What is the context window for OpenAI o3-pro in Atlas?

OpenAI o3-pro provides a 200K token context window for use with Atlas. This capacity allows it to handle substantial coding problems and complex codebases, enabling deep analysis and comprehensive problem-solving within its limits.

How much does OpenAI o3-pro cost with Atlas?

When driving Atlas, OpenAI o3-pro is priced at $20 per Mtok for input tokens and $80 per Mtok for output tokens. This pricing reflects its enhanced reasoning capabilities compared to base models.

Is OpenAI o3-pro good for interactive coding in Atlas?

No, OpenAI o3-pro has very high latency, making it unsuitable as a default or for interactive sessions in Atlas. It is best reserved for hard, non-interactive problems where its extensive reasoning budget can be applied without impacting real-time workflow.

How does o3-pro compare to other OpenAI models for Atlas?

OpenAI o3-pro offers 10x the reasoning budget of base o3, making it significantly more capable for complex tasks. It is also 7.5x cheaper than o1-pro on both input ($20 vs $150) and output ($80 vs $600), positioning it as a powerful yet more cost-effective option for high-effort, non-interactive tasks in Atlas.

Can Atlas generate long responses with OpenAI o3-pro?

Yes, OpenAI o3-pro has a 100K max output token limit, which allows Atlas to generate very long derivations, detailed design documents, or extensive code blocks in a single response, ensuring comprehensive outputs for complex problems.

How do I manage costs when using OpenAI o3-pro in Atlas?

To effectively manage costs with OpenAI o3-pro in Atlas, it is crucial to give it tight, pre-scoped prompts from the Atlas plan agent to avoid unnecessary token usage for file discovery. Additionally, diligently watch the cost footer in Atlas, especially since output tokens bill at a substantially higher rate of $80 per Mtok.

What are the main tradeoffs of using OpenAI o3-pro with Atlas?

The main tradeoffs for OpenAI o3-pro in Atlas are its very high latency, which makes it unsuitable for interactive use, and its 200K context window, which is smaller than some modern GPT-5 releases at 400K to 1.05M tokens in 2026.

When should I switch to OpenAI o3-pro in Atlas?

You should switch to OpenAI o3-pro in Atlas using /models only for single, hard problems that require its substantial reasoning budget and can tolerate its high latency. It is not recommended for default or routine interactive tasks.

Atlas with OpenAI o3-pro in 2026

OpenAI o3-pro is ideal for Atlas users tackling hard, non-interactive coding problems requiring deep reasoning, offering a 200K token context window. It costs $20 per Mtok for input and $80 per Mtok for output, providing a substantial reasoning budget for complex tasks in 2026.

What is OpenAI o3-pro best for in Atlas?

OpenAI o3-pro, released in June 2025, excels within Atlas for tackling highly complex, non-interactive coding challenges that demand extensive reasoning. Its 10x larger reasoning budget compared to base o3 allows Atlas to perform deep analysis and generate comprehensive solutions for difficult problems.

OpenAI o3-pro is specifically engineered for scenarios where Atlas needs to perform deep, multi-step reasoning over a substantial codebase. Its enhanced reasoning compute, a direct result of its $20 per Mtok input and $80 per Mtok output pricing, enables Atlas to process intricate logic, understand complex architectural patterns, and devise sophisticated solutions. This model is particularly effective when Atlas is given a tightly pre-scoped prompt from the Atlas plan agent, allowing it to focus its considerable capabilities on a well-defined problem without incurring unnecessary costs for initial file discovery. For instance, when Atlas needs to draft a detailed design document, generate a long derivation, or perform a comprehensive code refactoring that impacts multiple files and requires a deep understanding of dependencies, o3-pro's 100K max output ensures the entire response fits within a single generation. Atlas's ability to fan out work to subagents can leverage o3-pro for specific, demanding sub-tasks, running them in parallel background sessions while other agents handle more interactive components. This allows developers to delegate the most challenging analytical and generative tasks to o3-pro, trusting its robust reasoning to deliver high-quality, thorough outputs for critical project phases. Atlas's Axis, the hybrid semantic and keyword code search, can efficiently feed relevant context to o3-pro, ensuring the model operates on the most pertinent information.

OpenAI o3-pro cost and context tradeoffs

In 2026, OpenAI o3-pro presents a distinct set of cost and context tradeoffs for Atlas users, featuring a 200K token context window and pricing at $20 per Mtok input and $80 per Mtok output. This model is 10x more expensive than base o3, reflecting its significantly larger per-request reasoning budget.

The primary tradeoff with OpenAI o3-pro is its very high latency, rendering it unusable as a default model in an interactive Atlas session. Developers must consciously switch to it using /models only for specific, hard problems, understanding that real-time feedback will be significantly delayed. While its 200K token context window is substantial for many tasks, it is considered small relative to modern GPT-5 releases in 2026, which offer context windows ranging from 400K to 1.05M tokens. This means that for extremely large codebases or problems requiring an even broader understanding of an entire repository, o3-pro might necessitate more careful prompt engineering, strategic use of Atlas's code indexing by AST declarations, or chunking of information to fit within its limits. The pricing structure, at $20 per Mtok input and $80 per Mtok output, represents a significant investment, being 10 times that of the base o3 model. However, it remains a more economical choice than o1-pro, being 7.5 times cheaper on both input ($20 vs $150) and output ($80 vs $600), positioning it as a mid-to-high-tier option for reasoning-intensive tasks. Atlas users must diligently watch the cost footer, a critical feature for monitoring expenditure, especially since output tokens bill at a substantially higher rate of $80 per Mtok, making concise and targeted prompts essential.

