Gemini 3.1 Flash Lite

Gemini 3.5 Flash is a high-throughput multimodal model from Google, featuring a 1M token context window and native audio/video reasoning. Built for speed and efficiency, it delivers elite performance for long-document QA and real-time analysis.

Modalitäten

Kontext

API-Nutzungsbeispiele

curl --request POST "https://gptproto.com/v1/chat/completions" \
    --header "Authorization: Bearer $GPTPROTO_API_KEY" \
    --header "Content-Type: application/json" \
    --data '{
        "model": "gemini-3.1-flash-lite",
        "messages": [
            {
                "role": "user",
                "content": "Hello"
            }
        ]
    }'

Gemini 3.1 Flash Lite Preise

Chat, Coding-Agenten und Dokumentenarbeit. Preise pro 1 Mio. Tokens – Eingabe, gecachte Eingabe und Ausgabe werden separat abgerechnet. GPTProto liegt 40% unter den offiziellen Preisen.

Szenario	Google Liste	OpenRouter	GPTProto	Du sparst / Monat
Persönlich10 Mio. Tokens / Monat (4.8 Mio. im Cache)	$3.92	$4.14	$2.35	−$1.57≈ $18.82 / Jahr
Team100 Mio. Tokens / Monat (48 Mio. im Cache)	$39.20	$41.36	$23.52	−$15.68≈ $188.16 / Jahr
Business500 Mio. Tokens / Monat (240 Mio. im Cache)	$196.00	$206.78	$117.60	−$78.40≈ $940.80 / Jahr

Monatliche Kosten nach Quelle

OpenRouter

$0.00

Google

$0.00

GPTProto

$0.00

Spare $0.00 / Monat

Jährliche Ersparnis ≈ $0.00 · Abrechnung nach Verbrauch

Key Features of Gemini 3.5 Flash

Discover the technical advantages that make Gemini 3.5 Flash a leader in high-speed, long-context multimodal AI development.

1M Token Context Window

Analyze massive datasets, entire libraries, or hour-long videos with near-perfect retrieval across a million-token window.

Native Multimodal Reasoning

Reason across text, images, audio, and video frames natively without losing temporal data or requiring external encoders.

Ultra-Low Latency Inference

Optimized for speed, delivering rapid time-to-first-token performance ideal for real-time chatbots and high-speed agents.

Cost-Efficient Context Caching

Reduce costs by 90% for repetitive queries by caching large datasets, making long-context workflows sustainable at scale.

Gemini 3.5 Flash API: Common Questions

Find expert answers about Gemini 3.5 Flash performance, pricing, and integration to help you build faster, high-context AI applications.

What makes Gemini 3.5 Flash different from Pro?

While Gemini 3.5 Flash and Pro both offer massive context windows, Flash is specifically optimized for throughput and latency. It delivers a time-to-first-token that is significantly faster than the Pro version, making it better for real-time applications like customer support bots. It handles multimodal inputs natively but at a much lower cost point, sacrificing a small amount of deep reasoning for extreme speed across large-scale tasks.

How reliable is the 1M token context window?

Gemini 3.5 Flash achieves 99%+ recall in 'Needle In A Haystack' tests across its entire context window. This means the model can accurately find and reason over specific information buried in hundreds of pages of text or an hour of video. It outperforms almost all sub-200k token models for large-scale document analysis and complex codebase queries, ensuring you don't lose critical details even in the most data-heavy conversational prompts.

Can I process video and audio directly?

Yes, Gemini 3.5 Flash is natively multimodal. Unlike other models that require separate transcription or image-to-text steps, Gemini processes audio and video frames directly. This allows it to capture nuances like background noise, emotional tone, and temporal changes in video sequences. You can upload up to an hour of video or large audio files and ask questions about specific timestamps as if it were a standard text-based interaction.

How does the context caching work?

Context caching allows you to store frequently used data—like a large documentation library or a massive codebase—on the server. When you send new queries against this cached data, you receive a 50% discount on input tokens and significantly reduced latency. This is a game-changer for businesses running repetitive analysis on the same large datasets, as it slashes operational costs by up to 90% over time for high-volume agentic workflows.

Is my data used to train Google's models?

No. When you access Gemini 3.5 Flash through our enterprise API, your data is protected. We ensure that none of the prompts, files, or outputs processed through GPTProto.com are used to train Google’s underlying models or our own systems. This allows you to build secure, privacy-compliant applications for sensitive industries like legal, healthcare, and finance without worrying about data leakage or proprietary information being exposed.

How do I switch from GPT-4o-mini to Gemini?

Migrating is simple because our API is OpenAI-compatible. You only need to update your base URL and change the model name to gemini-3.5-flash. Most system instructions and function calling structures are 1:1 compatible. By switching, you gain access to a much larger context window and native video processing at a competitive price point, often with lower latency for high-throughput production workloads and complex data extraction tasks.