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Google Gemini API Review: Models, Features, Examples, and Pricing in 2026

Information current as of September 29, 2026.

Over the past two years, the Google Gemini API has evolved from a relatively simple interface to language models into a large multimodal platform. Through one stack, developers can work with text, images, PDFs, audio, and video; connect Google Search and Google Maps; run Python code; build RAG over their own documents; generate images; recognize and synthesize speech; launch real-time voice agents; and use managed AI agents with their own Linux sandbox.

The main change in 2026 concerns more than just the models. Since June, Google has recommended the Interactions API as the primary interface for new projects. The old generateContent endpoint still works, but is now considered a legacy API. Interactions brings standard generation, multimodal requests, stateful conversations, tools, background execution, and managed agents together in one interaction model.

This article looks at the current state of the Gemini API, its main models, pricing, built-in tools, Nano Banana, Live API, File Search, Managed Agents, and how to connect to Gemini from PHP.

What is the Gemini API?

The Gemini API is Google's developer platform for programmatic access to Gemini models and related specialized models. You can get an API key, test a prompt, and view usage through Google AI Studio; requests themselves go to generativelanguage.googleapis.com.

Today, the Gemini API offers more than regular LLM requests. One ecosystem includes:

  • Gemini 3.8 Flash and other text and multimodal models;
  • Nano Banana for image generation and editing;
  • Gemini Live for real-time audio and voice agents;
  • Gemini TTS and Transcribe;
  • Gemini Omni Flash for video generation and editing;
  • Gemini Embedding for semantic search and RAG;
  • Deep Research and Antigravity Managed Agents;
  • built-in Google Search, Google Maps, File Search, URL Context, Code Execution, and Computer Use.

This is one of the main differences between Google's modern platform and the early Gemini API: developers no longer need to assemble separate services for text, search, documents, voice, and some agentic infrastructure.

Interactions API: the primary interface since 2026

Until 2026, most Gemini examples were built around generateContent. That endpoint is still supported, but since June 2026 Google has recommended the Interactions API for all new projects.

A minimal request looks like this:

curl -X POST "https://generativelanguage.googleapis.com/v1/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-3.8-flash",
    "input": "Briefly explain the difference between REST and GraphQL."
  }'

The Interactions API is designed for more than one-off generation. The same interface is used for text, multimodal input, structured outputs, function calling, built-in tools, stateful conversations, background tasks, and managed agents.

One important difference from a classic stateless API is that the server can store conversation state. The next request passes previous_interaction_id, after which Google restores the previous context automatically. You do not have to send the entire conversation history again.

You can also work fully stateless when needed by setting store: false. The application then retains control of the history, but the developer must pass the previous model steps manually, including the tool calls and thinking-related steps needed to continue the conversation.

Current main Gemini models

Google's lineup is fairly large, but three models are particularly important for a typical backend application right now.

Model Status Context Maximum output Standard input / output per 1M tokens
Gemini 3.8 Flash GA 1,048,576 65,536 $0.75 / $3.75 through Dec. 31, 2026
Gemini 3.1 Pro Preview 1M 64K $2 / $12 up to 200K input tokens
Gemini 3.5 Flash-Lite GA 1,048,576 65,536 $0.30 / $2.50

Gemini 3.8 Flash has an introductory price through the end of 2026. Starting January 1, 2027, Google lists $1.50 per million input tokens and $7.50 per million output tokens.

Gemini 3.1 Pro has different pricing: for prompts up to 200K tokens, the price is $2/$12; above that size, it is $4/$18. This matters if the 1M-token context window is used in every request rather than being needed only in theory.

Gemini 3.8 Flash

Gemini 3.8 Flash is Google's current general-purpose model and has reached GA status. Despite the Flash name, Google positions it not merely as a fast, lightweight option, but as a model for long-horizon software engineering, autonomous agents, and complex enterprise workflows.

It accepts text, images, video, audio, and PDFs, and returns text. The context window is 1,048,576 tokens, with a maximum output of 65,536 tokens. It supports function calling, structured outputs, File Search, Google Search, Google Maps, URL Context, Code Execution, Computer Use in preview, and context caching.

