Google Gemini Models Explained: Pro, Flash, Flash-Lite, Omni, Nano Banana and More
GuidesThursday, 30 July 2026 at 11:00
The current Google Gemini lineup includes Gemini 3.1 Pro for demanding reasoning, Gemini 3.6 Flash for efficient everyday and agentic work, Gemini 3.5 Flash-Lite for high-throughput tasks and specialized families for deep reasoning, images, audio, video, cyber and robotics.
That short description hides an important complication: users do not see the same model catalog everywhere.
The Gemini app may expose a small set of model choices and automatically route features. Google AI Studio and the Gemini API use exact model IDs. Gemini Enterprise Agent Platform offers a managed enterprise catalog. Search, Workspace and Android can use Gemini without giving the user a conventional model picker.
This guide owns model-selection intent. For features, plans and product surfaces, begin with the complete Google Gemini guide.
Model availability was checked on July 24, 2026. Preview IDs and supported models can change quickly. Production developers should verify the official Gemini API model list and deprecation notices before deployment.
The current Gemini model families at a glance
| Model or family | Designed for | Where you may encounter it |
| Gemini 3.1 Pro | Difficult reasoning, coding and multimodal analysis | Gemini paid access, API preview, enterprise services |
| Gemini 3.1 Deep Think | Extended reasoning for science, research and engineering | Selected premium and research access |
| Gemini 3.6 Flash | Strong general capability with lower latency and token cost | Gemini app, API, enterprise platform |
| Gemini 3.5 Flash-Lite | High-volume, low-latency and cost-sensitive workloads | API and enterprise workloads |
| Gemini 3.5 Flash Cyber | Defensive vulnerability discovery and remediation | Restricted pilot for governments and trusted partners |
| Gemini Omni | Multimodal understanding, creation and editing, beginning with video | Gemini app, Google Flow, expanding developer access |
| Gemini Image/Nano Banana | Image generation and editing | Gemini, AI Studio, API and creative tools |
| Gemini Audio | Real-time speech, translation and generative audio | Live experiences and developer services |
| Gemini Robotics | Reasoning and action in physical environments | Research and robotics partnerships |
| Gemma | Lightweight open models built from related Google research | Local, cloud and custom developer deployments |
The table groups model families by purpose. Exact availability, status and model IDs differ.
Gemini model versus Gemini product
The model is the intelligence component. The product provides the interface, tools, memory, data access and policy layer.
For example, Deep Research is not merely “Gemini 3.1 Pro with a long prompt.” It can plan a research process, search and browse sources, manage intermediate work and produce a report. Gemini Live combines models with streaming audio, camera or screen input. Workspace features combine Gemini with the user’s document and organizational permissions.
This distinction matters because:
- changing the model may not change the available tools;
- the same model can behave differently under different product instructions;
- an application can add retrieval, memory and safety systems around the model;
- privacy terms follow the service and account, not only the model name.
Do not compare an API model response with a complete assistant workflow as if they were the same product.
Gemini 3.6 Flash: the current workhorse
Google introduced Gemini 3.6 Flash on July 21, 2026. It is positioned as an efficient general model for coding, knowledge work, multimodal analysis and agentic tasks.
Google says 3.6 Flash uses fewer output tokens than 3.5 Flash while improving several capabilities. Its public launch price was $1.50 per million input tokens and $7.50 per million output tokens through the developer service.
The model is available in:
- the Gemini app;
- Google AI Studio and the Gemini API;
- Gemini Enterprise Agent Platform;
- the Gemini Enterprise app;
- and selected developer environments.
When to choose 3.6 Flash
Use it as the default starting point for:
- chat and assistance;
- document parsing;
- chart and data interpretation;
- routine coding;
- report drafting;
- tool-using agents;
- tasks where Pro-level cost or latency is unnecessary.
Flash does not mean “small and unintelligent.” It describes a speed-and-efficiency position in the lineup. Test the model on your actual workload rather than assuming the most expensive model always produces the best operational result.
Gemini 3.1 Pro: complex reasoning and multimodal work
Gemini 3.1 Pro is Google’s high-capability model for tasks requiring stronger reasoning across text, images, video, audio and code. It supports a very large input context in the API and is aimed at difficult analysis, software engineering and knowledge work.
