Gemini 3.1 Pro — Specifiche
Compiled by Mustafa Ihsan from the vendor’s published documentation · Last updated
| Sviluppatore | |
|---|---|
| Tipo | LLM (multimodale) |
| Modalità | Testo, immagine, audio, video → Testo |
| Parametri | Non divulgato |
| Finestra contestuale | 1,05 milioni |
| Output massimo | 65K |
| Licenza | Proprietaria |
| Pesi aperti | No |
| Pubblicato | 2026 |
| Prezzo dell’input | $2.00 /1M |
| Prezzo dell’output | $12.00 /1M |
| Provider 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
| Input (per ogni milione di token) | $2.00 |
|---|---|
| Output (per ogni milione di token) | $12.00 |
| Rapporto output/input | 6× |
| Combinato (4:1 in:out) | $4.00 per 1 milione di token |
What Gemini 3.1 Pro costs per month
Spesa mensile reale con un mix input-to-output 4:1 — il rapporto effettivamente prodotto da un tipico carico di lavoro basato su chat o RAG.
| Carico di lavoro | Token/mese | Costo/mese |
|---|---|---|
| Progetto secondario | 1 milione in ingresso / 0,25 milioni in uscita | $5.00 |
| Piccolo team | 20 milioni in ingresso / 5 milioni in uscita | $100 |
| Produzione | 200 milioni in ingresso / 50 milioni in uscita | $1,000 |
Calcola i tuoi numeri personalizzati nel Calcolatore dei costi delle API per l'IA.
Cheaper alternatives to Gemini 3.1 Pro
| Modello | Costo combinato ($/1 milione) | Risparmi |
|---|---|---|
| GLM 5.2 aperta | $2.00 | 50% cheaper |
| Gemini 3.5 Flash | $3.00 | 25% cheaper |
| DeepSeek V4-Pro aperta | $0.522 | 87% cheaper |
Domande frequenti
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.
Confronta i prezzi di tutti i modelli Gemini uno accanto all’altro: Prezzi dell'API Gemini.
I prezzi indicati sono le tariffe ufficiali pubblicate per l'API principale del modello e vengono aggiornati man mano che i fornitori li modificano. Sconti per volumi elevati, elaborazione batch e input memorizzati nella cache non sono inclusi. Confronta tutti i modelli fianco a fianco nel Database di modelli IA o il Classifica LLM.