Gemini 3.5 Flash — 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 Mio. |
| Maximale Ausgabe | 65 K |
| Lizenz | Proprietär |
| Offene Gewichte | Nein |
| Veröffentlicht | 2026 |
| Eingabepreis | $1.50 /1M |
| Ausgabepreis | $9.00 /1M |
| API-Anbieter | Google AI Studio, Vertex AI |
What is Gemini 3.5 Flash?
Gemini 3.5 Flash is Google’s fast multimodal model, and an unusual case of a cheaper tier
beating the more expensive one on the benchmarks most people care about: it outperforms
Gemini 3.1 Pro on coding and agentic tasks while running roughly 4× faster and about 25%
cheaper, with the same 1M-token context. Pricing is $1.50 in / $9 out per million tokens.
When the faster, cheaper model also scores higher, the “Pro” tier stops being the default
and becomes a specialist choice. For coding assistants, agent loops and anything where a user
is waiting on a response, 3.5 Flash is the better pick on every axis at once — a rare thing
in model selection, where speed, cost and quality usually trade against each other. Reserve
3.1 Pro for workloads that specifically need its multimodal depth. The one caveat is the same
one that applies across the Gemini line: check the long-context billing terms for your tier
before building a feature that routinely sends very large prompts, because the headline rate
is not always the rate you pay above 200K tokens.
Gemini 3.5 Flash pricing: API cost per 1M tokens
| Eingabe (pro 1 Mio. Token) | $1.50 |
|---|---|
| Ausgabe (pro 1 Mio. Token) | $9.00 |
| Verhältnis Output/Input | 6× |
| Gemischt (4:1 Input:Output) | $3.00 pro 1 Mio. Tokens |
What Gemini 3.5 Flash 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 | $3.75 |
| Kleines Team | 20 Mio. Eingabe / 5 Mio. Ausgabe | $75 |
| Produktion | 200 Mio. Eingabe / 50 Mio. Ausgabe | $750 |
Stellen Sie Ihre eigenen Berechnungen im KI-API-Kostenrechner.
Cheaper alternatives to Gemini 3.5 Flash
| Modell | Gewichteter Preis pro Million US-Dollar | Sie sparen |
|---|---|---|
| GLM 5.2 offenere | $2.00 | 33 % günstiger |
| DeepSeek V4-Pro offenere | $0.522 | 83% cheaper |
| Kimi K2.7 Code offenere | $0.980 | 67 % günstiger |
Häufig gestellte Fragen
How much does Gemini 3.5 Flash cost per 1M tokens?
Gemini 3.5 Flash costs $1.50 per 1M input tokens and $9.00 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $3.00 per 1M tokens.
How much does Gemini 3.5 Flash cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $75 on Gemini 3.5 Flash. A side project (1M in / 0.25M out) costs roughly $3.75.
What is a cheaper alternative to Gemini 3.5 Flash?
GLM 5.2 is the strongest cheaper option in our database at $2.00 per 1M blended — about 33% less than Gemini 3.5 Flash. It is also open-weight, so self-hosting is an option.
Can I run Gemini 3.5 Flash locally?
No. Gemini 3.5 Flash is a closed, API-only model — the weights are not released, so it cannot be self-hosted.
Why does Gemini 3.5 Flash 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 3.5 Flash 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.