In the consumer app, Google advertises varying free access and higher paid access. In the developer catalog, model status can be preview or otherwise restricted.
When to choose Pro
Pro is the stronger candidate for:
- complicated multi-constraint decisions;
- demanding code design and debugging;
- analysis across a large mixed-media source set;
- research synthesis;
- difficult mathematics or technical reasoning;
- tasks where a more deliberate answer is worth added time and cost.
When not to choose Pro
Pro can be wasteful for:
- classification at large volume;
- simple extraction;
- short rewrites;
- routing requests;
- basic summaries;
- latency-sensitive chat;
- subagents performing narrow steps.
A robust system often combines models. A stronger model can plan and verify while faster models perform repeated bounded operations.
Gemini 3.1 Deep Think
Deep Think is designed for extended reasoning on difficult scientific, engineering and research problems. It is better understood as a high-compute reasoning mode than as an ordinary default chat choice.
Use it when:
- the problem is genuinely hard;
- the answer can be checked;
- more latency is acceptable;
- reasoning depth matters more than throughput.
Do not use Deep Think to add theatrical deliberation to an unclear task. More compute does not replace missing facts, an invalid premise or a human decision-maker.
For high-stakes research, ask the system to separate assumptions, intermediate conclusions and evidence. Then verify the result independently.
Gemini 3.5 Flash-Lite: speed and scale
Gemini 3.5 Flash-Lite is optimized for high throughput, low latency and price-sensitive agentic workflows. Google launched it at $0.30 per million input tokens and $2.50 per million output tokens.
Typical uses include:
- extraction from many documents;
- translation and summarization at volume;
- simple subagent tasks;
- search and retrieval processing;
- routing and classification;
- repetitive structured transformation;
- real-time products where response speed matters.
Flash-Lite can be configured with different thinking levels in supported APIs. This lets developers trade some latency and cost for more reasoning when the individual task requires it.
The label “Lite” should not be treated as a permanent quality ranking. On bounded workloads, a smaller or faster model can outperform a larger one because it follows the required pattern more consistently and economically.
Gemini 3.5 Flash Cyber
Gemini 3.5 Flash Cyber is a specialized model for identifying and helping repair software vulnerabilities. Google pairs it with CodeMender and has described its release as a limited-access pilot for governments and trusted partners.
It is not a general consumer coding model. The restricted deployment reflects the dual-use nature of offensive and defensive cybersecurity capabilities.
Security teams should not infer availability from a public announcement. Access, permitted use and safeguards must be confirmed through the relevant program.
Gemini Omni: understanding meets creation
Gemini Omni is a multimodal family intended to transform mixed inputs into created or edited outputs. The first public focus is video.
Gemini Omni Flash can use combinations of text, images, video and audio as references and produce a unified video result. It appears in the Gemini app and Google Flow for eligible subscribers, with broader API and enterprise availability rolling out separately.
How Omni differs from ordinary Gemini
Conventional Gemini models primarily return text, code or structured reasoning about multimodal inputs. Omni is designed to create and edit rich media while retaining world understanding and conversational control.
Use it when the desired output is a media artifact, not merely an explanation of one.
Gemini Omni versus Veo
Google says Omni is replacing Veo inside the Gemini app for conversational video generation and editing. Veo continues as a dedicated Google DeepMind video model and can remain available through other creative or developer surfaces.
Product location matters. “Replaced in Gemini” does not mean the Veo research and developer family has ceased to exist.
Gemini Image and Nano Banana
Nano Banana is the product nickname for Google’s Gemini Image models.
The current family includes:
- Nano Banana 2: high-quality image generation and editing at Flash speed;
- Nano Banana 2 Lite: faster and lower-cost production;
- Nano Banana Pro: higher precision and control for studio-oriented work.
These models can generate images, edit supplied images, follow reference material and maintain more context than a basic text-to-image system.
Use Pro when fine control, typography, layout or complex editing justifies the cost. Use the faster variants for iteration and high-volume production.
