How to Use Gemini 3.5 Flash and Gemini Omni for Free: Entry Points for Users and Developers

After Google released Gemini 3.5 Flash and Gemini Omni, the practical question is not the benchmark score, but how ordinary users and developers can actually use them, which entry points are free, and which ones are only low-friction trials.

The short version:

• For chat, writing, image understanding, and everyday Q&A: use Gemini app first.
• To test Gemini 3.5 Flash parameters, prompts, and multimodal input: use Google AI Studio.
• To call Gemini 3.5 Flash from code: create an API key in AI Studio.
• To try it from the terminal for free: look at Gemini CLI.
• To try Gemini Omni video editing: start with Gemini app and Google Flow.
• For real production use: do not rely on free quotas; move to a paid API or Vertex AI.

Note: free quotas, regional availability, subscription tiers, and model menus change over time. This article was written on May 20, 2026. Before official use, always check Google’s current pages.

Free Gemini 3.5 Flash Method 1: Gemini App

The simplest entry point is Gemini app:

https://gemini.google.com/

The basic flow is straightforward:

1. Open Gemini.
2. Sign in with a Google account.
3. Look for 3.5 Flash in the model selector.
4. Start chatting.

This entry point is best for ordinary users. You can use it for writing, summarization, image understanding, file analysis, everyday Q&A, and simple planning. According to public reports, Gemini 3.5 Flash has been made available to users globally and can be selected from Gemini’s model dropdown.

The limits are also clear: free users usually face daily message, regional, and feature limits. If you exceed the limit, you need to wait for the quota to refresh or upgrade your subscription.

Free Gemini 3.5 Flash Method 2: Google AI Studio

If you want more than chat, and need to tune prompts, inspect parameters, or test structured output, Google AI Studio is a better fit:

https://aistudio.google.com/

Basic flow:

1. Sign in to Google AI Studio.
2. Create a new prompt.
3. Select gemini-3.5-flash in the model dropdown.
4. Enter the prompt and run it.

AI Studio gives you more control. You can adjust temperature, system instructions, structured output, and multi-image input, and you can export a working prompt into code or an API call.

For developers, AI Studio is a free testing bench. Tune the prompt and input format here first, then move into API integration to avoid wasting quota.

Free Gemini 3.5 Flash Method 3: Free API Key

Developers care most about the API. AI Studio can create a Gemini API key for calling gemini-3.5-flash.

Basic flow:

1. Open Google AI Studio.
2. Find Get API key.
3. Select or create a project.
4. Create an API key.
5. Save the key to a local environment variable.

Python example:

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import os
from google import genai

client = genai.Client(api_key=os.environ["GEMINI_API_KEY"])

response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Explain in three sentences what Gemini 3.5 Flash is best suited for."
)

print(response.text)

Node.js example:

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import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

const response = await ai.models.generateContent({
  model: "gemini-3.5-flash",
  contents: "Explain in three sentences what Gemini 3.5 Flash is best suited for."
});

console.log(response.text);

curl example:

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curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.5-flash:generateContent" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"contents":[{"parts":[{"text":"Hello Gemini 3.5 Flash"}]}]}'

Public information suggests that the AI Studio free tier usually gives Gemini Flash models a daily request allowance. The exact numbers can vary by time, region, and account status. Common claims include around 1,500 requests per day, per-minute request limits, and token limits. Do not bake those numbers into a production plan; check Google’s current pricing and limits pages before launch.

Free Gemini 3.5 Flash Method 4: Gemini CLI

If you like the command line, look at Gemini CLI. It is useful for temporary scripts, repository summaries, file reading, and quick Q&A in the terminal.

Installation is usually:

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npm install -g @google/gemini-cli

Then run:

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gemini

The CLI is better suited to personal developer workflows than production integration. Production should still use API keys, service accounts, permission controls, and auditable calling patterns.

Free or Low-Friction Gemini Omni Access: Gemini App and Google Flow

Gemini Omni is a multimodal model for video creation and editing. Its core capability is not ordinary text Q&A, but multi-turn video editing with natural language while referencing image, text, video, and audio inputs.

