Google Gemini: Add Models

The Gemini provider supports six modalities: text/chat, image generation, video generation, TTS, ASR, and embeddings. Language models are auto-discovered from the API; every other modality uses a hand-maintained array in the provider file.

Audience: coding agents and contributors who need to add a new Gemini, Imagen, or Veo model to NodeTool.

TL;DR

Text/chat models: nothing to do — they are fetched live from GET /v1beta/models.
Image models (Imagen + gemini-* image generation): add one entry to getAvailableImageModels() in the provider.
Video models (Veo): add one entry to getAvailableVideoModels().
TTS / ASR / embedding models: add one entry to the matching method.
Run npm run check before committing.

Where things live

Concern	Path
Provider (all Gemini logic)	`packages/runtime/src/providers/gemini-provider.ts`
Language model listing (dynamic)	`GeminiProvider.getAvailableLanguageModels()`
Image model listing (static)	`GeminiProvider.getAvailableImageModels()`
TTS model listing (static)	`GeminiProvider.getAvailableTTSModels()`
ASR model listing (static)	`GeminiProvider.getAvailableASRModels()`
Video model listing (static)	`GeminiProvider.getAvailableVideoModels()`
Embedding model listing (static)	`GeminiProvider.getAvailableEmbeddingModels()`
Token/chat cost	`@pydantic/genai-prices` catalog (automatic — no edit needed)
Non-token cost tiers	`packages/runtime/src/providers/cost-calculator.ts` — `PRICING_TIERS` / `MODEL_TO_TIER`
Provider registration	`packages/runtime/src/providers/index.ts` line 211

How Gemini models are defined

Language models — dynamic

getAvailableLanguageModels() calls GET https://generativelanguage.googleapis.com/v1beta/models?key=<GEMINI_API_KEY>, filters entries whose supportedGenerationMethods includes "generateContent", and maps each to { id, name, provider: "gemini" }. A new text/chat model becomes available when Google adds it to that endpoint.

Image models — static array

getAvailableImageModels() returns a hardcoded array. Two dispatch paths exist inside textToImage():

IDs starting with "gemini-" — call POST /models/<id>:generateContent with responseModalities: ["IMAGE", "TEXT"].
All other IDs (Imagen: "imagen-*") call POST /models/<id>:predict.

Video models — static array

getAvailableVideoModels() returns a hardcoded array. Both textToVideo() and imageToVideo() require a veo-* model ID. Veo calls use the async predictLongRunning endpoint with polling.

TTS / ASR / Embedding — static arrays

Each is a simple array returned from the matching getAvailable* method. TTS models carry a voices field. Embedding models carry a dimensions field.

Add a new model

1. Text/chat model

Nothing to do. The model appears automatically once Google adds it to the list API. Verify it shows up:

curl "https://generativelanguage.googleapis.com/v1beta/models?key=$GEMINI_API_KEY" \
  | jq '[.models[] | select(.supportedGenerationMethods[] | contains("generateContent")) | .name]'

Token/chat cost is priced through @pydantic/genai-prices. NodeTool maps the "gemini" provider to "google" in GENAI_PROVIDER_MAP.

2. Image model

Open packages/runtime/src/providers/gemini-provider.ts and add an entry to getAvailableImageModels():

async getAvailableImageModels(): Promise<ImageModel[]> {
  return [
    {
      id: "gemini-3.1-flash-image",
      name: "Gemini 3.1 Flash Image",
      provider: "gemini"
    },
    { id: "gemini-3-pro-image", name: "Gemini 3 Pro Image", provider: "gemini" }
  ];
}

The "gemini-" prefix uses the native generateContent endpoint. Legacy Imagen IDs use predict. Check Google’s deprecation table before adding an Imagen model.

3. Gemini native image model (`gemini-*`)

Same as above, but use a "gemini-" prefixed ID. The textToImage() dispatcher routes it to generateContent with responseModalities: ["IMAGE", "TEXT"] automatically.

Use the exact ID returned by Google. Do not add guessed future IDs.

4. Veo video model

Open getAvailableVideoModels() and add an entry:

override async getAvailableVideoModels(): Promise<VideoModel[]> {
  return [
    { id: "veo-3.1-generate-preview", name: "Veo 3.1 Preview", provider: "gemini" },
    { id: "veo-3.1-fast-generate-preview", name: "Veo 3.1 Fast Preview", provider: "gemini" },
    { id: "veo-3.1-lite-generate-preview", name: "Veo 3.1 Lite Preview", provider: "gemini" }
  ];
}

Veo model IDs must start with "veo-". Check supported durations and resolutions for each variant before adding it.

5. ASR model

async getAvailableASRModels(): Promise<ASRModel[]> {
  return [
    { id: "gemini-3.5-flash", name: "Gemini 3.5 Flash", provider: "gemini" },
    { id: "gemini-3.1-flash-lite", name: "Gemini 3.1 Flash-Lite", provider: "gemini" }
  ];
}

6. TTS model

Add the model and its supported voices:

async getAvailableTTSModels(): Promise<TTSModel[]> {
  const voices = ["Zephyr", "Puck" /*, ... existing voices ... */];
  return [
    { id: "gemini-3.1-flash-tts-preview", name: "Gemini 3.1 Flash TTS Preview", provider: "gemini", voices }
  ];
}

7. Embedding model

async getAvailableEmbeddingModels(): Promise<EmbeddingModel[]> {
  return [
    { id: "gemini-embedding-2", name: "Gemini Embedding 2", provider: "gemini", dimensions: 3072 }
  ];
}

Verify

# 1. Type-check all packages
npm run typecheck

# 2. Lint
npm run lint

# 3. Run all tests
npm run test

# 4. Smoke-test a new image node (requires GEMINI_API_KEY in env or DB)
npm run dev:nodetool -- node run nodetool.image.TextToImage \
  --props '{"prompt": "a red apple", "model": {"type": "image_model", "id": "gemini-3.1-flash-image", "provider": "gemini", "name": "Gemini 3.1 Flash Image"}}'

# 5. Smoke-test via chat agent (text model — auto-discovered, no list change needed)
npm run dev:chat -- --provider gemini --model gemini-3.5-flash

# Combined (typecheck + lint + test):
npm run check

Contributing

Open a PR at https://github.com/nodetool-ai/nodetool. Run npm run check (typecheck + lint + test) before pushing. Join the discussion on Discord.

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