Gemini 3 Pro
google/gemini-3-proNatively multimodal frontier: text, vision and audio in one model with a million-token window.
1M ctx · Free
Frontier multimodality at planetary scale
Models
5
Founded
1998
Country
United States
Mountain View
Founded 1998. DeepMind model era from 2023.
Google builds its models out of Mountain View, United States. Webparam tracks five of its models, spanning language, video, image and embeddings: free to call and reaching 1M.
Popular models
The three highest-usage models from this lab on Webparam.
google/gemini-3-proNatively multimodal frontier: text, vision and audio in one model with a million-token window.
1M ctx · Free
google/gemini-2.5-flashThe high-volume multimodal default, 1M context and vision input at workhorse prices.
1M ctx · Free
google/veo-3.1State-of-the-art video generation with native audio and precise cinematic control.
— ctx · Free
Full catalogue
Sorted by weekly usage. Choose any column header to reorder.
| Category | Actions | ||||
|---|---|---|---|---|---|
Gemini 3 Progoogle/gemini-3-pro | Language | 1M | $0.00/1M tokensFree tier | ||
Gemini 2.5 Flashgoogle/gemini-2.5-flash | Language | 1M | $0.00/1M tokensFree tier | ||
Veo 3.1google/veo-3.1 | Video | — | $0.00/video-secFree tier | ||
Imagen 4google/imagen-4 | Image | — | $0.00/imageFree tier | ||
Gemini Embeddinggoogle/gemini-embedding | Embeddings | 8K | $0.00/1M tokensFree tier |
Recent releases
Google DeepMind's Gemini line is natively multimodal (text, vision and audio in one system) backed by custom TPU infrastructure nobody else can match. Veo and Imagen lead generative media, and Gemini's long-context engineering set the 1M-token standard the industry chased.
Google DeepMind's Gemini line is natively multimodal (text, vision and audio in one system) backed by custom TPU infrastructure nobody else can match. Veo and Imagen lead generative media, and Gemini's long-context engineering set the 1M-token standard the industry chased.
Every model above is callable through one OpenAI-compatible convention: change the model ID, keep the request.
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