Generated images still require review for:
- factual details;
- brand and trademark use;
- identifiable people;
- manipulated documentary scenes;
- unsafe or deceptive applications;
- small text and numerical accuracy.
Gemini Audio
Gemini Audio covers advanced real-time audio systems, including speech interaction and translation. Gemini 3.5 Live Translate supports low-latency speech-to-speech translation across many languages.
Audio models can support:
- live translation;
- conversational voice interfaces;
- transcription and understanding;
- expressive speech;
- real-time multimodal agents.
Language support does not guarantee equal quality across accents, dialects, noisy environments or specialist vocabulary. Human interpretation remains necessary for legal, medical and safety-critical communication.
Lyria is a separate Google DeepMind family for music generation. It belongs to the wider Google generative-media portfolio but should not be called a general Gemini reasoning model.
Gemini Robotics
Gemini Robotics adapts multimodal and agentic capabilities to physical systems. These models reason about objects, space, instructions and actions in a real environment.
Robotics adds risks that do not exist in an ordinary chat:
- physical harm;
- sensor errors;
- unexpected environments;
- timing and control failures;
- responsibility for autonomous action.
The relevant evaluation is not whether a model writes a convincing plan. It is whether the complete robot behaves safely under real-world constraints.
Is Gemini Nano still a model?
The original Gemini 1.0 lineup used Ultra, Pro and Nano to describe different deployment scales. Nano referred to efficient on-device models.
Google’s current public product language emphasizes newer Gemini, Flash, Pro, Omni and specialized families. On-device intelligence remains strategically important across Android and Pixel, but readers should not assume that “Gemini Nano” is the user-selectable equivalent of the current cloud model catalog.
For device features, consult the specific Android or hardware documentation. The model name alone does not reveal whether processing is local, cloud-based or hybrid.
Gemini versus Gemma
Gemini and Gemma are related but different.
| Gemini | Gemma |
| Primarily proprietary hosted model family | Open-model family with downloadable weights under Google’s terms |
| Used across Gemini app, Google services, API and enterprise products | Used for local, customized and developer-controlled deployments |
| Includes frontier multimodal and agentic systems | Emphasizes efficient models and specialized variants |
| Google operates the hosted service | The deployer can operate the model in another environment |
Google describes Gemma as built from the same research and technology used for Gemini. Gemma 4 is its current major open-model generation as of this review.
Choose Gemma when local control, customization, model weights or deployment flexibility matters. Choose hosted Gemini when access to Google’s frontier models, managed tooling and integrated services matters more.
Pro versus Flash versus Flash-Lite
| Decision factor | Pro | Flash | Flash-Lite |
| Reasoning depth | Highest general target | Strong | Bounded and configurable |
| Latency | Usually higher | Lower | Lowest target |
| Cost | Higher | Middle | Lowest |
| High-volume processing | Expensive | Good | Best fit |
| Difficult coding | Strongest starting point | Often sufficient | Narrow steps |
| Agent orchestration | Planner or verifier | General agent work | Subagents and repetitive actions |
| Everyday consumer use | Premium or varying access | Default workhorse | Usually behind the scenes |
This is a starting framework, not a benchmark verdict. Evaluate quality, latency and cost on a representative dataset.
How to choose a Gemini model
Step 1: define the output
Specify whether you need prose, JSON, code, an image, video, audio, an embedding or a physical action.
Step 2: identify the hard part
Is the challenge reasoning, volume, latency, media fidelity, context length, tool use or cost?
Step 3: start with the cheapest plausible model
Run the workload on Flash-Lite or Flash when appropriate. Move to Pro only when the evaluation shows a meaningful quality gain.
Step 4: use a test set
Collect realistic prompts and expected criteria. Include difficult, ambiguous and unsafe cases.
Step 5: measure the complete system
Track:
- task success;
- factual error;
- structured-output validity;
- latency;
- input and output tokens;
- tool failures;
- refusals;
- human correction time;
- total cost per usable result.
Step 6: plan for model change
Pin exact model IDs in production, watch deprecation notices and maintain a migration test. Preview aliases can move or disappear.
