Gemini 3.1 Pro — Spezifikationen
Compiled by Mustafa Ihsan from the vendor’s published documentation · Last updated
| Entwickler | |
|---|---|
| Typ | LLM (multimodal) |
| Modality | Text, Bild, Audio, Video → Text |
| Parameter | Nicht offengelegt |
| Kontextfenster | 1,05 Mio. |
| Maximale Ausgabe | 65 K |
| Lizenz | Proprietär |
| Offene Gewichte | Nein |
| Veröffentlicht | 2026 |
| Eingabepreis | $2.00 /1M |
| Ausgabepreis | $12.00 /1M |
| API-Anbieter | 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
| Eingabe (pro 1 Mio. Token) | $2.00 |
|---|---|
| Ausgabe (pro 1 Mio. Token) | $12.00 |
| Verhältnis Output/Input | 6× |
| Gemischt (4:1 Input:Output) | $4.00 pro 1 Mio. Tokens |
What Gemini 3.1 Pro costs per month
Tatsächliche monatliche Ausgaben bei einem 4:1-Input-zu-Output-Mix – dem Verhältnis, das typische Chat- oder RAG-Arbeitslasten tatsächlich erzeugen.
| Workload | Tokens/Monat | Kosten pro Monat |
|---|---|---|
| Nebenprojekt | 1 Mio. Eingabe / 0,25 Mio. Ausgabe | $5.00 |
| Kleines Team | 20 Mio. Eingabe / 5 Mio. Ausgabe | $100 |
| Produktion | 200 Mio. Eingabe / 50 Mio. Ausgabe | $1,000 |
Stellen Sie Ihre eigenen Berechnungen im KI-API-Kostenrechner.
Cheaper alternatives to Gemini 3.1 Pro
| Modell | Gewichteter Preis pro Million US-Dollar | Sie sparen |
|---|---|---|
| GLM 5.2 offenere | $2.00 | 50% cheaper |
| Gemini 3.5 Flash | $3.00 | 25% cheaper |
| DeepSeek V4-Pro offenere | $0.522 | 87% cheaper |
Häufig gestellte Fragen
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.
Vergleichen Sie alle Gemini-Modelle nebeneinander nach Preis: Preise für die Gemini-API.
Die Preise entsprechen den offiziell veröffentlichten Listenpreisen für die primäre API des jeweiligen Modells und werden regelmäßig aktualisiert, sobald Anbieter diese ändern. Volumen-, Batch- und Cached-Input-Rabatte sind nicht enthalten. Vergleichen Sie alle Modelle nebeneinander im Datenbank für KI-Modelle oder das LLM-Leaderboard.