The Google DeepMind page lists these entry points:

• Gemini app.
• Google Flow.
• YouTube Shorts.

The page also notes that a Google AI subscription is required, and that features vary by subscription tier and region. So “free access” to Gemini Omni should be understood more carefully: some entry points may let free users see or try part of the experience, but full video editing may require a subscription, regional availability, or product rollout access.

If you only want to try it, use this order:

1. Open Gemini app first and check whether Gemini Omni or a related video editing entry is available.
2. Then open Google Flow: https://flow.google/
3. If you make short-form content, watch for Omni-related editing features in YouTube Shorts.

If the entry point is not visible, it usually does not mean you did something wrong. Your account, region, subscription tier, or rollout group may simply not qualify yet.

How Gemini Omni Is Best Used

Gemini Omni is more suitable for creators than for ordinary chat.

You can try these directions:

• Upload or select a video and ask it to change the style.
• Make a specific action in the video more exaggerated.
• Use a reference image to replace an object or character in the scene.
• Modify camera, action, environment, and style over multiple turns.
• Combine sketches, reference images, audio, or video into a new output.

You can write prompts as if giving instructions to an editor:

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Keep the original person and room structure unchanged. Change the effect after touching the mirror into liquid ripples. The motion should feel natural, and the lighting should not change abruptly.

For multi-turn editing, do not pack too many requirements into one request. A safer approach is:

1. Change the main action first.
2. Then change the style.
3. Then adjust the camera angle.
4. Finally tune sound, text, and rhythm.

This makes consistency easier to maintain and helps you identify which step caused a problem.

Gemini Omni Flash developer integration

What Gemini Omni Flash can do

Google’s documentation positions Gemini Omni Flash as a high-performance multimodal model. Its core capabilities fall into three areas:

• Text-to-video: generate a video with audio from a text prompt.
• Image-to-video: upload a reference image, then describe motion, camera movement, and atmosphere in the prompt.
• Stateful video editing: continue editing a previously generated video without restating every visual detail.

The main difference from traditional video models is interaction. Through the Gemini API Interactions API, each generation or edit becomes an interaction. Later requests can reference a previous interaction, giving the model context and helping it preserve content that was not explicitly changed.

Minimal call pattern

Gemini Omni Flash is called through interactions.create. The simplest text-to-video request only needs the model name and a prompt:

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import base64
from google import genai

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-omni-flash-preview",
    input="A marble rolling fast on a chain reaction style track, continuous smooth shot."
)

with open("marble.mp4", "wb") as f:
    f.write(base64.b64decode(interaction.output_video.data))

If you call the REST API directly, note that the response structure differs from the SDK. The SDK provides convenient fields such as interaction.output_video; in REST responses, you usually need to find the video content inside the model_output entry in the steps array.

Controlling aspect ratio and output

The default output is landscape 16:9. To generate vertical video, set aspect_ratio in response_format:

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interaction = client.interactions.create(
    model="gemini-omni-flash-preview",
    input="A futuristic city with neon lights and flying cars, cyberpunk style",
    response_format={
        "type": "video",
        "aspect_ratio": "9:16"
    }
)

This matters for short videos, ad creative, and mobile content. Product interfaces should expose landscape and vertical options explicitly instead of relying only on natural-language prompts, because parameters are more stable than wording.

Image-to-video integration

Image-to-video input combines an image and text. The image can serve as motion reference, subject reference, style reference, or starting frame. The basic input structure looks like this:

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interaction = client.interactions.create(
    model="gemini-omni-flash-preview",
    input=[
        {"type": "image", "data": base64_image, "mime_type": "image/jpeg"},
        {"type": "text", "text": "Turn this into realistic footage, using the drawing only as a guide for movement."}
    ],
)

In real use, do not stop at “make it move.” A better prompt should describe the action, shot, scene, lighting, and elements that must remain unchanged. For example, when turning a product image into a short video, specify that the product appearance and brand mark must stay consistent, how the camera should move, and how the background may change.