Context windows and “one million tokens”
Several Gemini models support context windows of up to one million input tokens in eligible surfaces. This is enough capacity for very large document, code or media collections.
It does not mean:
- every consumer account has the same allowance;
- every token is weighted equally;
- all details will be recalled perfectly;
- the output can also be one million tokens;
- one huge prompt is cheaper than retrieval;
- privacy and rights concerns disappear.
Long-context evaluation should test whether the model finds details, preserves relationships and resists misleading content buried inside the input.
Thinking levels
Gemini 3 models can support configurable thinking levels in the developer API. Google currently describes levels such as minimal, low, medium and high, with support varying by model.
The setting controls the relative amount of internal reasoning, not a guaranteed number of visible thinking tokens. Lower levels reduce latency for simple tasks. Higher levels are appropriate for difficult work.
Google recommends keeping Gemini 3 temperature at its default of 1.0 because changing it can degrade complex reasoning behavior. Old prompting habits should not be copied blindly into a new model generation.
Preview, stable and restricted models
Model status matters:
- Stable/GA models are the safer default for production.
- Preview models offer newer capabilities but can change more quickly.
- Experimental models may have tighter limits and limited support.
- Restricted models require approval or a specific customer program.
- Shut-down models should not appear in new applications.
An article saying a model “exists” does not guarantee that a particular account can call it.
Which model does the Gemini app use?
The current consumer plan page lists Gemini 3.6 Flash for free users and varying access to Gemini 3.1 Pro. Paid plans expand access.
Some features can route to specialized models without presenting the user with the raw model ID. Image, video, research and live functions may use their own systems.
If exact model reproducibility matters, use the developer API and record the model identifier. A consumer assistant is designed to evolve, not to serve as a frozen evaluation endpoint.
Frequently asked questions
What is the latest Gemini model?
As of July 24, 2026, Gemini 3.6 Flash is Google’s newest broadly released workhorse model. Google has also announced 3.5 Flash-Lite and is testing 3.5 Pro with partners. “Latest” depends on whether the reader means consumer, API, media or restricted models.
What is Google’s most capable Gemini model?
Gemini 3.1 Pro is the main high-capability general model in current consumer and developer materials, while Deep Think provides additional reasoning for selected difficult problems. Access and status vary.
Is Gemini Flash worse than Pro?
Not for every task. Pro targets difficult reasoning; Flash targets an efficient balance of capability, latency and cost. Flash can be the better production choice for routine or interactive work.
What is Flash-Lite?
Flash-Lite is optimized for high-volume, low-latency and cost-sensitive processing. It is well suited to narrow steps, extraction, transformation and subagent work.
What is Gemini Omni?
Gemini Omni combines multimodal understanding with media creation and editing. Its first major output focus is video.
Is Nano Banana a real model?
Nano Banana is Google’s nickname for its Gemini Image models. The family includes fast, efficient and Pro variants.
Is Gemma the open-source version of Gemini?
Gemma is a separate family of open models built from related Google research and technology. “Open model” or “open weights” is more precise than calling it an identical open-source Gemini.
Can I download Gemini?
Google’s frontier Gemini models are generally accessed as hosted services. Gemma is the family designed for downloadable and customizable model deployments.
Which Gemini model is best for coding?
Start with Gemini 3.6 Flash for routine and agentic coding. Test Gemini 3.1 Pro for difficult architecture, debugging and reasoning. Use a real repository test rather than a generic benchmark alone.
Which model is cheapest?
Pricing varies, but Flash-Lite is designed as the lowest-cost high-volume family. Media, grounding, caching and context can add separate charges.
Bottom line
Choose Gemini models by workload, not prestige.
Use Pro for the hardest general reasoning. Use Flash as the everyday workhorse. Use Flash-Lite when throughput and latency dominate. Use Omni, Nano Banana, Audio, Robotics or other specialized systems when the output or environment requires them.
Then evaluate the surrounding product. The same model can be a consumer assistant, an API component or part of a governed enterprise agent. That context determines tools, data access, reliability and cost.
Continue with the Gemini API and AI Studio guide, compare Gemini pricing, or return to the complete Gemini cornerstone.
byDavid Porter
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