The model also supports adjustable thinking levels: low, medium, and high, with medium as the default. This means you do not have to pay for the same reasoning budget on every task.

Through December 31, 2026, standard pricing is $0.75 per million input tokens and $3.75 per million output tokens. For a new production integration, this is generally the first Gemini model worth testing.

Gemini 3.1 Pro Preview

Gemini 3.1 Pro remains the heaviest Pro model in the current lineup for complex multimodal reasoning, agentic tasks, and programming, but its Preview status is important. For a production system, that can mean more frequent changes and a more cautious approach to tying the architecture to a specific version.

The context window is about 1M tokens, with output up to 64K. The model accepts text, images, video, audio, and documents, and supports Gemini tools.

Pricing depends on prompt size:

  • up to 200K tokens: $2 input and $12 output per million tokens;
  • over 200K tokens: $4 input and $18 output.

So do not use Pro automatically for all traffic. It is more sensible to compare it with Gemini 3.8 Flash on your own difficult tasks and use it where the improvement in results justifies the price difference and preview status.

Gemini 3.5 Flash-Lite

Gemini 3.5 Flash-Lite is the current budget GA model for high-volume workloads. It also has a huge 1,048,576-token context window and output up to 65,536 tokens, but costs $0.30/$2.50 per million input and output tokens.

The model accepts text, images, video, audio, and PDFs. It supports thinking, function calling, structured output, File Search, Google Search, Google Maps, URL Context, and Code Execution, but not Computer Use.

Flash-Lite is especially suitable for classification, data extraction, translation, document parsing, request routing, and sub-agent tasks. At high call volumes, the difference between $0.30 and $0.75 per million input tokens is noticeable, so splitting workloads between Flash and Flash-Lite can be more useful than picking one model “for every situation.”

Older Gemini 2.5 and previous Flash models

Gemini 2.5 Pro, Flash, and Flash-Lite have not been declared deprecated yet, but Google is already limiting new access to them and recommends using the current Gemini 3.8 Flash and Gemini 3.5 Flash-Lite for new projects.

The lineup also still includes Gemini 3.5 Flash, 3.6 Flash, and 3.7 Flash. They remain supported, but belong to earlier Flash generations. Unless a new project depends on the specific behavior of an older version, there is usually little reason to start an integration with them.

This is an important general principle for the Gemini API in production: track not only the family name, but also the status of the specific model ID. Preview models can change faster, while older versions gradually receive shutdown dates and recommended replacements.

Multimodality: text, images, video, audio, and PDFs

One of Gemini's strengths is that multimodality is not split off into a separate analyzer. Gemini 3.8 Flash and Flash-Lite can receive text, images, video, audio, and documents together in a single request.

For example, an application can pass a product photo, an audio recording of a user's comment, and a text instruction, and the model will process them as shared context. For video, Gemini can analyze both the frames and the audio track; for PDFs, it can analyze text and the visual structure of the pages.

Small files can be sent directly in the request. The total inline payload is limited to about 100 MB, or 50 MB for PDFs. For larger files or repeated use, the Files API is preferable.

Files API

The Gemini Files API lets you upload an image, audio file, video, or document once and then use its URI in model requests.

A single file can be up to 2 GB, with up to 20 GB of total storage per project. Regular uploaded files are automatically deleted after 48 hours. The Files API itself is free; processing the file with a model is what incurs a charge.

There are other ways to pass data, too. You can register a GCS URI for files in Google Cloud Storage, and public files can be read directly from a URL in supported scenarios.

The Files API solves a transport problem; it is not automatically a knowledge base. Google offers a separate File Search for searching large document collections.

File Search: built-in RAG in the Gemini API

File Search is Gemini's built-in retrieval tool. You upload documents to a File Search store; Google splits them into chunks, creates embeddings, and automatically retrieves suitable passages into the model's context when asked.

This is much closer to ready-made RAG than the regular Files API. Developers do not have to deploy a separate vector database, write a chunking pipeline, and manually add retrieved passages to the prompt.

