AI provider
Google Provider
Google develops the Gemini family of AI models through Google DeepMind, with models designed for multimodal reasoning, coding, long-context analysis, agentic workflows, and high-volume applications.
- Models
- 11
- active Google entries
- Largest context
- 1.05M
- tokens in current catalog
- Current family
- Gemini 3.8
- latest family in EidoStack
- Access
- Your key
- connect your Google API key
01 / Overview
About Google AI and Gemini
Google develops Gemini through Google DeepMind as its flagship family of multimodal AI models for reasoning, coding, long-context analysis, agents, and high-volume applications.
Gemini is used across Google's developer and product ecosystem. Developers can access Gemini models through the Gemini API and Google AI Studio, while enterprise workloads can also use Gemini through Vertex AI and other Google Cloud services.
The Gemini family is designed around multimodality. Depending on the model and API configuration, Gemini can work with combinations of text, images, audio, video, code, documents, and tool results. This makes the family relevant for applications that need more than traditional text generation.
Google also uses different model tiers to cover different production requirements. Pro-class models target more difficult reasoning and analysis. Flash models emphasize a balance of intelligence, speed, and cost. Flash-Lite models are designed for high-throughput workloads where latency and price are especially important.
For developers, this means that choosing a Gemini model is not simply a matter of selecting the newest release. The right model depends on the difficulty of the task, the amount and type of context, the expected response length, latency requirements, tool use, and total production cost.
Google DeepMind and the Gemini platform
Google DeepMind is Google's AI research organization and the team behind the Gemini model family. It was formed by bringing together Google Brain and DeepMind, combining research in large-scale machine learning, reinforcement learning, multimodal systems, scientific AI, robotics, and advanced model development.
Gemini has become the central general-purpose model family across Google's AI stack. It appears in developer APIs, Google Cloud, the Gemini app, Search experiences, coding tools, and agent-oriented products.
For application developers, the most important part of this ecosystem is the Gemini API. It provides access to Google's current generative models and capabilities such as structured output, function calling, multimodal input, grounding, context caching, and model-specific reasoning features.
The practical advantage of a broad model family is choice. A developer can evaluate a high-capability model for difficult reasoning, a Flash model for interactive workloads, or a Flash-Lite model for large-scale processing. The correct production choice should be based on representative tests rather than model naming alone.
- Provider
- AI organization
- Google DeepMind
- Primary family
- Gemini
- Current EidoStack family
- Gemini 3.8
- Developer platform
- Gemini API
- Focus
- Multimodal, reasoning, coding, agents
02 / Evolution
How Google Gemini Models Evolved
Gemini has evolved from Google's first native multimodal model family into a broad platform for long-context reasoning, coding, multimodal understanding, tool use, and agentic workflows.
Gemini 1.0: a native multimodal foundation
Google introduced Gemini 1.0 in late 2023 as a model family designed from the beginning to work across multiple types of information rather than treating multimodality as an add-on.
The first generation established the core Gemini approach: a family of models at different capability and efficiency levels, with multimodal understanding as a central design goal.
For developers, Gemini 1.0 marked the beginning of Google's transition from earlier generative model families toward Gemini as the primary platform for new AI applications.
Gemini 1.5: long context becomes a core capability
Gemini 1.5 significantly expanded context capacity. Long-context processing became one of the most recognizable strengths of the family, making it practical to work with large documents, long videos, extensive code, and other inputs that would otherwise need to be split into many smaller requests.
This changed how developers could design applications. Instead of relying only on aggressive chunking, a model could work with much larger working sets in a single request.
Long context does not remove the need for good retrieval or context management, but it gives applications more flexibility when the task genuinely requires a large amount of source material.
Gemini 2.0: multimodal models become more agentic
Gemini 2.0 expanded the family toward tool use and agentic behavior. Google positioned the generation around models that could understand information, reason about it, and interact with external systems more effectively.
For developers, this made Gemini more relevant for workflows that combine generation with search, function calling, tools, structured actions, and multi-step execution.
The model was no longer only a component that returned an answer. It could increasingly act as the reasoning layer inside a larger application workflow.