If the input contains multiple images, use video_config.task to clarify the task type and reduce model guesswork:

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interaction = client.interactions.create(
    model="gemini-omni-flash-preview",
    input=[
        {"type": "image", "data": base64_image, "mime_type": "image/jpeg"},
        {"type": "text", "text": "Use this image as a reference and generate a cinematic product shot."}
    ],
    generation_config={
        "video_config": {
            "task": "image_to_video"
        }
    },
)

task can distinguish text_to_video, image_to_video, reference_to_video, and edit. For application development, this is a useful parameter to expose to advanced users or internal template systems.

Stateful video editing

Stateful editing is what makes Gemini Omni Flash feel more like a creative tool than a single generation endpoint. After the first video is generated, the second request can reference the previous result with previous_interaction_id:

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first = client.interactions.create(
    model="gemini-omni-flash-preview",
    input="A woman playing violin outdoors."
)

second = client.interactions.create(
    model="gemini-omni-flash-preview",
    previous_interaction_id=first.id,
    input="Make the violin invisible. Keep everything else the same."
)

The key is not writing a long prompt; it is specifying the exact change. Google’s prompting guidance also emphasizes that video editing often works better with short instructions. When changing a local element, adding Keep everything else the same. can reduce the chance that the model changes unrelated parts of the scene.

Editing your own video

To edit user-uploaded videos, Google recommends uploading the video through the Files API first, then passing the file URI to Gemini Omni Flash. The reason is practical: video files can be large, and sending them directly as base64 makes request size and reliability harder to manage.

When integrating this capability, the product layer should handle at least four things:

• Poll file state after upload and confirm it is no longer PROCESSING.
• Handle the FAILED state and show a failure reason users can understand.
• Set clear limits for large files, long videos, and high-resolution material.
• Hide or degrade the feature in regions where uploaded-video editing is not supported.

The official documentation also notes that uploaded-video editing is not supported in every region. For global products, do not treat “editing model-generated video” and “editing user-uploaded video” as the same capability.

Use URI delivery for large videos

If the generated video is larger than 4 MB, use delivery: "uri" in response_format. The response then returns a Google-hosted URI that the client can poll and download, instead of embedding a large base64 payload in JSON.

This is useful for web applications: the frontend can show task progress while the backend handles polling and download, avoiding huge inline responses in the browser. One detail matters: Google’s docs say GET /v1beta/interactions/{id} may still return embedded base64 in the data field, while the uri field is only guaranteed in the initial creation response or SSE stream. If your workflow depends on the URI, save it from the creation response.

Prompting: make the shot explicit

Gemini Omni Flash may generate multiple shots by default. If you need a single shot, say so clearly:

• A single uninterrupted scene.
• A continuous shot.
• No scene cuts.

If you need timing control, describe timing in natural language, such as “after 3 seconds, the character enters the frame,” or write time ranges like [0-3s] and [3-6s]. For ads, tutorials, and product demos, this is more controllable than only describing the visual.

When text needs to appear in the video, write the exact text in the prompt. The model can try to render readable text, but if the prompt does not define signage, subtitles, or screen text, it may generate unstable or meaningless text.

Current limits

Gemini Omni Flash preview has several boundaries to consider before integration:

• Users in the European Economic Area, Switzerland, and the United Kingdom cannot upload and edit images containing minors.
• Some images of identifiable people are not supported for upload and editing.
• Users in the European Economic Area, Switzerland, and the United Kingdom currently cannot edit uploaded videos, but can edit model-generated videos.
• The current API does not support uploaded audio references.
• It does not support referencing or reasoning across multiple videos.
• It does not support video extension, video interpolation, or speech editing.
• It does not support provisioned throughput.
• It does not support system instructions, temperature, top_p, stop sequences, negative prompts, and other common generation parameters.
• It does not support YouTube videos as media sources.

These limits directly affect product design. For enterprise-facing products, region, people-related media, uploaded videos, review policy, and failure messages should all be part of the workflow rather than hidden behind a single prompt box and generate button.

