Models

gemini-3.5-flash β€” API, Pricing & Context Window | Vivgrid

gemini-3.5-flash on Vivgrid: Google's fast multimodal model with a 1M-token context window across text, image, video, audio and PDF.

gemini-3.5-flash is Google's fast, fully multimodal model, accepting text, image, video, audio, and PDF inputs within a 1M-token context window. It's a strong agent model for workflows that mix media types at scale.

On Vivgrid, gemini-3.5-flash runs as a globally centralized model reachable through the same OpenAI-compatible endpoint and unified key as every other model in the catalog.

Specifications

ProviderGoogle
Model IDgemini-3.5-flash
Best forGeneral-purpose
Available onChat Completions
Context window1,000,000 tokens
Max output65,536 tokens
ModalitiesText, Image, Video, Audio, PDF
Tool / function callingYes
Knowledge cutoff2025-01
Acceleration🌐 Global (Centralized)

Pricing

Pricing in USD per 1M tokens.

InputCached inputOutput
$1.50$0.15$9.00

What one request costs

The rates above applied to a 20K-token prompt with a 2K-token reply β€” a typical single agent turn, small enough that every request is billed at the base rate.

RequestCost
Cold prompt, nothing cached$0.0480
Warm prompt, 90% of the input served from cache$0.0237

API surfaces

The Vivgrid Console catalogs gemini-3.5-flash on one surface, all behind the same API key. The endpoint shown is the one the Console previews for that surface.

SurfacePrimary endpointUse it for
Chat Completions/chat/completionsAgent projects, where Vivgrid injects the model, system prompt and tools server-side, and Model API calls that name the model per request.

Quick start

Get an API key from the Vivgrid Console, then call gemini-3.5-flash directly.

curl https://api.vivgrid.com/v1/chat/completions \
  -H "Authorization: Bearer $VIVGRID_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-3.5-flash",
    "messages": [
      { "role": "user", "content": "Say hello in English, Chinese and Spanish." }
    ],
    "stream": true
  }'

Ideal use cases

  • Multimodal agents combining video, audio, images, and PDFs
  • Long-context understanding up to 1M tokens
  • Fast, high-volume reasoning and summarization
  • Media-heavy ingestion and analysis pipelines

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