Pricing is relatively simple. File storage and embeddings at query time are free; embeddings during initial indexing cost $0.15 per million tokens. Retrieved passages are then billed as regular input for the selected Gemini model.

File Search also supports multimodal retrieval: Gemini Embedding is used for text, while Gemini Embedding 2 can build search over images and other data types. Audio and video are not yet supported directly in File Search.

Structured Outputs

Free-form model text is inconvenient when a backend expects a specific structure. Gemini supports Structured Outputs with JSON Schema: the developer describes the required structure, and the model returns JSON that conforms to the schema.

In the Interactions API, the format is set through response_format. For example, you can require an object with a product name, category, price, and an array of features.

This is useful for:

  • extracting data from documents;
  • classifying support requests;
  • filling CRM objects;
  • parsing incoming emails;
  • building API responses;
  • agentic workflows where the next step expects specific fields.

Gemini supports a subset of JSON Schema, so a complex schema should be checked for compatibility with the specific endpoint before a production launch.

Function calling

Function calling lets a model access application functions. You describe a function's name, purpose, and parameters; Gemini decides when to call it and returns structured arguments.

For example, you can give the model findCustomer, getOrder, and createSupportTicket functions. A user writes: “Check order 54821 and create a support ticket if it is delayed.” Gemini determines the sequence of calls, but your application makes the actual database requests and changes the data.

Gemini supports more than a single call. It can make parallel function calls for independent actions and compositional function calls when the result of one function is needed for the next.

As with other LLM APIs, function access is not a substitute for backend authorization. The model can choose an action, but the application must separately check the user's permissions, the arguments, and whether the operation is allowed.

Gemini's built-in tools

In addition to user-defined functions, Gemini has a fairly large set of server-side tools. Gemini 3.8 Flash supports Google Search, Google Maps, URL Context, File Search, and Code Execution; Computer Use is a separate client-side tool in preview.

These tools can be combined. For example, the model can first find current information through Google Search, open a specific page through URL Context, run calculations in Code Execution, and then call one of your application's functions.

For complex AI products, this matters more than a small benchmark difference: some of the infrastructure that previously had to be built by hand is now part of the API itself.

Grounding with Google Search

Grounding with Google Search lets the model decide for itself when it needs fresh online data. The application receives not only the final text, but also grounding metadata that can be used to show sources.

For Gemini 3.x on the paid tier, the first 5,000 search requests per month are free within the shared family limit; after that, Google charges $14 per 1,000 requests. One important detail: the charge is for each search request made by the model. A single user prompt can generate several search requests.

Search can be used with other built-in tools and custom function calls. This makes it possible, for example, to get current information online and then save what is needed in the application through a custom function.

Grounding with Google Maps

Gemini has a separate Google Maps integration. It is designed for questions about places, organizations, and geographic context where ordinary web search is not enough.

For Gemini 3, pricing is similar to Search: 5,000 free requests per month on the paid tier within the applicable limit, then $14 per 1,000 search queries.

For local travel, recommendation, and business apps, this is a notable advantage of Google's ecosystem: the model can work with map data directly through a built-in tool instead of a separate Places API integration for every AI workflow.

URL Context

URL Context is for cases where a specific URL is known and the model needs to read its contents. Instead of a separate parser, the application passes the link, and Gemini extracts the available content and uses it in its response.

The Interactions API response then includes annotations with url_citation, so the application can link parts of the response to the source page.

There is no separate charge for the URL Context call itself, but the extracted material counts as input tokens for the selected model. So loading several very long pages directly into the context can cost more than a retrieval approach.

Code Execution

Code Execution lets Gemini generate and run Python code in a server-side environment. The model can inspect the execution result, fix the code, and continue reasoning iteratively.

This is useful for calculations, statistics, data analysis, and tasks where actually running a program is more reliable than generating text alone.

There is no separate hourly runtime fee. Generated code and execution results are charged through the selected model's standard token pricing: when they are created, they count as output; when the model uses them in the next reasoning cycle, they count as input.