Developer integration advice

If you plan to integrate Gemini Omni Flash into your application, a practical sequence is:

1. Start with a text-to-video MVP that only supports 16:9 and 9:16.
2. Add image-to-video, with upload format and size limits plus prompt templates.
3. Add stateful editing and store each interaction.id in the task record.
4. Enable URI delivery for large videos to avoid base64 responses overwhelming the frontend and backend.
5. Represent region limits, content-safety failures, file-processing failures, and timeouts as explicit states.
6. Add multi-image references, time codes, text rendering, and more complex shot templates later.

It is better suited to a creative workflow tool than a plain chat interface. A good product experience should be organized around tasks: generating a short ad, animating a product image, replacing a background, changing lighting, adding subtitles, or making a vertical version. Avoid putting every capability into one free-form text box.

Gemini Omni positioning and capabilities

The Core Problem It Tries to Solve

Traditional video editing often requires timelines, layers, masks, keyframes, color grading, audio tracks, and a lot of manual work. AI video generation tools can already create clips from prompts, but they often run into two problems:

• A generated result is hard to refine precisely.
• During multi-turn edits, characters, scenes, styles, and actions can drift.

Gemini Omni is aimed at the second step: not just generating a video, but letting users keep asking for changes as if they were talking to an editor.

The project page describes it as a way to edit any video through natural, step-by-step conversation. Each edit builds on the prior result, with the goal of maintaining a coherent and unified scene.

Main Capabilities

Gemini Omni’s capabilities can be grouped into several areas.

The first is natural-language video editing. Users can directly ask the model to change a video’s aesthetic style, motion, or effects. For example, it can make a mirror ripple like liquid, turn a person into line art, a felt toy, or a transparent holographic wireframe, or transform an entire environment into 3D voxel art.

The second is action reconstruction. It can change what happens in a video, such as enlarging a hand-formed hole, making a toy produce the corresponding animal sound, or making building lights react to music.

The third is editing real video based on reference images. Users can provide an image reference and ask the model to place a building, sun, aircraft, or other object into a real video scene.

The fourth is maintaining consistency across multi-turn edits. The page shows a continuous editing flow: moving a violinist into a reference-image environment, removing the violin, and then changing the shot to an over-the-shoulder angle. This is closer to an actual creative process than a one-shot prompt.

The fifth is multi-input reference. Gemini Omni can combine image, text, video, and audio inputs into one output, supporting tasks such as style transfer, motion transfer, character replacement, and sketch-to-video generation.

Why It Emphasizes World Knowledge

Google repeatedly emphasizes that Gemini Omni is not only about making visuals look realistic. It also uses Gemini’s world knowledge, physical intuition, history, science, and narrative logic.

That matters. If a video model only optimizes for visual quality, it can easily produce illogical motion, confused object relationships, or mismatches between text and image. Gemini Omni’s goal is for video to look right while also being more coherent in story, physics, and meaning.

Examples on the page include:

• A marble rolling through a chain-reaction track.
• A claymation explanation of protein folding.
• A stop-motion style explanation of how the hippocampus works.
• Letters appearing in sync with objects in the scene.
• On-screen words appearing one by one to the rhythm.

These examples suggest that Gemini Omni is not just a short-video effects tool. It tries to combine knowledge expression, storytelling, and audiovisual generation.

How It Relates to Veo, Flow, and Nano Banana

In Google’s current product lineup, Gemini Omni looks like a layer for multimodal creation and editing.

Veo is more focused on the video generation model itself, emphasizing cinematic video and audio generation. Google Flow is an AI creative studio for creators, suitable for organizing shots, assets, and video projects. Nano Banana is more focused on image creation and detailed editing. Gemini Omni emphasizes multimodal editing from any input to a consistent output, especially multi-turn natural-language control for video.

A simple way to understand it:

• To generate high-quality video, watch Veo.
• To organize video projects in a creative workflow, watch Google Flow.
• To edit images, watch Nano Banana.
• To modify video conversationally while referencing images, text, video, and audio, watch Gemini Omni.

