0,50 US$ / 9 US$
Gemini 3.8 Flash

Gemini 3.8 Flash is Google's most intelligent Flash model with significant gains from 3.7 Flash across software engineering, agentic tasks, and multi-step reasoning.

TextoContexto de 1,0M0,75 US$ / 3,75 US$
Gemini 3.8 Flash (batch)

Gemini 3.8 Flash is Google's most intelligent Flash model with significant gains from 3.7 Flash across software engineering, agentic tasks, and multi-step reasoning.

TextoContexto de 1,0M0,375 US$ / 1,875 US$
Gemini 3.7 Flash

Gemini 3.7 Flash is a multimodal model from Google for fast agentic workflows, coding, and complex multi-step reasoning. It is designed for tasks that require responsive performance and reliable multi-step problem solving.

TextoContexto de 1,0M0,75 US$ / 3,75 US$
Gemini 3.7 Flash (batch)

Gemini 3.7 Flash is a multimodal model from Google for fast agentic workflows, coding, and complex multi-step reasoning. It is designed for tasks that require responsive performance and reliable multi-step problem solving.

TextoContexto de 1,0M0,375 US$ / 1,875 US$
Gemini 3.6 Flash

Gemini 3.6 Flash is a high-efficiency model from Google for coding, agentic workflows, and web and app development. It is designed to produce polished outputs with fewer unnecessary edits and less hedging, while reducing token use and the number of model calls needed to complete a task.

TextoContexto de 1,0M0,75 US$ / 3,75 US$
Gemini 3.6 Flash (batch)

Gemini 3.6 Flash is a high-efficiency model from Google for coding, agentic workflows, and web and app development. It is designed to produce polished outputs with fewer unnecessary edits and less hedging, while reducing token use and the number of model calls needed to complete a task.

TextoContexto de 1,0M0,375 US$ / 1,875 US$
Gemini 3.5 Flash Lite

Gemini 3.5 Flash Lite is a high-efficiency model from Google with upgraded agentic capabilities. It is suited for subagents that execute focused tasks within complex, multi-agent workflows.

TextoContexto de 1,0M0,30 US$ / 2,50 US$
Gemini 3.5 Flash Lite (batch)

Gemini 3.5 Flash Lite is a high-efficiency model from Google with upgraded agentic capabilities. It is suited for subagents that execute focused tasks within complex, multi-agent workflows.

TextoContexto de 1,0M0,15 US$ / 1,25 US$
Nano Banana 2 Lite

Nano Banana 2 Lite (Gemini 3.1 Flash Lite Image) is Google's fastest, most cost-efficient Gemini image model, built for high-velocity developer pipelines and rapid-fire visual exploration. It delivers text-to-image generation in roughly 4 seconds — about 2.7× faster than Gemini 3.1 Flash Image — while keeping the character consistency, precise editing, and real-world knowledge of the Nano Banana family.

A single drop-in API handles text-to-image, image editing, and multi-image composition. As a multimodal model it also returns text alongside images. Outputs are generated at 1K resolution across 14 aspect ratios and carry an invisible SynthID watermark so they can be identified as AI-generated.

Positioned as the best balance of quality and speed in the Nano Banana 2 line, it lets you generate thousands of images at a fraction of the cost of heavier production models — ideal for prototyping, real-time apps, and visual workflows at scale.

ImagenContexto de 66K0,25 US$ / 30 US$
Nano Banana 2

Gemini 3.1 Flash Image, a.k.a. "Nano Banana 2," is Google’s latest state of the art image generation and editing model, delivering Pro-level visual quality at Flash speed. It combines advanced contextual understanding with fast, cost-efficient inference, making complex image generation and iterative edits significantly more accessible. Aspect ratios can be controlled with the image_config API Parameter

ImagenContexto de 131K0,50 US$ / 60 US$
Nano Banana Pro

Nano Banana Pro is Google’s most advanced image-generation and editing model, built on Gemini 3 Pro. It extends the original Nano Banana with significantly improved multimodal reasoning, real-world grounding, and high-fidelity visual synthesis. The model generates context-rich graphics, from infographics and diagrams to cinematic composites, and can incorporate real-time information via Search grounding.

It offers industry-leading text rendering in images (including long passages and multilingual layouts), consistent multi-image blending, and accurate identity preservation across up to five subjects. Nano Banana Pro adds fine-grained creative controls such as localized edits, lighting and focus adjustments, camera transformations, and support for 2K/4K outputs and flexible aspect ratios. It is designed for professional-grade design, product visualization, storyboarding, and complex multi-element compositions while remaining efficient for general image creation workflows.

ImagenContexto de 131K2 US$ / 120 US$
Gemini Embedding 2

Gemini Embedding 2 is Google's first multimodal embedding model. We currently support mapping text and images into a unified vector space for semantic search and retrieval-augmented generation (RAG). It supports input context up to 8,192 tokens and flexible output dimensions from 128 to 3,072 (recommended: 768, 1536, or 3,072). Designed for cross-modal similarity — you can embed a text query and retrieve the most relevant images, or vice versa — making it well-suited for multimodal search, recommendation, and document understanding pipelines.

Embeddings0,20 US$/M tokens
Gemini Embedding 2 (batch)

Gemini Embedding 2 is Google's first multimodal embedding model. We currently support mapping text and images into a unified vector space for semantic search and retrieval-augmented generation (RAG). It supports input context up to 8,192 tokens and flexible output dimensions from 128 to 3,072 (recommended: 768, 1536, or 3,072). Designed for cross-modal similarity — you can embed a text query and retrieve the most relevant images, or vice versa — making it well-suited for multimodal search, recommendation, and document understanding pipelines.

Embeddings0,10 US$/M tokens
Gemini 3.5 Flash

Gemini 3.5 Flash is Google's high-efficiency multimodal model, bringing near-Pro level coding and reasoning at Flash-tier cost and speed. It is highly optimized for coding proficiency and parallel agentic execution loops, supporting text, image, video, audio, and PDF inputs.

Defaults to medium thinking effort for faster and more cost-efficient responses, with full support for thinking levels (minimal, low, medium, high) for fine-grained cost/performance trade-offs.

TextoContexto de 1,0M1,50 US$ / 9 US$

Gemini 2.5 Pro cuesta 1,25 US/Mdetokensdeentraday10,00 US/M de tokens de entrada y 10,00 US/Mdetokensdeentraday10,00 US/M de tókenes de salida, con tarifas independientes para Cache Read a 0,125 US/Mtokens,CacheWritea0,375 US/M tokens, Cache Write a 0,375 US/Mtokens,CacheWritea0,375 US/M tokens, Image Input a 1,25 US/Mtokens,InputAudioa1,25 US/M tokens, Input Audio a 1,25 US/Mtokens,InputAudioa1,25 US/M tokens e Input Audio Cache a 0,125 US$/M tokens.