Built-in Code Execution runs Python specifically. Gemini can write PHP, JavaScript, or another language, but this server-side tool cannot run them.

Computer Use

Gemini 3.8 Flash supports Computer Use in preview. This mode is designed for agents that can see an interface and perform actions such as clicking, entering text, and navigating.

Unlike Google Search or Code Execution, Computer Use is a client-side tool: the application provides the interface state and carries out the actions requested by the model.

This approach is useful for automating services without a suitable API, but it requires especially strict restrictions. It is sensible to gate any financial, destructive, or external actions with confirmation and normal backend checks instead of giving a model an entirely uncontrolled GUI.

Thinking and reasoning

Gemini 3.8 Flash supports low, medium, and high thinking levels. The default is medium.

Output pricing includes thinking tokens. This matters when estimating cost: two requests with the same user text can use different numbers of internal reasoning tokens depending on task complexity and the thinking setting.

So in production there is no reason to use the maximum thinking level automatically. Simple classification or data formatting usually does not need the same reasoning budget as architecture analysis, complex programming, or a multi-step agent.

Stateful conversations and background execution

The Interactions API can store conversation state on Google's side. You can continue the next turn with previous_interaction_id without resending the entire conversation manually.

For long-running tasks, there is background: true. The API immediately returns an interaction ID, after which the application can poll for status or receive updates. This is useful for complex analysis and agentic workflows that go beyond a regular HTTP timeout.

Streaming is also supported. The Interactions API sends events through SSE, including text deltas, tool calls, and other execution steps, so the user interface can show results as they arrive.

Context caching

With a long context, one of the main sources of cost is resending the same data. Gemini uses implicit caching, which is enabled automatically for Gemini 2.5 and newer models.

For Gemini 3.8 Flash, a cache-eligible prompt must be at least 4,096 tokens. If a prefix matches previous requests, Google automatically counts a cache hit and applies a lower price.

Through the end of 2026, cached input for 3.8 Flash costs $0.075 per million tokens instead of the regular $0.75. With explicit caching, there is also a charge for storing the cached context—currently $0.50 per million tokens per hour.

There is an important difference between APIs. The Interactions API supports only implicit caching. To manually create a cache object and set its TTL, you must use the legacy generateContent; explicit caching there is still in beta.

Standard, Flex, Priority, and Batch

Google lets you choose an inference mode as well as a model. This gives you another way to optimize cost and latency.

Standard is the regular synchronous mode for most applications.

Flex costs about 50% less than Standard but uses best-effort capacity. The request remains synchronous, though latency can be measured in minutes, and high load can result in 429 and 503 responses. It is a convenient option for sequential background workflows where Batch is unsuitable because one step depends on another.

Priority costs about 75–100% more than Standard, but requests get higher priority and are intended for critical user-facing systems. If Priority capacity is exhausted, the API may automatically process a request as Standard and charge the standard price.

Batch API also offers a 50% discount, but works asynchronously. The target turnaround is up to 24 hours, though many jobs finish sooner. Batch is suitable for bulk classification, catalog processing, embeddings, and evaluations. At the time this article was written, Batch API was available through the legacy generateContent, not the Interactions API.

What one large request costs

Consider a hypothetical request with 100K input tokens and 10K output tokens.

Model Approximate Standard cost
Gemini 3.5 Flash-Lite $0.055
Gemini 3.8 Flash $0.1125
Gemini 3.5 Flash $0.24
Gemini 3.1 Pro Preview $0.32

For 3.1 Pro, this calculation is for a prompt up to 200K tokens. Above that threshold, the rate is higher.

Through the end of 2026, the same volume with Gemini 3.8 Flash through Batch or Flex costs about $0.05625. Priority costs more than Standard.

This is only the model cost. Google Search, Maps, and other paid tools are calculated separately, and an agentic workflow may make several model and tool calls for one user instruction.

Nano Banana: image generation and editing

Image generation in the Gemini API is developing as the Nano Banana family. In 2026, the main options are:

  • Nano Banana 2 Lite (gemini-3.1-flash-lite-image) is the least expensive and fastest option;
  • Nano Banana 2 (gemini-3.1-flash-image) is a general-purpose model for generation and editing;
  • Nano Banana Pro (gemini-3-pro-image) is a more expensive model for complex contextual image generation.

