Google Gemini all models available: full lineup, roles, and platform exposure
- Graziano Stefanelli
- 1 hour ago
- 4 min read
Google Gemini is no longer presented as a single model or even a simple tiered family.
Instead, Gemini is now an ecosystem of model families, variants, and operating modes that are selectively exposed across consumer apps, developer tools, and enterprise platforms.
Here we share how the Gemini model lineup is structured today, which models are actually available, where each one appears, and how Google differentiates speed, reasoning depth, and cost across the Gemini stack.
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Gemini 3.0 represents Google’s current-generation foundation models.
The Gemini 3.0 family is the latest core generation and sits at the center of Google’s AI strategy.
These models power the Gemini app, Google Search AI responses, Google AI Studio, and Vertex AI.
Gemini 3.0 models are designed to unify long-context reasoning, multimodality, and scalable deployment.
They replace earlier Gemini 1.x models and progressively supersede parts of the 2.5 family.
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Gemini 3 Flash is the default, speed-first model across consumer products.
Gemini 3 Flash is optimized for low latency, high throughput, and cost efficiency.
It is the default model in the Gemini web and mobile apps and the primary engine behind AI-powered Google Search experiences.
Flash delivers strong general reasoning while prioritizing responsiveness and scalability.
This makes it suitable for everyday chat, summaries, coding assistance, and high-volume interactions.
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Gemini 3 Flash profile
Aspect | Description |
Role | Default consumer model |
Focus | Speed and scale |
Context window | Up to ~1,000,000 tokens (developer surfaces) |
Multimodality | Text, images, documents, PDFs |
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Gemini 3 Pro is the flagship for deep reasoning and enterprise workflows.
Gemini 3 Pro is Google’s highest-capability standalone Gemini model.
It is tuned for complex reasoning, agentic workflows, and structured multi-step tasks.
Pro is commonly used in Google AI Studio and Vertex AI rather than as a default consumer option.
Its higher cost and latency are offset by greater consistency on demanding analytical workloads.
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Gemini 3 Pro profile
Aspect | Description |
Role | Flagship reasoning model |
Focus | Accuracy and depth |
Context window | Up to ~1,000,000 tokens |
Typical usage | Agents, research, enterprise pipelines |
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Gemini 3 Thinking is a reasoning mode layered on Gemini 3 models.
Gemini 3 Thinking, sometimes labeled Deep Think, is not a separate model checkpoint.
It is a compute-intensive operating mode that allows Gemini 3 models to spend more internal reasoning budget per request.
Thinking trades speed for accuracy and multi-step reasoning stability.
It is surfaced as a toggle or configuration option rather than a standalone model selection.
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Gemini 3 Thinking characteristics
Aspect | Description |
Type | Reasoning mode |
Latency | Higher |
Accuracy | Highest for complex logic |
Availability | Gemini app (eligible tiers), AI Studio |
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The Gemini 2.5 family remains widely available and cost-stable.
Gemini 2.5 models continue to be offered alongside Gemini 3.0.
They provide a balance between strong reasoning and predictable cost profiles.
Many production systems still rely on 2.5 models for stability and budget control.
Google has not fully deprecated this family.
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Gemini 2.5 Pro targets high-quality reasoning at lower cost than 3 Pro.
Gemini 2.5 Pro delivers advanced reasoning with a slightly reduced performance ceiling compared to Gemini 3 Pro.
It remains popular in Vertex AI deployments where cost efficiency matters.
Its large context window supports long-document analysis and RAG pipelines.
For many enterprise use cases, it represents a practical compromise.
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Gemini 2.5 Pro profile
Aspect | Description |
Role | Cost-efficient reasoning |
Focus | Stability and value |
Context window | Large-scale |
Typical usage | Enterprise production workloads |
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Gemini 2.5 Flash and Flash-Lite address high-volume and low-cost needs.
Gemini 2.5 Flash is optimized for speed and throughput in API-driven environments.
Gemini 2.5 Flash-Lite further reduces cost and latency by limiting reasoning depth.
These models are designed for routing, summarization, and lightweight tasks.
They are not intended for deep analytical workflows.
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Gemini 2.5 Flash variants
Model | Primary goal |
Flash | Fast general-purpose inference |
Flash-Lite | Ultra-low cost, minimal reasoning |
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Gemini 2.5 Flash Image supports vision-heavy analysis.
Gemini 2.5 Flash Image is specialized for image understanding and visual analysis.
It is used for vision tasks rather than image generation.
This variant processes image-heavy prompts more efficiently than text-first models.
It is typically combined with text-focused models in multimodal pipelines.
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Legacy Gemini models remain visible but are no longer recommended.
Gemini 1.5 Pro and 1.5 Flash introduced large-context reasoning to Gemini.
They are now considered legacy models.
Existing projects may still access them, but new development is encouraged to move to 2.5 or 3.0.
Google positions these models in maintenance mode only.
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Model availability depends on the platform surface.
Gemini app users typically see only a simplified subset of models.
Google AI Studio exposes a broader selection for experimentation.
Vertex AI provides the full Gemini catalog with pricing and quota controls.
This layered exposure reduces complexity for consumers while preserving flexibility for developers.
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Gemini model availability by platform
Platform | Available models |
Gemini app | 3 Flash, 3 Pro, Thinking mode |
Google AI Studio | 3 Flash, 3 Pro, 2.5 family |
Vertex AI | Full 2.5 and 3.0 lineup |
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Google’s Gemini strategy prioritizes routing over manual model choice.
Google increasingly relies on internal routing to match tasks with the appropriate Gemini variant.
Users are shielded from excessive model selection complexity.
Developers retain control where precision is required.
This strategy reflects Google’s focus on scale, efficiency, and consistent user experience.
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