> ## Documentation Index
> Fetch the complete documentation index at: https://langwatch.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Google Gemini

> Configure Google Gemini (AI Studio) as a provider behind the LangWatch AI Gateway.

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.

## 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.

## 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`:

```json theme={null}
{
  "model_aliases": {
    "gemini-flash": "gemini/gemini-2.5-flash",
    "gemini-pro":   "gemini/gemini-2.5-pro"
  }
}
```

## 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.

## 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.

## 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.

## 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](/docs/ai-gateway/providers/vertex).

Use AI Studio for dev, scoping, SaaS; use Vertex for production enterprise.