Google AI Models — Complete Guide
🔵 Google AI Models — Complete Guide
Section titled “🔵 Google AI Models — Complete Guide”Google’s AI model portfolio divides into two distinct families: Gemini (cloud-hosted, frontier models) and Gemma (open-weight, self-hostable models). Both families support multimodal inputs and share architectural DNA with Google DeepMind’s research.
🗺️ Google AI Model Families
Section titled “🗺️ Google AI Model Families”| 🗂️ Family | 🎯 Purpose | 🏃 Where It Runs | 📜 Licence |
|---|---|---|---|
| 🔵 Gemini 3.x | Latest frontier models | Cloud (Gemini API · Google Cloud) | Proprietary |
| 🔵 Gemini 2.5 | Previous frontier generation | Cloud (Gemini API · Google Cloud) | Proprietary |
| 🟢 Gemma 4 | Open-weight general LLM (2026) | Self-hosted · Cloud | Apache 2.0 |
| 🟢 Gemma 3 | Open-weight general LLM (2025) | Self-hosted · Cloud | Apache 2.0 |
| 🩺 MedGemma | Medical specialised open model | Self-hosted | Apache 2.0 |
| 🛡️ ShieldGemma | Safety and content filtering | Self-hosted | Apache 2.0 |
📊 Current Gemini Model Overview
Section titled “📊 Current Gemini Model Overview”| 🤖 Model | 📅 Released | ⚙️ Context | 🎯 Best For | ⚡ Speed |
|---|---|---|---|---|
| 🚀 Gemini 3.5 Flash | May 2026 | 1M tokens | Agentic tasks, production speed | Fastest |
| 💎 Gemini 3.1 Pro | Feb 2026 | 1M tokens | Research, complex coding, analysis | Slower |
| ⚖️ Gemini 3 Flash | Dec 2025 | 1M tokens | Balanced production workloads | Fast |
| 🧠 Gemini 2.5 Pro | 2025 | 1M tokens | Complex reasoning, multimodal | Medium |
| ⚡ Gemini 2.5 Flash | 2025 | 1M tokens | Speed + intelligence balance | Fast |
| ⚡ Gemini 2.5 Flash-Lite | 2025 | 1M tokens | High-volume, cost-efficient | Fastest (2.5 gen) |
🚀 Gemini 3.x — Latest Generation
Section titled “🚀 Gemini 3.x — Latest Generation”🚀 Gemini 3.5 Flash — Fastest Frontier Model (May 2026)
| 🔧 Property | 📋 Detail |
|---|---|
| 📅 Released | May 2026 (Google I/O 2026) |
| 📄 Context window | 1,000,000 tokens |
| 🎯 Focus | Agentic tasks · fast production workloads |
| 🌐 Access | Gemini API · Google AI Studio · Google Cloud Vertex AI |
🏆 Benchmarks:
- Beats Gemini 3.1 Pro on 11 of 15 published benchmarks
- First Flash-tier model to compete with Pro-tier in agentic workflows
✅ Ideal for:
- 🤖 Agentic pipelines with many tool calls
- ⚡ High-speed production chat and assistants
- 💻 Code generation and explanation
- 📊 Document analysis at scale
- ⏱️ Latency-sensitive applications needing frontier intelligence
💡 Example:
“Given this product requirements document and codebase, plan the implementation steps, identify risks, and generate the skeleton code with test placeholders.”
💎 Gemini 3.1 Pro — Premium Research and Coding (Feb 2026)
| 🔧 Property | 📋 Detail |
|---|---|
| 📅 Released | February 2026 |
| 📄 Context window | 1,000,000 tokens |
| 🎯 Focus | Complex reasoning · advanced coding · scientific analysis |
| 🌐 Access | Gemini API · Google AI Studio · Google Cloud Vertex AI |
🏆 Benchmarks:
- 🔢 80.6% on SWE-bench Verified (software engineering)
- 🔬 94.3% on GPQA Diamond (scientific reasoning)
- 🧩 77.1% on ARC-AGI-2
✅ Ideal for:
- 🔬 Scientific research and academic analysis
- 🏗️ Complex software architecture decisions
- 📄 Long-context document analysis (research papers, legal, financial)
- 🧠 Tasks requiring the deepest available reasoning
💡 Example:
“Analyse this clinical trial dataset (attached), identify statistically significant patterns, and write a structured findings section suitable for peer review.”
