What is the context window size for Gemini 2.5 Pro in Atlas?

Gemini 2.5 Pro provides a substantial 1M tokens (1,048,576) context window, which is highly beneficial for Atlas when analyzing large codebases and performing complex refactoring tasks. This allows Atlas to maintain a deep understanding of your project.

How much does it cost to use Gemini 2.5 Pro with Atlas?

Using Gemini 2.5 Pro with Atlas costs $1.25 per Mtok for input tokens and $10 per Mtok for output tokens. The input cost is 37 percent cheaper than Gemini 3 Pro for the same 1,048,576 token window, making it very economical for reading code.

Is Gemini 2.5 Pro still a good choice in 2026 compared to newer models?

Yes, in 2026, Gemini 2.5 Pro remains a strong default, especially for long-context coding work where cost-efficiency for input tokens is a priority. While newer Gemini 3 models offer advanced capabilities, Gemini 2.5 Pro's stable performance and lower input cost make it a valuable option for many Atlas tasks.

Can Atlas use Gemini 2.5 Pro for generating long code outputs?

Yes, Gemini 2.5 Pro supports a 65,536 token output cap, which is comfortably large enough for Atlas to generate detailed code suggestions, multi-file modifications, and comprehensive explanations. This output capacity exceeds many older models.

How do I configure Atlas to use Gemini 2.5 Pro?

To configure Atlas, first export your `GOOGLE_GENERATIVE_AI_API_KEY` or `GEMINI_API_KEY`. Then, verify `gemini-2.5-pro` is listed by `atlas models google`. Finally, add "model": "google/gemini-2.5-pro" to your `atlas.json` file.

What are the main tradeoffs of using Gemini 2.5 Pro with Atlas?

The primary tradeoff is that Gemini 2.5 Pro has been superseded on raw capability by the Gemini 3 line, meaning hard reasoning tasks might favor newer checkpoints. Additionally, its $10 per Mtok output cost is only a modest saving over Gemini 3 Pro's $12.

Does Atlas leverage Gemini 2.5 Pro's context window effectively?

Absolutely. Atlas is designed to maximize large context windows. Its plan agent uses the 1M token window to thoroughly analyze code before making changes, and Axis, Atlas's hybrid semantic and keyword code search, benefits from the model's ability to process extensive relevant information.

Atlas with Gemini 2.5 Pro in 2026

Gemini 2.5 Pro is an excellent choice for Atlas users in 2026 who prioritize cost-effective long-context coding. With its 1,048,576 token window and an input price of $1.25 per Mtok, it excels at reading extensive codebases for tasks like refactoring and analysis within Atlas's plan agent, offering a 37 percent saving over Gemini 3 Pro for input tokens.

When is Gemini 2.5 Pro the best model for Atlas?

Gemini 2.5 Pro, released in June 2025, remains a strong default for long-context coding work within Atlas, especially when reading large codebases. Its 1,048,576 token context window is ideal for Atlas's plan agent to analyze extensive projects before drafting changes.

Atlas users in 2026 will find Gemini 2.5 Pro particularly effective for tasks requiring deep understanding of large codebases. Its 1M token context window allows Atlas to ingest significant amounts of code, enabling comprehensive analysis for refactoring, debugging, and feature implementation. This model is well-suited for Atlas's plan agent, which drafts a plan in a read-only state, leveraging the extensive context to form a robust strategy before switching to the build agent. The model's stable GA id, gemini-2.5-pro, ensures consistent behavior, which is crucial for predictable agent performance across multiple sessions. Atlas's ability to search code with Axis, its hybrid semantic and keyword retrieval system, further benefits from Gemini 2.5 Pro's large context, allowing the model to process and reason over more relevant code snippets.

How cost-effective is Gemini 2.5 Pro for Atlas?

Gemini 2.5 Pro offers significant cost savings for input tokens, priced at $1.25 per Mtok, making it 37 percent cheaper to read with than Gemini 3 Pro. This makes it an economical choice for Atlas users who frequently engage in code analysis and large-scale refactoring tasks.