Gemini 2.5: reasoning and price-performance tiers
Gemini 2.5 introduced stronger reasoning and clearer differentiation between model tiers.
Gemini 2.5 Pro targeted complex reasoning, coding, and long-context analysis. Gemini 2.5 Flash focused on strong price-performance for lower-latency, higher-volume tasks that still required reasoning. Gemini 2.5 Flash-Lite pushed further toward low-cost, high-throughput execution.
This Pro / Flash / Flash-Lite structure gives developers a practical way to think about the family:
- Pro for difficult tasks where capability matters most.
- Flash for a balance of intelligence, latency, and cost.
- Flash-Lite for frequent, lightweight, cost-sensitive workloads.
Gemini 3: reasoning, multimodality, and agents
Google introduced Gemini 3 in November 2025 with stronger reasoning, multimodal understanding, coding, and agentic capabilities.
Gemini 3 Flash followed as a model designed to combine the intelligence of the Gemini 3 generation with lower latency and cost. This made Flash relevant not only for ordinary high-volume tasks but also for increasingly sophisticated reasoning and agentic workflows.
The Gemini 3 generation continued Google's broader direction toward models that can work across multiple modalities, use tools, understand complex context, and perform multi-step tasks.
Gemini 3.x: faster iteration and more specialized workloads
During 2026, Google expanded the Gemini 3 family with several newer Flash and Flash-Lite releases. The EidoStack catalog includes models from Gemini 3.1, 3.5, 3.6, 3.7, and 3.8.
These releases increasingly target real-world software engineering, browser and computer interaction, agentic automation, multimodal workflows, and high-throughput production use.
Gemini 3.5 Flash, for example, added built-in computer-use capabilities for agents that need to interact with browser, mobile, and desktop interfaces. Later Flash releases continued improving reasoning, coding, and long-horizon workflows.
Gemini 3.8 Flash: current EidoStack generation
Gemini 3.8 Flash is the newest Google model currently listed in EidoStack. Google introduced it in September 2026 as a high-intelligence Flash model with improvements in software engineering, agentic tasks, and multi-step reasoning.
In the EidoStack registry it sits at the top of the current Google catalog, while earlier Gemini 3.x and Gemini 2.5 models remain useful comparison points for different cost, latency, and workload requirements.
Not every model released by Google is necessarily available in EidoStack. The table on this page is generated from EidoStack's current model registry and therefore represents the Google models that EidoStack actually supports.
- Gemini 1.0
Native multimodality
A model family designed from the beginning to work across multiple types of information.
- Gemini 1.5
Long context
Much larger working contexts made long documents, video, code, and other large inputs more practical.
- Gemini 2.0
Tools and agents
The family expanded toward tool use, structured actions, and more agentic application workflows.
- Gemini 2.5
Reasoning and price-performance
Pro, Flash, and Flash-Lite created clearer choices across capability, speed, and cost.
- Gemini 3
Advanced reasoning and multimodality
Stronger reasoning, coding, multimodal understanding, and agentic capabilities.
- Gemini 3.x
Rapid Flash evolution
Newer Flash and Flash-Lite releases focus on software engineering, agents, computer use, and production efficiency.
03 / Models
Google Models Available in EidoStack
EidoStack supports Google models across Gemini Pro, Flash, and Flash-Lite families. Use the table below to compare the Gemini models currently available in EidoStack by pricing, context window, and output limits.