⚖️ Gemini 3 Flash — Balanced Default (Dec 2025)
| 🔧 Property | 📋 Detail |
|---|---|
| 📅 Released | December 2025 |
| 📄 Context window | 1,000,000 tokens |
| 🎯 Focus | Frontier intelligence at production speed |
| 🌐 Access | Gemini API · Google AI Studio · Gemini app (default) |
✅ Ideal for:
- 💬 Production chat assistants
- 📝 Writing, summarisation, and editing
- 💻 Everyday coding and explanation
- 🔄 General-purpose multimodal workloads
🧠 Gemini 2.5 — Previous Generation (Still Supported)
Section titled “🧠 Gemini 2.5 — Previous Generation (Still Supported)”🧠 Gemini 2.5 Pro — Deep Reasoning
| 🔧 Property | 📋 Detail |
|---|---|
| 📄 Context window | 1,000,000 tokens |
| 🎯 Focus | Adaptive thinking · complex agentic + multimodal challenges |
| 🌐 Access | Gemini API · Google Cloud Vertex AI |
✅ Best for:
- 🔬 Tasks that need the model to “think before answering” (adaptive thinking mode)
- 🤖 Complex agentic and multimodal workflows
- 📄 Very long document or codebase analysis
⚡ Gemini 2.5 Flash — Speed + Intelligence
| 🔧 Property | 📋 Detail |
|---|---|
| 📄 Context window | 1,000,000 tokens |
| 🎯 Focus | Balance of intelligence and latency |
| 🌐 Access | Gemini API · Google Cloud Vertex AI |
| 🧠 Special feature | Controllable “thinking budget” — adjust reasoning depth per request |
✅ Best for:
- ⚡ Production workloads needing tunable reasoning depth
- 💬 Assistants where latency and quality both matter
- 🔄 Versatile multimodal tasks
⚡ Gemini 2.5 Flash-Lite — High-Volume Efficiency
| 🔧 Property | 📋 Detail |
|---|---|
| 📄 Context window | 1,000,000 tokens |
| 🎯 Focus | Massive scale — cost and speed |
| 🌐 Access | Gemini API |
✅ Best for:
- 🏷️ Classification and extraction at very high volume
- 📱 Latency-critical mobile or edge API workloads
- 💰 Cost-optimised production pipelines
🟢 Gemma — Google’s Open-Weight Models
Section titled “🟢 Gemma — Google’s Open-Weight Models”The Gemma family gives developers open-weight models they can download, fine-tune, and self-host — built on the same architecture and training approach as Gemini.
🟢 Gemma 4 — Latest Open Model (April 2026)
| 🔧 Property | 📋 Detail |
|---|---|
| 📅 Released | April 2, 2026 |
| 📜 Licence | Apache 2.0 — free to use, modify, and distribute |
| ⚙️ Sizes | E2B · E4B · 26B A4B · 31B |
| 🌐 Access | Google AI · Hugging Face · Kaggle |
💡 What “E2B” and “26B A4B” mean:
- E2B / E4B = 2B and 4B effective parameter MoE models (only a subset of parameters active at inference)
- 26B A4B = 26B total parameters, 4B active — large-model quality at small-model inference cost
- 31B = dense model variant
✅ Best for:
- 🏠 Self-hosted deployment on local servers or cloud VMs
- 🔧 Fine-tuning on proprietary domain data
- 🔒 Privacy-sensitive workloads where data cannot leave the organisation
- 💰 Very high-volume inference where API costs are prohibitive
🟢 Gemma 3 — Production Open Model (March 2025)
| 🔧 Property | 📋 Detail |
|---|---|
| 📅 Released | March 12, 2025 |
| 📜 Licence | Apache 2.0 |
| ⚙️ Sizes | 1B · 4B · 12B · 27B |
| 📄 Context window | 128,000 tokens |
| 👁️ Modality | Text + image input (all sizes) |
| 🌍 Languages | 140+ languages |
| 🌐 Access | Google AI · Hugging Face · Kaggle · NVIDIA NIM |
✅ Best for:
- 📱 Mobile and edge deployment (1B, 4B sizes)
- 💬 Self-hosted chat assistants
- 🔍 RAG pipelines on private data
- 💻 Code generation and explanation (12B, 27B sizes)
- 🌍 Multilingual applications
📦 Gemma 3n — Mobile-Optimised Variant:
Gemma 3n is specifically optimised for phones, laptops, and tablets — designed for on-device AI without a GPU.