For developers using Atlas in 2026, Gemini 2.5 Pro presents a compelling economic advantage, especially for input-heavy operations. Its $1.25 per Mtok input cost is 37 percent less than Gemini 3 Pro's $2 for the same 1,048,576 token context window. This cost efficiency is particularly beneficial when Atlas is indexing code by AST declarations using tree-sitter or performing extensive code searches with Axis, where large volumes of code are fed into the model. While the output pricing at $10 per Mtok is only a modest saving over Gemini 3 Pro's $12, the substantial input savings make Gemini 2.5 Pro a smart choice for tasks where the agent primarily reads and processes existing code before generating a final output.

What is Gemini 2.5 Pro's context window and reasoning capability?

Gemini 2.5 Pro provides a substantial 1M tokens (1,048,576) context window, a feature it pioneered for the Pro tier, enabling deep reasoning over large codebases. This model also supports a 65,536 token output cap, comfortably exceeding older models' shorter output profiles.

The 1,048,576 token context window of Gemini 2.5 Pro is a key strength for Atlas users, allowing the model to maintain a comprehensive understanding of complex projects. This extensive context is crucial for Atlas's ability to draft detailed plans and compute unified diffs for every file edit, ensuring changes are well-informed. The 65,536 token output cap further enhances its utility, providing ample space for the model to generate detailed explanations, code suggestions, and multi-file modifications. This capability is particularly useful when Atlas fans out work to subagents or needs to present a thorough plan for approval, as it can articulate complex reasoning without being constrained by short output limits, unlike older models with 128K context but shorter outputs.

What are the tradeoffs of Gemini 2.5 Pro, and when should I use a different model?

While Gemini 2.5 Pro offers excellent value for its 1M token context, it has been superseded on raw capability by the newer Gemini 3 line. For hard reasoning tasks or highly complex problem-solving, developers in 2026 might find the latest Gemini 3 Pro checkpoints more effective, despite their higher input cost.

Developers using Atlas should be aware that while Gemini 2.5 Pro is a strong performer, it is superseded on capability by the Gemini 3 line. For tasks demanding the absolute current in reasoning or when tackling exceptionally intricate problems, the newer Gemini 3 Pro checkpoints may offer superior performance. Atlas allows you to switch the active model and provider on the fly with favorites and recents, making it easy to A/B test gemini-2.5-pro against gemini-3-pro-preview mid-session. The output cost of Gemini 2.5 Pro, at $10 per Mtok, is also only a modest saving compared to Gemini 3 Pro's $12, so if output length and quality are paramount for a specific task, the marginal cost difference might be acceptable for the newer model's enhanced capabilities. Consider the complexity of the task: for routine coding, refactoring, and code understanding, Gemini 2.5 Pro is highly capable; for frontier research or highly abstract problem-solving, a newer model might be warranted.

Setup

011: Export your API key: Set either GOOGLE_GENERATIVE_AI_API_KEY or GEMINI_API_KEY in your environment with an AI Studio key.
022: Verify model availability: Run `atlas models google` in your terminal and confirm that `gemini-2.5-pro` is listed as a stable ID.
033: Configure Atlas: Add "model": "google/gemini-2.5-pro" to your `atlas.json` configuration file.
044: Optimize for large refactors: For extensive code changes, use the Atlas plan agent first. This ensures the 1M token window is primarily spent on reading and understanding the codebase before the build agent begins writing.
055: Switch models mid-session: If you wish to compare its performance, use the `/models` command within an Atlas session to A/B test `gemini-2.5-pro` against `gemini-3-pro-preview`.