11 Google models
| Model | Provider | Input price / 1M | Output price / 1M | Context window | Max output |
|---|---|---|---|---|---|
| Gemini 3.8 Flash | $0.75 | $3.75 | 1,048,576 tokens | 65,536 | |
| Gemini 3.7 Flash | $0.75 | $3.75 | 1,048,576 tokens | 65,536 | |
| Gemini 3.6 Flash | $1.50 | $7.50 | 1,048,576 tokens | 65,536 | |
| Gemini 3.1 Pro Preview | $2.00 | $12.00 | 1,048,576 tokens | — | |
| Gemini 3.5 Flash | $1.50 | $9.00 | 1,048,576 tokens | 65,536 | |
| Gemini 3.5 Flash-Lite | $0.30 | $2.50 | 1,048,576 tokens | 65,536 | |
| Gemini 3 Flash Preview | $0.50 | $3.00 | 1,048,576 tokens | — | |
| Gemini 3.1 Flash-Lite | $0.25 | $1.50 | 1,048,576 tokens | — | |
| Gemini 2.5 Flash | $0.30 | $2.50 | 1,048,576 tokens | — | |
| Gemini 2.5 Flash-Lite | $0.10 | $0.40 | 1,048,576 tokens | — | |
| Gemini 2.5 Pro | $1.25 | $10.00 | 1,048,576 tokens | — |
The models table must be generated from
AI_MODELSby filtering for the provider identified byproviderId. Model names, prices, context windows, max output values, and links must not be duplicated in this Markdown file.
04 / Choosing
How to Choose a Google Gemini Model
The right Gemini model depends on the difficulty of your task, how much multimodal or long-context information it needs, how quickly it must respond, and how much each successful request can cost.
A practical way to choose is to start with the workload rather than the model name. Identify what the application actually needs, shortlist a few candidates, and run the same representative requests through each one.
Start with the task difficulty
For difficult reasoning, coding, large-document analysis, and complex technical workflows, begin with the more capable Gemini models available in the current catalog.
Pro models are natural candidates when maximizing reasoning quality is more important than minimizing latency or price. Newer high-capability Flash models can also perform sophisticated work while targeting lower cost and faster responses.
The important question is whether a more expensive model produces enough improvement on your actual task to justify the difference.
Use Flash for balanced production workloads
Flash is one of the most important Gemini tiers for application developers.
It is designed to combine strong model capability with lower latency and lower cost than the highest-end models. That makes Flash a natural candidate for interactive applications, coding assistants, agents, multimodal workflows, document processing, and other production systems that need both intelligence and throughput.
Recent Gemini 3.x Flash releases also blur the old distinction between "fast model" and "smart model." They are increasingly capable of reasoning, coding, tool use, and long-running tasks that previously required a larger model.
That makes direct testing especially important. A Flash model may be sufficient for workloads that would otherwise default to a more expensive tier.
Use Flash-Lite for high-volume, cost-sensitive tasks
Flash-Lite models are designed for workloads where low cost, low latency, and high request volume are major constraints.
Typical examples include:
- classification;
- extraction;
- routing;
- short summarization;
- structured transformations;
- simple customer-support tasks;
- lightweight multimodal processing;
- high-frequency background jobs.
A Flash-Lite model should still be evaluated against realistic examples. Low token price is useful only if the model reliably reaches the quality threshold your application needs.
Consider multimodal input requirements
Gemini is built around multimodality, so the input type can be an important part of model selection.
If the application needs to understand images, video, audio, documents, code, or combinations of these inputs, confirm that the exact Gemini model and API configuration support the required modalities and features.
A model that is excellent for text-only coding may not be the best choice for a workflow that also needs image or video understanding.
Match context size to the workload
Many current Gemini models are designed for very large contexts. In EidoStack, the active Google model entries currently expose context windows around one million tokens.
That capacity is useful for large codebases, long documents, extensive research material, long conversations, and retrieval-heavy workflows.
But a large context window is a maximum capacity, not a recommendation to fill every request. Sending unnecessary context can increase cost, latency, and noise.
Measure how much context your real application sends and test whether adding more information actually improves results.
Evaluate agentic behavior end to end
Recent Gemini releases place increasing emphasis on tool use and agentic workflows.
For an agent, do not evaluate only the first response. Test whether the model can:
- understand the objective;
- choose appropriate tools;
- use tool results correctly;
- maintain relevant state;
- recover from failed actions;
- continue through multiple steps;
- finish the complete task.
A model with a higher per-token price may still be cheaper per completed workflow if it avoids retries or unnecessary tool calls.
Plan for model lifecycle changes
Google releases Gemini models frequently, including preview versions and newer Flash generations.
