Gemini 3.1 Pro — Especificaciones
Compiled by Mustafa Ihsan from the vendor’s published documentation · Last updated
| Desarrollador | |
|---|---|
| Tipo | LLM (multimodal) |
| Modalidad | Texto, imagen, audio y vídeo → Texto |
| Parámetros | No revelado |
| Ventana de contexto | 1,05 millones |
| Salida máxima | 65 000 |
| Licencia | Propietario |
| Pesos abiertos | No |
| Lanzado | 2026 |
| Precio de entrada | $2.00 /1M |
| Precio de salida | $12.00 /1M |
| Proveedores de API | Google AI Studio, Vertex AI |
What is Gemini 3.1 Pro?
Gemini 3.1 Pro is Google’s Pro-tier multimodal model, combining strong reasoning and
agentic ability with a context window of roughly 1.05M tokens. It is served through Google AI
Studio and Vertex AI at $2 in / $12 out per million tokens.
The pricing footnote matters more than the headline rate: prompts above 200K tokens are
billed at roughly double the input rate. For a model whose main selling point is a million-token
context, that creates a sharp cost cliff exactly where you would want to use it. Budgeting a
long-context feature on the $2 figure will understate the bill substantially once real user
prompts start crossing 200K. The practical response is to design around the threshold —
aggressive retrieval and reranking so that prompts stay under it — or to compare against a
model that charges one flat rate across the whole window, such as Claude Opus 4.8. Note too
that Gemini 3.5 Flash sits below 3.1 Pro on price while outperforming it on coding and
agentic benchmarks, so 3.1 Pro is worth choosing for its multimodal breadth rather than by
default.
Gemini 3.1 Pro pricing: API cost per 1M tokens
| Entrada (por cada millón de tokens) | $2.00 |
|---|---|
| Salida (por cada millón de tokens) | $12.00 |
| Relación salida/entrada | 6× |
| Combinada (4:1 entrada:salida) | $4.00 por 1 millón de tokens |
What Gemini 3.1 Pro costs per month
Gasto mensual real con una mezcla entrada:salida de 4:1, es decir, la proporción que realmente genera una carga de trabajo típica de chat o RAG.
| Carga de trabajo | Tokens/mes | Coste mensual |
|---|---|---|
| Proyecto secundario | 1 millón de tokens de entrada / 0,25 millones de tokens de salida | $5.00 |
| Pequeño equipo | 20 millones de tokens de entrada / 5 millones de tokens de salida | $100 |
| Producción | 200 millones de tokens de entrada / 50 millones de tokens de salida | $1,000 |
Calcule sus propios números en la Calculadora de costos de API de IA.
Cheaper alternatives to Gemini 3.1 Pro
| Modelo | Dólares por millón combinados | Usted ahorra |
|---|---|---|
| GLM 5.2 abierta | $2.00 | 50% cheaper |
| Gemini 3.5 Flash | $3.00 | 25% cheaper |
| DeepSeek V4-Pro abierta | $0.522 | 87% cheaper |
Preguntas frecuentes
How much does Gemini 3.1 Pro cost per 1M tokens?
Gemini 3.1 Pro costs $2.00 per 1M input tokens and $12.00 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $4.00 per 1M tokens.
How much does Gemini 3.1 Pro cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $100 on Gemini 3.1 Pro. A side project (1M in / 0.25M out) costs roughly $5.00.
What is a cheaper alternative to Gemini 3.1 Pro?
GLM 5.2 is the strongest cheaper option in our database at $2.00 per 1M blended — about 50% less than Gemini 3.1 Pro. It is also open-weight, so self-hosting is an option.
Can I run Gemini 3.1 Pro locally?
No. Gemini 3.1 Pro is a closed, API-only model — the weights are not released, so it cannot be self-hosted.
Why does Gemini 3.1 Pro charge more for output than input?
Output tokens are generated one at a time and cannot be batched the way a prompt can, so they cost the provider more to serve. Gemini 3.1 Pro charges 6× more for output, which is why prompt-heavy workloads are far cheaper to run than generation-heavy ones.
Compare los precios de todos los modelos Gemini uno al lado del otro: Precios de la API Gemini.
Los precios corresponden a las tarifas oficiales publicadas para la API principal del modelo y se revisan periódicamente conforme los proveedores los actualicen. No incluyen descuentos por volumen, procesamiento por lotes ni entradas en caché. Compare todos los modelos uno al lado del otro en la Base de datos de modelos de IA o el Clasificación de modelos de lenguaje grande (LLM).