Frequently asked questions

What is the context window size for Gemini 2.5 Pro in Atlas?: Gemini 2.5 Pro provides a substantial 1M tokens (1,048,576) context window, which is highly beneficial for Atlas when analyzing large codebases and performing complex refactoring tasks. This allows Atlas to maintain a deep understanding of your project.
How much does it cost to use Gemini 2.5 Pro with Atlas?: Using Gemini 2.5 Pro with Atlas costs $1.25 per Mtok for input tokens and $10 per Mtok for output tokens. The input cost is 37 percent cheaper than Gemini 3 Pro for the same 1,048,576 token window, making it very economical for reading code.
Is Gemini 2.5 Pro still a good choice in 2026 compared to newer models?: Yes, in 2026, Gemini 2.5 Pro remains a strong default, especially for long-context coding work where cost-efficiency for input tokens is a priority. While newer Gemini 3 models offer advanced capabilities, Gemini 2.5 Pro's stable performance and lower input cost make it a valuable option for many Atlas tasks.
Can Atlas use Gemini 2.5 Pro for generating long code outputs?: Yes, Gemini 2.5 Pro supports a 65,536 token output cap, which is comfortably large enough for Atlas to generate detailed code suggestions, multi-file modifications, and comprehensive explanations. This output capacity exceeds many older models.
How do I configure Atlas to use Gemini 2.5 Pro?: To configure Atlas, first export your `GOOGLE_GENERATIVE_AI_API_KEY` or `GEMINI_API_KEY`. Then, verify `gemini-2.5-pro` is listed by `atlas models google`. Finally, add "model": "google/gemini-2.5-pro" to your `atlas.json` file.
What are the main tradeoffs of using Gemini 2.5 Pro with Atlas?: The primary tradeoff is that Gemini 2.5 Pro has been superseded on raw capability by the Gemini 3 line, meaning hard reasoning tasks might favor newer checkpoints. Additionally, its $10 per Mtok output cost is only a modest saving over Gemini 3 Pro's $12.
Does Atlas leverage Gemini 2.5 Pro's context window effectively?: Absolutely. Atlas is designed to maximize large context windows. Its plan agent uses the 1M token window to thoroughly analyze code before making changes, and Axis, Atlas's hybrid semantic and keyword code search, benefits from the model's ability to process extensive relevant information.

Try SeaShell in your terminal

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

Install SeaShell

Related guides

Atlas vs Sourcery: Terminal AI Coding Agents in 2026

Comparing Atlas, the terminal-native AI coding agent, with Sourcery, the Python-first AI reviewer, in 2026. Evaluate features, pricing, and workflow for developers.

Atlas vs Devin: Terminal AI Coding Agents in 2026

Atlas and Devin offer distinct approaches to AI coding in 2026. Compare their terminal-native TUI, sandboxed VMs, pricing, and code safety features.

Atlas vs Poolside: Terminal AI Coding Agents in 2026

Compare Atlas and Poolside in 2026. Atlas offers a terminal-native TUI with permission-gated tools and a free core. Poolside provides open-weight Laguna models and on-prem deployment options.

Atlas vs JetBrains Junie: Terminal AI Coding Agents in 2026

Comparing Atlas and JetBrains Junie in 2026. Atlas offers a terminal-native TUI with permission-gated tools and local indexing. JetBrains Junie features IDE-integrated debugging and plan mode.

Document a Module with a README Using Atlas (2026 Workflow)

How to document a module with a README using Atlas in 2026: the lsp tool's documentSymbol enumerates the real exports, read supplies the behavior, write emits the README.

Atlas vs OpenHands: Terminal AI Coding Agents in 2026

Comparing Atlas and OpenHands in 2026 for AI coding agents. Atlas offers terminal-native TUI and explicit change review, while OpenHands provides an open-source platform with skills and a limited free cloud tier.

Atlas vs Jules: Choosing Your AI Coding Agent in 2026

Comparing Atlas and Jules in 2026: Atlas offers terminal-native TUI and local execution, while Jules provides cloud-based async GitHub integration with a Critic agent.

Research a Third-Party API Before Integrating It with Atlas in 2026

How to research a third-party API with Atlas in 2026: websearch finds the current docs, webfetch pulls the page as markdown or text, and grep checks repo conventions.

Browse this resource hub