Nano Banana works natively with multimodal context. You can pass text, source images, and other supported data, then continue editing in a conversation.

Nano Banana 2 supports images up to 4K. At Standard rates, the approximate cost is $0.067 for 1K, $0.101 for 2K, and $0.151 for 4K. Nano Banana 2 Lite is less expensive at about $0.0336 for 1K. Nano Banana Pro costs about $0.134 for 1K/2K and $0.24 for 4K.

This means the Gemini API can provide both text and image features for a product without connecting a separate image provider.

Gemini Omni Flash and video

Google has also added Gemini Omni Flash, a preview model for video generation and editing with native audio.

The model accepts text, images, video, and audio. Text output costs $9 per million tokens; video costs $17.50 per million output tokens, which Google estimates at about $0.10 per second of 720p video under the standard generation setup.

This area is less mature than text-based Gemini 3.8 Flash and remains in preview. For a production system, account separately for possible API and model ID changes.

Live API and real-time voice

For voice assistants, Google offers the separate Gemini Live family. The current Gemini 3.8 Live accepts text, images, audio, and video, and can return text and audio in real time.

The model has a 131,072-token context window and output up to 65,536 tokens. It supports streaming audio, function calling, Google Search, and interleaved reasoning.

Audio input costs about $0.005 per minute and audio output about $0.018 per minute. This makes it possible to build voice assistants and operators without a separate speech-to-text → LLM → text-to-speech chain.

For scenarios that need more background reasoning, there is Gemini 3.8 Live Extended Thinking.

Text-to-Speech

For regular speech synthesis without a real-time conversation, Google offers Gemini 3.8 Flash TTS and the less expensive Flash-Lite TTS.

Gemini 3.8 Flash TTS is designed for quality, expressiveness, and long-form speech. Through the end of 2026, text input costs $0.50 per million tokens and audio output costs $9 per million audio tokens.

Gemini 3.8 Flash-Lite TTS is intended for high-volume and price-sensitive tasks. Its audio output costs $6 per million tokens, which Google estimates at about $0.0015 per 10 seconds of audio through the end of 2026.

Speech-to-Text: Gemini 3.5 Transcribe

For transcription, there is the specialized Gemini 3.5 Transcribe model. It detects language automatically, supports more than 85 languages, speaker diarization, word-level timestamps, and custom vocabulary.

The regular version accepts a file up to one hour long. With diarization or word timestamps enabled, the limit drops to 30 minutes.

The approximate combined price is about $0.005 per minute of audio. There is also a real-time model, gemini-3.5-transcribe-live, which works over WebSockets and costs about $0.009 per minute of combined input/output.

Embeddings

For semantic search, recommendations, clustering, and custom RAG, Google offers Gemini Embedding 2.

This is a multimodal embedding model: text, images, video, audio, and PDFs can all be mapped into one embedding space. Input is limited to 8,192 tokens, and the embedding size can be selected from 128 to 3,072 dimensions.

Text embeddings cost $0.20 per million tokens; image input costs $0.45 per million tokens, or approximately $0.00012 per image. Batch reduces the price by about half.

For older text-only scenarios, gemini-embedding-001 is still available.

Managed Agents

By 2026, the Gemini API contains not only models, but also ready-made agent infrastructure. Managed Agents provide an agent harness with a Linux sandbox, code execution, a file system, and web access through a single API call.

The main general-purpose agent is called Antigravity Agent. The current version uses Gemini 3.8 Flash by default. The agent can plan a task, run code, work with files, and search the web inside an isolated Google environment.

You can extend it with your own system instructions, tools, MCP servers, functions, and files such as AGENTS.md and SKILL.md. To control spending, max_total_tokens limits the combined input, output, and thinking within the agent loop.

Managed Agents is still in preview, so its actions should be controlled and checked separately for critical production processes.

