What is the context window for Gemini 3.1 Pro on Google Vertex AI (gateway)?

Gemini 3.1 Pro, when accessed via Google Vertex AI (gateway) in Atlas, offers a substantial 1M token context window. This allows for extensive code analysis and complex task execution within Atlas.

How much does Google Vertex AI (gateway) cost with Atlas?

Pricing for Google Vertex AI (gateway) through Atlas is passthrough at Google Cloud rates. For example, Gemini 3.1 Pro costs $2 per million input tokens and $12 per million output tokens.

Can I use both Gemini and Claude models with Atlas via Google Vertex AI (gateway)?

Yes, Google Vertex AI (gateway) allows Atlas users to run both Gemini and Anthropic's Claude models (like claude-opus-4-1 and claude-haiku-4-5) side by side within a single Google Cloud project, with unified billing.

What are the security benefits of using Google Vertex AI (gateway) with Atlas?

A primary security benefit is service-account authentication via `GOOGLE_APPLICATION_CREDENTIALS`, which is often required by enterprise security teams, offering a more secure alternative to long-lived API keys.

Is the setup for Google Vertex AI (gateway) complex for Atlas?

Yes, setting up Google Vertex AI (gateway) requires more steps than a simple API key. It involves configuring a GCP project, selecting a region, and enabling specific APIs before any tokens can be processed by Atlas.

How do I configure Atlas to use Google Vertex AI (gateway)?

You need to set `GOOGLE_VERTEX_PROJECT` and `GOOGLE_VERTEX_LOCATION` environment variables, and authenticate your service account by pointing `GOOGLE_APPLICATION_CREDENTIALS` to your JSON key or running `gcloud auth application-default login`. Then, select a model in Atlas's `/models` interface.

Does Atlas support Claude models directly through Google Vertex AI (gateway)?

Yes, Atlas ships a dedicated `google-vertex-anthropic` route, making Claude on Vertex AI a first-class provider rather than a workaround, ensuring direct integration and model switching.

Atlas with Google Vertex AI (gateway) in 2026

Atlas with Google Vertex AI (gateway) offers a robust enterprise solution for developers in 2026, enabling secure access to both Gemini and Anthropic's Claude models within a single Google Cloud project. This integration is ideal for organizations requiring service-account authentication and unified billing. Pricing is passthrough at Google Cloud rates, with Gemini 3.1 Pro costing $2 per million input tokens and $12 per million output tokens, supporting a 1M context window.

What is Google Vertex AI (gateway) best at for Atlas users?

For Atlas users in 2026, Google Vertex AI (gateway) excels as the enterprise-grade conduit to leading AI models, offering a secure and unified platform. It allows running both Gemini and Anthropic's Claude models side by side within a single Google Cloud project, all billed to one account.

Google Vertex AI (gateway) provides Atlas developers with a critical advantage in 2026: a secure, enterprise-compliant pathway to powerful AI models. This gateway allows you to operate both Gemini and Anthropic's Claude models, including claude-opus-4-1 and claude-haiku-4-5, concurrently within a single Google Cloud project. All usage is consolidated and billed to one account, simplifying financial oversight. A key security benefit is the support for service-account authentication via `GOOGLE_APPLICATION_CREDENTIALS`, which is often a mandatory requirement for enterprise security teams, replacing less secure long-lived API keys. Furthermore, Atlas ensures that Claude models on Vertex AI are treated as first-class providers, not workarounds, by shipping a dedicated `google-vertex-anthropic` route. This means developers can direct switch between Gemini and Claude models, leveraging Atlas's ability to switch the active model and provider on the fly, all while maintaining a consistent and secure Google Cloud environment. Atlas's capabilities, such as Axis, the hybrid semantic and keyword code search, and its ability to build its code index with local Ollama embeddings, complement the robust model access provided by Vertex AI.

What are the cost and context window tradeoffs for Google Vertex AI (gateway)?

Google Vertex AI (gateway) offers passthrough pricing at Google Cloud rates, ensuring transparency for Atlas users in 2026. For instance, Gemini 3.1 Pro provides a substantial 1M context window, with input tokens priced at $2 per million and output tokens at $12 per million.

When using Atlas with Google Vertex AI (gateway), developers will find that pricing is a direct passthrough of Google Cloud rates. This means there are no additional markups from Atlas, providing clear cost visibility. For specific models like Gemini 3.1 Pro, the cost is $2 per million input tokens and $12 per million output tokens. The context window for models served via Vertex AI varies, but for Gemini 3.1 Pro, it is a significant 1M tokens. This large context window is beneficial for Atlas's operations, especially when performing complex tasks that require extensive code analysis, such as those involving Axis, the hybrid semantic and keyword code search, or when Atlas drafts a plan in a read-only plan agent. While the 1M context window on Gemini 3.1 Pro is substantial, it is important to remember that other models available through Vertex AI may offer different context window sizes, requiring developers to consider their specific task requirements and model choice. Atlas's ability to switch the active model and provider on the fly allows for flexible adaptation to these varying model capabilities and costs.

When should I consider an alternative to Google Vertex AI (gateway) for Atlas?

While powerful, Google Vertex AI (gateway) requires more initial setup than a simple API key, a factor for Atlas users in 2026. It demands a GCP project, region selection, and enabled APIs before any tokens flow, and new model availability can lag direct APIs by weeks.