When to pick a different model for Atlas

Developers using Atlas in 2026 should opt for a different model when interactive speed is paramount or when the problem does not demand the extensive reasoning budget of OpenAI o3-pro. Its very high latency makes it unsuitable for any default or highly interactive use case.

OpenAI o3-pro's high latency makes it a poor choice for any interactive Atlas workflow, such as real-time code suggestions, quick file navigation, or rapid iteration during development. For these scenarios, a faster, lower-cost model is preferable, even if it has a smaller context window or less reasoning capability. Atlas's TUI theme system, for example, is designed for responsive interaction, which would be undermined by o3-pro's delays. Additionally, if the task involves simple code generation, refactoring small functions, or answering straightforward questions that do not require deep architectural understanding or complex problem-solving, the $20 per Mtok input and $80 per Mtok output cost of o3-pro is likely overkill. Atlas's ability to switch the active model and provider on the fly with favorites and recents allows developers to easily select a more cost-effective and responsive model for routine tasks, reserving o3-pro for its intended purpose: hard, non-interactive problems where its 10x reasoning budget can be fully leveraged. For problems requiring context windows larger than 200K tokens, especially those approaching 400K to 1.05M tokens available in other 2026 models, a different model would be more efficient to avoid context truncation or excessive prompt engineering. Atlas's plan agent can help scope problems appropriately, guiding the choice of model.

Setup

01Export OPENAI_API_KEY.
02Run `atlas models openai` and check o3-pro.
03Keep it off the default: switch to it with `/models` only for a single hard problem.
04Give it a tight, pre-scoped prompt from the Atlas plan agent so you are not paying $20 per Mtok for file discovery.
05Watch the cost footer, since output bills at $80 per Mtok.

Frequently asked questions

What is the context window for OpenAI o3-pro in Atlas?: OpenAI o3-pro provides a 200K token context window for use with Atlas. This capacity allows it to handle substantial coding problems and complex codebases, enabling deep analysis and comprehensive problem-solving within its limits.
How much does OpenAI o3-pro cost with Atlas?: When driving Atlas, OpenAI o3-pro is priced at $20 per Mtok for input tokens and $80 per Mtok for output tokens. This pricing reflects its enhanced reasoning capabilities compared to base models.
Is OpenAI o3-pro good for interactive coding in Atlas?: No, OpenAI o3-pro has very high latency, making it unsuitable as a default or for interactive sessions in Atlas. It is best reserved for hard, non-interactive problems where its extensive reasoning budget can be applied without impacting real-time workflow.
How does o3-pro compare to other OpenAI models for Atlas?: OpenAI o3-pro offers 10x the reasoning budget of base o3, making it significantly more capable for complex tasks. It is also 7.5x cheaper than o1-pro on both input ($20 vs $150) and output ($80 vs $600), positioning it as a powerful yet more cost-effective option for high-effort, non-interactive tasks in Atlas.
Can Atlas generate long responses with OpenAI o3-pro?: Yes, OpenAI o3-pro has a 100K max output token limit, which allows Atlas to generate very long derivations, detailed design documents, or extensive code blocks in a single response, ensuring comprehensive outputs for complex problems.
How do I manage costs when using OpenAI o3-pro in Atlas?: To effectively manage costs with OpenAI o3-pro in Atlas, it is crucial to give it tight, pre-scoped prompts from the Atlas plan agent to avoid unnecessary token usage for file discovery. Additionally, diligently watch the cost footer in Atlas, especially since output tokens bill at a substantially higher rate of $80 per Mtok.
What are the main tradeoffs of using OpenAI o3-pro with Atlas?: The main tradeoffs for OpenAI o3-pro in Atlas are its very high latency, which makes it unsuitable for interactive use, and its 200K context window, which is smaller than some modern GPT-5 releases at 400K to 1.05M tokens in 2026.
When should I switch to OpenAI o3-pro in Atlas?: You should switch to OpenAI o3-pro in Atlas using /models only for single, hard problems that require its substantial reasoning budget and can tolerate its high latency. It is not recommended for default or routine interactive tasks.

Try SeaShell in your terminal

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

Install SeaShell

Related guides

Atlas for Electron: Terminal-Native AI Coding for Main, Preload, and Renderer in 2026

Atlas is a terminal-native AI coding agent for Electron in 2026, where the main and renderer split, contextIsolation, and preload bridges are the security model.

Atlas for Nim: A Terminal-Native AI Coding Agent for Nimble Packages and Macros in 2026

Atlas is a terminal-native AI coding agent for Nim in 2026. It reads .nimble requires and asterisk-exported symbols, adds std/unittest suites, runs nimble test, formats with nph.

Atlas for Svelte in 2026

Adopt Atlas, the terminal-native AI coding agent, for Svelte and SvelteKit projects in 2026. Enhance development with intelligent code search, secure local embeddings, and guided code generation.

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.

Locate Where a Behavior Is Implemented with Atlas in 2026

How to locate where a behavior is implemented with Atlas in 2026: codebase_search for meaning, grep for exact text, and the lsp tool for the symbol graph.

Atlas vs. Graphite: Choosing Your AI Coding Agent in 2026

Atlas and Graphite comparison for 2026. Atlas offers terminal-native AI with permission-gated tools and local embeddings. Graphite provides stacked PRs and AI review for GitHub teams.

Atlas for Swift in 2026

Atlas for Swift in 2026 empowers developers with a terminal-native AI coding agent. Index code by AST, ensure privacy with local embeddings, and review changes with unified diffs.

Atlas vs Qodo: Choosing Your AI Coding Agent in 2026

Compare Atlas, the terminal-native AI coding agent, with Qodo 2.0, the multi-agent PR reviewer, for developers in 2026. Evaluate features, pricing, and workflow.

Browse this resource hub