For production systems, avoid treating a model ID as permanent. Keep a representative evaluation set and use it when considering a newer model.
Before migrating, compare quality, latency, token usage, cost, supported capabilities, and any API behavior your application depends on.
- 01
Task difficulty
Start with the level of reasoning, coding, or analysis your workload actually requires.
- 02
Model tier
Compare Pro, Flash, and Flash-Lite based on capability, latency, throughput, and cost.
- 03
Modalities
Confirm that the candidate model supports the text, image, audio, video, or document inputs your application needs.
- 04
Context
Use the amount of context your real workload needs instead of selecting only by the largest advertised window.
- 05
Agent behavior
For agents, evaluate complete multi-step workflows rather than only individual responses.
- 06
Production cost
Compare the cost of successful tasks, including output length, retries, context, and tool use.
05 / Pricing
Google Gemini Pricing and Context Windows
When comparing Gemini models, focus first on how much your requests cost and how much information each model can process in one request.
How Gemini API pricing works
Gemini models are generally priced according to token usage, with separate rates for input and output.
Input tokens are the information you send to the model. This can include instructions, conversation history, documents, code, images represented in the model request, retrieved knowledge, and other context.
Output tokens are the content generated by Gemini. For models that use internal thinking or reasoning tokens, Google may include those tokens in output pricing depending on the model and API pricing rules.
For a long-document or codebase application, input cost can become important because every request contains a large amount of context. For long generated reports, code, or reasoning-heavy responses, output cost can become a larger part of the total.
The cheapest token rate is not automatically the cheapest production option. If a low-cost model requires retries or fails difficult tasks more often, a stronger model can have a lower cost per successful result.
Context size can affect pricing
Some Gemini pricing depends on the amount of context sent in the request. Google may use different rates for requests above certain context thresholds on specific models.
That matters for applications that routinely send very large documents, codebases, or accumulated conversation history.
Do not estimate production cost only from the headline token rate. Measure representative request sizes and check the pricing rules for the exact Gemini model you plan to use.
Context caching can reduce repeated input cost
Google supports context caching for compatible Gemini models.
Caching is useful when an application repeatedly sends the same large body of information, such as:
- a long system prompt;
- a large reference document;
- a codebase snapshot;
- reusable product documentation;
- persistent agent instructions.
Instead of processing all of that repeated context at the full standard input price for every request, caching can reduce repeated input cost.
Caching itself can have separate storage or token-related charges, so it should be evaluated using the actual request pattern of the application.
Grounding and tools can add separate costs
Gemini applications can use features such as grounding with Google Search, Maps, or other tools.
Those features can have pricing rules that are separate from ordinary input and output token charges.
This means the cost of an agent or grounded application may include more than model tokens. If your production workflow uses search, grounding, caching, or other paid capabilities, include those costs in the final estimate.
What a context window means
The context window is the maximum amount of information the model can work with in one request.
That context can include prompts, previous messages, documents, code, multimodal inputs, tool results, and retrieved information.
A large context window is useful when the task genuinely requires a large working set. It does not mean that larger prompts automatically produce better answers.
Unnecessary context can increase cost and make important information harder for the model to identify.
What max output means
Max output is the maximum amount of content a model can generate in one response.
This matters for tasks such as long reports, large code generation, detailed analysis, or large structured outputs.
Context window and max output describe different limits:
- Context window controls how much information the model can consider.
- Max output controls how much the model can return.
What to compare between Gemini models
When choosing between Google models, look at these values together:
- Input price — the cost of prompts, context, code, documents, and other inputs.
- Output price — the cost of generated responses and, where applicable, thinking tokens.
- Context window — how much information the model can process in one request.
- Max output — how much content it can generate in one response.
- Latency — whether the model responds quickly enough for your application.
- Capabilities — whether it supports the modalities, tools, and features your workflow needs.
- Quality — whether it reliably completes your actual task.
Use the model table above to create a shortlist, then test those candidates with representative examples before choosing one for production.
- 01
Input price
What you pay for prompts, context, documents, code, and other information sent to Gemini.