Developers using Atlas should consider alternatives to Google Vertex AI (gateway) if rapid, minimal-setup access to the absolute newest models is a higher priority than enterprise security and unified billing. The primary tradeoff is the initial setup complexity: getting started with Vertex AI requires establishing a Google Cloud Platform (GCP) project, selecting a specific region, and enabling the necessary APIs. This process is significantly more involved than simply obtaining and configuring a long-lived API key for direct model access. For teams that prioritize immediate deployment without extensive cloud infrastructure configuration, this overhead might be a deterrent. Additionally, the region availability for the very newest models on Vertex AI can lag behind the direct Gemini API by several weeks. If your development workflow in 2026 absolutely depends on having access to the latest model iterations the moment they are released, a direct API integration might be more suitable. However, for most enterprise scenarios where security, centralized management, and the ability to run both Gemini and Claude models under one GCP project are paramount, the benefits of Google Vertex AI (gateway) with Atlas often outweigh these initial setup and availability considerations. Atlas's flexibility to switch models on the fly can mitigate some of these concerns by allowing users to easily pivot if a specific model is not yet available on Vertex AI.

Setup

011: Configure your Google Cloud Project: Set the environment variables `GOOGLE_VERTEX_PROJECT` to your GCP project ID (e.g., `my-project`) and `GOOGLE_VERTEX_LOCATION` to your desired region (e.g., `us-central1`). Atlas also supports `GOOGLE_CLOUD_PROJECT` and `GCP_PROJECT` as fallbacks.
022: Authenticate your service account: Point the `GOOGLE_APPLICATION_CREDENTIALS` environment variable to the path of your service account JSON key file. Alternatively, you can run `gcloud auth application-default login` to authenticate via the gcloud CLI.
033: Verify Vertex AI configuration: Open Atlas and run the command `atlas models google-vertex` to confirm that your Vertex AI setup is correctly resolved and recognized by Atlas.
044: Select your desired model: Navigate to the `/models` interface within Atlas and choose a Gemini or Claude model. For Claude models on GCP, Atlas will automatically route requests through its dedicated `google-vertex-anthropic` provider.

Frequently asked questions

What is the context window for Gemini 3.1 Pro on Google Vertex AI (gateway)?: Gemini 3.1 Pro, when accessed via Google Vertex AI (gateway) in Atlas, offers a substantial 1M token context window. This allows for extensive code analysis and complex task execution within Atlas.
How much does Google Vertex AI (gateway) cost with Atlas?: Pricing for Google Vertex AI (gateway) through Atlas is passthrough at Google Cloud rates. For example, Gemini 3.1 Pro costs $2 per million input tokens and $12 per million output tokens.
Can I use both Gemini and Claude models with Atlas via Google Vertex AI (gateway)?: Yes, Google Vertex AI (gateway) allows Atlas users to run both Gemini and Anthropic's Claude models (like claude-opus-4-1 and claude-haiku-4-5) side by side within a single Google Cloud project, with unified billing.
What are the security benefits of using Google Vertex AI (gateway) with Atlas?: A primary security benefit is service-account authentication via `GOOGLE_APPLICATION_CREDENTIALS`, which is often required by enterprise security teams, offering a more secure alternative to long-lived API keys.
Is the setup for Google Vertex AI (gateway) complex for Atlas?: Yes, setting up Google Vertex AI (gateway) requires more steps than a simple API key. It involves configuring a GCP project, selecting a region, and enabling specific APIs before any tokens can be processed by Atlas.
How do I configure Atlas to use Google Vertex AI (gateway)?: You need to set `GOOGLE_VERTEX_PROJECT` and `GOOGLE_VERTEX_LOCATION` environment variables, and authenticate your service account by pointing `GOOGLE_APPLICATION_CREDENTIALS` to your JSON key or running `gcloud auth application-default login`. Then, select a model in Atlas's `/models` interface.
Does Atlas support Claude models directly through Google Vertex AI (gateway)?: Yes, Atlas ships a dedicated `google-vertex-anthropic` route, making Claude on Vertex AI a first-class provider rather than a workaround, ensuring direct integration and model switching.

Try SeaShell in your terminal

The terminal-native AI coding agent. Free core, single binary.

Install SeaShell

Related guides

Atlas for Expo: Terminal-Native AI Coding for expo-router and Config Plugins in 2026

Atlas is a terminal-native AI coding agent for Expo apps in 2026, covering expo-router file routes, config plugins, and EAS build profiles with diff-first review.

Atlas for Go in 2026

Atlas, the terminal-native AI coding agent, empowers Go developers in 2026 with intelligent code understanding, safe refactoring, and robust testing capabilities.

Atlas for dbt: Terminal-Native AI Coding in 2026

Atlas is a terminal-native AI coding agent for dbt. Read the ref() DAG, convert a table model to incremental, run dbt build against dev, and add tests in 2026.

Atlas vs Mistral Vibe for Code: Terminal AI Coding Agents in 2026

Compare Atlas and Mistral Vibe for Code in 2026. Atlas offers terminal-native TUI, permission-gated tools, and local embeddings. Mistral Vibe provides a four-model stack and EU data sovereignty.

Atlas for Polars: Terminal-Native AI Coding in 2026

Atlas is a terminal-native AI coding agent for Polars. Build LazyFrame chains, push scan_csv predicates into the reader, and read explain() plans in 2026.

Atlas vs Tabby: Terminal AI Coding Agents in 2026

Comparing Atlas and Tabby in 2026: Atlas offers terminal-native AI with explicit planning and diffing, while Tabby provides self-hosted GPU-based completion and a cloud agent.

Atlas for Spring in 2026

Atlas, the terminal native AI coding agent, empowers Spring developers in 2026 with intelligent code assistance, secure local embeddings, and transparent review processes for enhanced productivity.

Atlas vs Codebuff: Terminal AI Coding Agents in 2026

Comparing Atlas and Codebuff in 2026 for terminal AI coding agents. Atlas offers a free core with permission-gated tools and diff review. Codebuff provides free hours on DeepSeek V4 Flash.

Browse this resource hub