Access Points

The page lists these access points:

• Gemini app.
• Google Flow.
• YouTube Shorts.

However, it also notes that a Google AI subscription is required, and availability depends on subscription tier and region. In other words, not every user in every region can immediately access the full feature set.

For creators, Google Flow may be the most important entry point because it is closer to a complete creative workspace. For general users, Gemini app and YouTube Shorts may be lower-friction ways to try it.

Safety and Content Labels

The Gemini Omni page specifically mentions safety work. Gemini Omni Flash was developed in collaboration with internal safety and responsibility teams, with automated evaluations, human evaluations, human red teaming, automated red teaming, and pre-launch ethics and safety reviews.

For content transparency, the page says content created or edited with Omni in Gemini app, Google Flow, or YouTube will include imperceptible SynthID digital watermarks and C2PA Content Credentials. Users can verify content in Gemini app, with expansion to Chrome and Search planned later.

This is especially important for video models. The more realistic video generation and editing becomes, the more important source labeling, abuse prevention, and verification tools become.

Who It Is For

Gemini Omni is suitable for several types of users:

• Content creators who want to modify video quickly with natural language.
• Design teams that need to combine sketches, reference images, audio, and video assets into a finished clip.
• People making short videos, ad concepts, educational explainers, and product visual drafts.
• Creators building AI video workflows in Google Flow.
• Developers and researchers watching the boundaries of multimodal video editing.

But it is not ideal for every scenario. Serious commercial films, brand key visuals, film production, and product launch videos still require human review, copyright checks, fact-checking, and asset management. AI can clearly speed up concept generation and first-draft iteration, but it should not replace final review.

Common Pitfalls When Using Free Access

First, free quota is not production quota. A free API key is suitable for testing, personal tools, and prototypes, not for promising a stable service.

Second, do not send sensitive data to free or third-party entry points. This includes private code, customer data, contracts, keys, financial spreadsheets, and internal documents.

Third, check data-use settings. Free tiers may have different data-use policies, so review the settings in AI Studio or your Google account before use.

Fourth, video capabilities are usually more restricted than text capabilities. Gemini Omni-style video editing may be limited by subscription, region, queueing, duration, resolution, and content safety policies.

Fifth, be careful with third-party “unlimited free API” services. Many gateways rate-limit, forward requests, keep logs, or use opaque payment methods. Sensitive work should not go through these entry points.

Which Entry Point Should You Choose?

If you are an ordinary user:

• Gemini 3.5 Flash: use Gemini app.
• Gemini Omni: check Gemini app first, then Google Flow.

If you are a creator:

• Use Google Flow to try Omni video workflows.
• Use Gemini app for scripts, storyboards, prompts, and material descriptions.

If you are a developer:

• Use AI Studio to debug prompts.
• Use an API key to integrate gemini-3.5-flash.
• Use Gemini CLI for personal terminal workflows.
• For production, consider Vertex AI or the paid API.

If you are an enterprise:

• Do not rely on free quotas.
• Focus on permissions, logs, audits, data residency, compliance, and key management.
• For video generation and editing, add watermarking, content review, and copyright processes.

Summary

Gemini 3.5 Flash has relatively clear free access paths: Gemini app, Google AI Studio, AI Studio API key, and Gemini CLI can all serve as low-friction entry points. It is suitable for chat, writing, coding, agent prototypes, and multimodal testing.

Gemini Omni focuses on video editing and multimodal creation. Its main entry points are Gemini app, Google Flow, and YouTube Shorts, but full capabilities are more likely to depend on subscription and region. It is best for creators to start with trials and concept validation, not to plan around it as a stable production service from day one.

The safest strategy is: test text and code tasks first with the Gemini 3.5 Flash free tier; validate video creation effects with Gemini Omni in Gemini app or Flow; when you need to launch something real, move to a formal setup with auditability, billing, and controlled permissions.

References:

• Google DeepMind: Gemini 3.5 Flash
• Google DeepMind: Gemini Omni
• Apidog: How to Use Gemini 3.5 Flash for Free
• freedidi original link