Deep Research

Another managed agent is Gemini Deep Research. It is designed not for a quick chat response, but for extended research: it plans the work, runs searches, reads sources, analyzes data, and produces a report with citations.

Deep Research is available only through the Interactions API and runs in background mode. There is the regular deep-research-preview-04-2026 and the more in-depth deep-research-max-preview-04-2026.

Google estimates about $1–3 for a typical medium-complexity Deep Research task, but this is not a fixed price. The agent is billed for actual token usage and tool calls, so a complex study with many Search requests can cost noticeably more.

This agent is excessive for a regular FAQ or chatbot. It is intended for tasks that involve dozens of searches, reading many sources, and multi-step analysis.

OpenAI SDK compatibility

Google offers a compatibility layer for the OpenAI API. In a simple case, the application changes the API key, base_url, and model ID:

from openai import OpenAI

client = OpenAI(
    api_key="GEMINI_API_KEY",
    base_url="https://generativelanguage.googleapis.com/v1beta/openai/"
)

response = client.chat.completions.create(
    model="gemini-3.8-flash",
    messages=[
        {"role": "user", "content": "Explain how AI works"}
    ]
)

This is convenient for quickly testing Gemini in an existing application. However, Google explicitly recommends the native Gemini API unless the application has to remain on the OpenAI schema.

The reason is simple: the compatibility layer does not map Gemini's capabilities one-to-one. The Files API, specific built-in tools, Live API, and some agentic features require the native interface or additional custom logic.

So OpenAI compatibility is useful as a quick migration path, but for a new product it is better to build the integration directly on the Interactions API.

Using the Gemini API in PHP

Google currently has no official GenAI SDK for PHP. Google explicitly recommends using the Direct REST API for languages without an SDK, including PHP.

A simple example in plain PHP:

<?php

$apiKey = $_ENV['GEMINI_API_KEY'];

$payload = [
    'model' => 'gemini-3.8-flash',
    'input' => 'Briefly explain what dependency injection is.',
];

$ch = curl_init('https://generativelanguage.googleapis.com/v1/interactions');

curl_setopt_array($ch, [
    CURLOPT_POST => true,
    CURLOPT_RETURNTRANSFER => true,
    CURLOPT_HTTPHEADER => [
        'Content-Type: application/json',
        'x-goog-api-key: ' . $apiKey,
    ],
    CURLOPT_POSTFIELDS => json_encode($payload, JSON_UNESCAPED_UNICODE),
]);

$response = curl_exec($ch);

if ($response === false) {
    throw new RuntimeException(curl_error($ch));
}

$data = json_decode($response, true, flags: JSON_THROW_ON_ERROR);

foreach ($data['steps'] ?? [] as $step) {
    if (($step['type'] ?? null) !== 'model_output') {
        continue;
    }

    foreach ($step['content'] ?? [] as $content) {
        if (($content['type'] ?? null) === 'text') {
            echo $content['text'];
        }
    }
}

In Laravel, it is more convenient to wrap this integration in a separate service class and use the HTTP Client. The API key should be stored in .env and moved into config/services.php, rather than passed directly to the frontend.

Direct REST has another advantage for PHP: you get the full Gemini API right away, without waiting for a third-party library to support new features.

API keys and Google AI Studio

An API key is created in Google AI Studio and linked to a Google Cloud project. Since May 2026, new keys have been created as auth keys, and Google's documentation requires migration from older standard keys: from September 2026, standard keys are to be rejected by the Gemini API.

So a new project should use a current auth key rather than copying an old guide that uses an unrestricted Google Cloud API key.

Rate limits apply at the project level, not to individual keys. The main limits are RPM, input TPM, and RPD; the specific values depend on the model and usage tier.

Free Tier and Paid Tier

The Gemini API has a full Free Tier for a number of models. It is convenient for development and small experiments, but there is an important difference between free and paid modes that is easy to miss.

Google's pricing page explicitly states that Free Tier data may be used to improve Google products, while Paid Tier data is not.

Paid Tier is enabled through Cloud Billing. In AI Studio, you can connect a billing account and switch to a paid project; Google also supports project-level spend caps and overall monthly limits depending on the usage tier.