- 02
Output price
What you pay for the tokens Gemini generates, including thinking tokens where the provider's pricing rules apply.
- 03
Context window
The maximum amount of information Gemini can work with in a single request.
- 04
Max output
The maximum response length the model can generate for one request.
Evaluate before production
Test Google models on your real prompts
Run the same prompt and context across Gemini models in EidoStack, then compare response quality, token usage, context consumption, and estimated request cost.
Start FreeConnect your own Google API key and evaluate Gemini models under conditions that resemble your production workload.
Common Questions
What Google models does EidoStack support?
EidoStack supports the Google models listed in its shared model registry. The current catalog includes Gemini 3.x and Gemini 2.5 models across Pro, Flash, and Flash-Lite variants. The exact list changes as EidoStack adds, updates, or retires provider integrations.
What is the best Google Gemini model?
There is no single best Gemini model for every workload. Higher-capability models are better candidates for difficult reasoning and analysis, Flash models target a balance of intelligence, latency, and cost, and Flash-Lite models focus on efficient high-volume execution. The right choice depends on your task and quality threshold.
What is the difference between Gemini Pro, Flash, and Flash-Lite?
They target different production requirements. Pro models prioritize capability for difficult reasoning and complex work. Flash models balance strong intelligence with lower latency and cost. Flash-Lite models prioritize efficiency, throughput, and low-cost execution for well-scoped workloads.
Which Gemini model is best for coding?
For difficult software-engineering and agentic coding tasks, start by evaluating the newest high-capability Gemini models available in EidoStack, including recent Gemini 3.x Flash models and Pro models where available. Test repository-level workflows rather than only short code-generation prompts.
Which Gemini model is best for high-volume workloads?
Flash-Lite is Google's efficiency-focused Gemini tier and is a strong starting point for high-volume, cost-sensitive work. Flash can be a better choice when the workload needs stronger reasoning or agentic behavior while still requiring good latency and throughput.
Which Google models have the largest context windows?
The active Google models in the current EidoStack registry are configured with context windows of approximately one million tokens. Exact values on this page are generated dynamically from EidoStack's AI_MODELS registry.
Does a one-million-token context window mean I should send one million tokens?
No. The context window is a capacity limit. Most applications should send only the information needed for the task. Unnecessary context increases cost, can increase latency, and may make relevant information harder to identify.
How much do Gemini models cost?
Gemini models have different input and output token rates. Effective cost can also depend on context length, caching, batch processing, grounding, tools, and other API features. The model table on this page shows the base values configured in EidoStack; verify Google's current Gemini API pricing before final production estimates.
What is context caching in Gemini?
Context caching lets compatible Gemini applications reuse large repeated context instead of processing it from scratch at the standard input rate for every request. It can be useful for long documents, codebases, large system prompts, and persistent agent context.
Can Gemini use Google Search or other grounding tools?
Supported Gemini models and API configurations can use grounding and tool capabilities such as Google Search. Availability and pricing depend on the specific model and API feature, so confirm the current Google documentation for the configuration your application needs.
Can I compare Google models side by side?
Yes. EidoStack is designed for model evaluation and comparison. You can run the same prompt against supported Gemini models and compare their responses together with token usage, context consumption, and estimated cost.
Do I need my own Google API key?
Yes. When using Google models through EidoStack, you connect your own Google provider API key. Provider-side billing, quotas, rate limits, and model availability are determined by your Google account and API project.
Why test several Gemini models instead of using the newest one?
The newest model may be unnecessary for a simple or high-volume task. A Flash or Flash-Lite model may meet the same production quality threshold with lower latency and cost. Testing several candidates helps identify the least expensive model that reliably completes your workload.
How should I migrate to a newer Gemini model?
Maintain a representative evaluation set from real application prompts. Run the current and replacement models under equivalent conditions, compare quality, latency, token usage, cost, and supported features, and examine difficult cases for regressions before changing the production model ID.
Official Google References
Provider information is based on Google AI for Developers, Google DeepMind, and official Google model documentation. Model specifications shown in the provider table must come from EidoStack's shared AI_MODELS registry rather than duplicated Markdown data.
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