🩺 Specialist Gemma Models
Section titled “🩺 Specialist Gemma Models”🩺 MedGemma — Medical AI (4B and 27B)
| 🔧 Property | 📋 Detail |
|---|---|
| ⚙️ Sizes | 4B · 27B |
| 🎯 Focus | Medical image analysis and clinical text understanding |
| 📜 Licence | Apache 2.0 |
✅ Use for:
- 🏥 Medical image analysis (radiology, pathology)
- 🩺 Clinical text understanding and extraction
- 🔬 Biomedical research assistance
⚠️ Not a replacement for clinical judgement — requires validation for any medical decision support use.
🛡️ ShieldGemma 2 — Safety and Content Filtering (4B)
| 🔧 Property | 📋 Detail |
|---|---|
| ⚙️ Size | 4B |
| 🎯 Focus | Classifying violent, dangerous, or sexually explicit images |
| 📜 Licence | Apache 2.0 |
✅ Use for:
- 🛡️ User-generated content moderation
- 🚨 Abuse detection pipelines
- 🔒 Pre-screening images before passing to other models
🎯 Which Google Model to Use
Section titled “🎯 Which Google Model to Use”| 🎯 Task | ✅ Best Model | 💡 Why |
|---|---|---|
| 🤖 Agentic pipeline with many tool calls | Gemini 3.5 Flash | Best agentic performance at Flash speed |
| 🔬 Research or complex scientific analysis | Gemini 3.1 Pro | Highest reasoning depth |
| 💻 Complex coding or architecture decisions | Gemini 3.1 Pro | Strong SWE-bench scores |
| 💬 Production chat assistant | Gemini 3 Flash | Balanced frontier intelligence at speed |
| 🧠 Task needing tunable reasoning depth | Gemini 2.5 Flash | Controllable thinking budget |
| 🏷️ High-volume classification/extraction | Gemini 2.5 Flash-Lite | Cheapest in the 2.5 family |
| 🏠 Self-hosted / on-premise | Gemma 4 (26B A4B) | Best open-weight quality |
| 📱 Mobile or edge device deployment | Gemma 3 (1B/4B) or Gemma 3n | Smallest and most efficient |
| 🩺 Medical image or clinical text | MedGemma (27B) | Specialist medical fine-tune |
| 🛡️ Image safety filtering | ShieldGemma 2 (4B) | Built for content classification |
🌐 Where to Access Google AI Models
Section titled “🌐 Where to Access Google AI Models”| 🏷️ Platform | 🤖 Models Available |
|---|---|
| 🔵 Google AI Studio (ai.google.dev) | All Gemini models · Free tier available |
| 🔵 Gemini API | All Gemini models · Pay-per-token |
| ☁️ Google Cloud Vertex AI | Gemini models · Enterprise SLA |
| 💬 Gemini App | Gemini 3 Flash (default) · Pro via Gemini Advanced |
| 🤗 Hugging Face | Full Gemma family |
| 🐙 Kaggle | Full Gemma family |
| 🟩 NVIDIA NIM | Gemma 3 family |