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Gemini via Google AI Studio (the “plain” Google API, distinct from Vertex) is the fastest way to get Gemini models behind the gateway. API-key auth, no GCP project setup.

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Configure the provider credential

Under Settings → Model Providers:
  1. Add provider → Google Gemini.
  2. Paste the AI Studio API key (starts with a long alphanumeric string).
  3. Save.

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Supported models

  • gemini-2.5-flash, fast + cheap. Default recommendation for coding CLIs on a Gemini-first VK.
  • gemini-2.5-pro, bigger, more capable.
  • gemini-2.0-flash, prior generation.
  • gemini-2.0-pro, prior generation.
  • Embedding models: text-embedding-004, gemini-embedding-001.
Expose via VK model_aliases: 
{
  "model_aliases": {
    "gemini-flash": "gemini/gemini-2.5-flash",
    "gemini-pro":   "gemini/gemini-2.5-pro"
  }
}

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Supported endpoints

  • POST /v1/chat/completions, OpenAI-shape dispatched to Gemini’s generateContent, streamGenerateContent endpoint.
  • POST /v1/embeddings, for Gemini embedding models.
Gemini has no /v1/messages equivalent; Anthropic-shape clients should use a VK that has Anthropic or Bedrock as primary.

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Context caching

Gemini has an implicit context cache for prompts > 32k tokens on most models. The gateway forwards requests untouched; caching behaviour is entirely upstream-managed. For explicit caching, Gemini offers a separate caches.create API that the gateway does not orchestrate in v1, but clients can call Gemini directly to create a cache and reference it in subsequent gateway calls.

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Known quirks

  • Safety filters on by default. Gemini applies safetySettings thresholds that return finish_reason: SAFETY when content crosses the threshold. Bifrost/core surfaces this as provider_error with the safety metadata in the OTel trace.
  • Rate limits. AI Studio’s free tier has aggressive rate limits; paid tier is unlocked via billing-attached Google account. A 429 triggers fallback if configured.
  • Tool/function calling. Gemini has its own function-calling format that bifrost/core translates from OpenAI-shape tools. Tool-call streaming deltas are less mature on Gemini, expect byte-level differences vs OpenAI.
  • Streaming chunks size. Gemini’s streaming emits larger chunks than OpenAI, fewer, bigger SSE frames. Gateway passes these through unchanged.
  • No system role. Gemini uses systemInstruction at the top level of the request. OpenAI-SDK clients that send a system role message get it rewritten by the translator.

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AI Studio vs Vertex

Google has two paths to Gemini:
  • AI Studio (this page), quickest, API-key auth, pay-as-you-go on a consumer-ish billing model.
  • Vertex AI: GCP-native, IAM-governed, data-residency, enterprise SLAs. See Vertex.
Use AI Studio for dev, scoping, SaaS; use Vertex for production enterprise.