Google

Gemini 2.5 Flash

Google

Improved across key benchmarks for reasoning, multimodality, code and long context while getting even more efficient. Best for fast performance on complex tasks.

Gemini Diffusion

Google

Our state-of-the-art, experimental text diffusion model that uses diffusion techniques to explore a new kind of language model that gives users greater control, creativity, and speed in text generation.

Gemma 3n

Google

Gemma 3n is a mobile-first AI model built on a groundbreaking architecture optimized for on-device performance. It features Per-Layer Embeddings (PLE) for reduced RAM usage, allowing larger models to run on mobile devices with a memory footprint comparable to much smaller models. Supports multimodal understanding including text, images, audio, and video.

MedGemma

Google

The MedGemma collection contains Google's most capable open models for medical text and image comprehension, built on Gemma 3. Developers can use MedGemma to accelerate building healthcare-based AI applications. MedGemma comes in two variants: a 4B multimodal version and a 27B text-only version. The 4B version utilizes a SigLIP image encoder specifically pre-trained on de-identified medical data including chest X-rays, dermatology images, ophthalmology images, and histopathology slides.

Gemini 2.5 Pro

Google

Gemini 2.5 Pro is capable of reasoning through its thoughts before responding, resulting in enhanced performance and improved accuracy. Features Deep Think, an enhanced reasoning mode, and native audio outputs that capture subtle nuances of speech.

Gemma 3

Google

Gemma 3 is a collection of lightweight, state-of-the-art open models built from the same research and technology that powers Gemini 2.0 models. The most capable model you can run on a single GPU or TPU, delivering state-of-the-art performance for its size and outperforming Llama3-405B, DeepSeek-V3 and o3-mini in preliminary human preference evaluations on LMArena's leaderboard. Features advanced text and visual reasoning capabilities, 128K token context window, function calling, and support for over 140 languages.

Gemini 2.0 Flash-Lite

Google

Best for cost-efficient performance. Better quality than 1.5 Flash, at the same speed and cost. Features a 1 million token context window and multimodal input.

Gemini 2.0 Flash

Google

Next iteration of Gemini, released in three versions (Flash, Flash-Lite, Pro). Represents Google's state-of-the-art multimodal model, likely incorporating Mixture-of-Experts at unprecedented scale and targeting dominance in both text and image understanding.

Gemini 2.0 Pro

Google

Google's best model yet for coding performance and complex prompts, with better understanding and reasoning of world knowledge than any previous release. Features a massive 2 million token context window and the ability to call tools like Google Search and code execution.

Gemini 1.5

Google

Upgrade over Gemini 1.0, featuring a **1 million+ token context window** (initially 1M, later expanded to 2M). Likely uses a Mixture-of-Experts backend and improved multimodal abilities. Released in variants (e.g. Flash).

Gemini 1.0

Google

First version of Google's Gemini, a multimodal model built by the joint Google DeepMind team. Comes in three sizes; integrated into a chatbot (Google's next-gen model after PaLM 2) with planned image and text capabilities.

PaLM 2

Google

Second-generation PaLM, actually a family of models (up to ~340B dense params) with training on a diverse multilingual dataset. Powers Google's Bard and other products; improved coding, reasoning, and multilingual skills over PaLM.

PaLM

Google

540B-parameter Transformer model trained with the Pathways system. Showed strong few-shot performance and was the largest dense model publicly disclosed as of 2022.

Chinchilla

Google

70B-parameter model trained on 4× more data than typical, to be compute-optimal. Achieved superior accuracy vs much larger models, validating data-scaling laws.

LaMDA

Google

137B-parameter dialogue-optimized language model ("Language Model for Dialogue Applications"). Known for conversational ability and for sparking discussions on AI sentience.

GLaM (Generalist Language Model)

Google

A 1.2 trillion-parameter **Mixture-of-Experts** Transformer model. Uses sparse activation (64 experts) to reduce inference cost relative to model size.

Gopher

Google

A 280B-parameter Transformer model from DeepMind, used to explore scaling laws and as a precursor to Chinchilla.

T5 (Text-To-Text Transfer Transformer)

Google

Encoder-decoder Transformer trained on a unified text-to-text format for diverse NLP tasks. Enabled prompt-based multitask learning.

XLNet

Google

Autoregressive Transformer that uses permuted language modeling as an alternative to BERT. Achieved state-of-the-art on several NLP tasks in 2019.

BERT

Google

Bidirectional Transformer pre-trained for language understanding (not generative). Influential for NLP tasks with masked-language modeling.