Since March 2026, the regular $300 Google Cloud Free Trial has not applied to Gemini API expenses.

Privacy and Zero Data Retention

For Paid Services, Google states that prompts, system instructions, cached content, uploaded files, and responses are not used to improve its products.

This does not mean Zero Data Retention is automatic. Some features retain data to operate the service or for abuse monitoring.

The Interactions API stores conversation state by default. To minimize state retention, set store: false. The Files API keeps uploaded files until they are deleted or expire; regular temporary uploads are retained for up to 48 hours. Explicit caches are also stored until their specified TTL.

Search and Maps deserve particular attention. With Grounding with Google Search, Google stores the prompt, context, and generated output for up to 30 days to create grounded results and for related purposes; this retention cannot be disabled when using Search. Separate retention rules also apply to Google Maps.

Google separately says that Vertex AI should be used for workloads that require guaranteed zero data retention or enterprise data processing agreements.

The Gemini Developer API and Vertex AI are not the same thing

Gemini models are available through both the Gemini Developer API and Google Cloud Vertex AI infrastructure. These are related, but not identical, products.

The Gemini Developer API through AI Studio is easier to get started with: an API key, quick access to new models, Free Tier, and relatively simple pricing. This is often enough for a small SaaS, prototype, or regular backend feature.

Vertex AI is designed for deeper Google Cloud integration: IAM, enterprise network configuration, data governance, corporate billing, and additional enterprise guarantees. If a company is already building its infrastructure in Google Cloud or has strict data residency and ZDR requirements, it should compare not only the models, but also the two platform options.

The strength of the Gemini API is the whole platform, not one model

If you look only at regular text generation, Gemini can be compared with OpenAI, Claude, Grok, or DeepSeek on the price and quality of a particular model. Google's real difference is more visible at the platform level.

One API provides a large multimodal context, Google Search and Maps, URL and file analysis, built-in RAG, code execution, image generation, real-time voice, transcription, TTS, embeddings, video, and managed agents. For an application that needs several of these features at once, this reduces the number of separate integrations.

The flip side of this breadth is that the API changes quickly. A project may use GA, Preview, and Legacy features at the same time, while different capabilities are available through Interactions, generateContent, or Live API. Model IDs and endpoint status should therefore be kept in configuration and deprecations checked regularly.

Which Gemini model should you choose?

For a new general-purpose application, Gemini 3.8 Flash is a logical starting point. It is the current GA model with a 1M-token context window, a strong set of tools, and a relatively low price through the end of 2026.

Gemini 3.5 Flash-Lite is worth testing for simple, high-volume operations where cost and latency matter more than maximum quality. It can be used for classification, extraction, routing, and some sub-agent workloads.

Gemini 3.1 Pro Preview should be used where difficult evaluation tasks show a real advantage over Flash. It does not need to handle every request, especially given the higher price for context above 200K tokens.

For image, voice, transcription, and real-time tasks, it is better to use specialized Gemini models rather than trying to solve everything through one text model endpoint.

Conclusion

In 2026, the Google Gemini API is no longer just access to a ChatGPT competitor. The Interactions API combines regular generation, stateful conversations, multimodality, tool use, and background execution, while Google has built separate models for images, speech, video, embeddings, and managed agents around it.

Three things are especially interesting for developers: a 1M-token context even in budget models, a rich set of built-in Google tools, and the ability to choose not only a model, but also an inference mode—Standard, Flex, Priority, or Batch.

If you are starting a new project now, a sensible baseline is fairly simple: Gemini 3.8 Flash through the Interactions API as the main model, Flash-Lite for inexpensive high-volume operations, your own evaluations for checking Pro, and Search, File Search, Code Execution, and specialized models only when a scenario actually needs them.

With this approach, the Gemini API can be treated not as a standalone LLM integration, but as a full platform for AI features in an application.

Official sources

Related: an xAI Grok API review with current models and tools.

Related: a DeepSeek API review with current models and pricing.

Date of publication:

Google Gemini API Review: Models, Features, Examples, and Pricing in 2026

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