What is the cost of GPT-4.1 nano for Atlas users?

The cost for GPT-4.1 nano is $0.10 per Mtok for input and $0.40 per Mtok for output. This makes it a highly economical option for processing large inputs within Atlas, especially for its designated side tasks.

What is the context window for GPT-4.1 nano?

GPT-4.1 nano features a substantial 1M token context window, precisely 1,047,576 tokens. This large capacity allows Atlas to provide extensive context for classification and extraction tasks.

Can GPT-4.1 nano handle complex coding tasks in Atlas?

No, GPT-4.1 nano is not viable as the main Atlas model for complex coding tasks. It lacks reasoning capabilities and will not perform multi-step refactors or other operations requiring logical thought. It is a bulk classification and extraction model, not a coding agent.

Why choose GPT-4.1 nano over gpt-5-nano for Atlas?

You would choose GPT-4.1 nano for its extremely low latency and its role as the cheapest large context option in the 4.1 family for tasks that require no reasoning at all. If reasoning is needed, even in a cheap slot, gpt-5-nano ($0.05 in, reasoning enabled) is generally a better choice.

How do I configure Atlas to use GPT-4.1 nano?

To configure Atlas, export your `OPENAI_API_KEY`, run `atlas models openai`, then set `"small_model": "openai/gpt-4.1-nano"` in your Atlas configuration. Ensure your main `"model"` is set to a reasoning model like `openai/gpt-5-codex` for core work.

Does Atlas use GPT-4.1 nano for all tasks by default?

No, Atlas does not use GPT-4.1 nano for all tasks by default. It is intended for the 'cheap slot' or 'small_model' configuration, handling side tasks like session titling. A separate, more capable reasoning model is required for the main agent work.

Atlas with GPT-4.1 nano in 2026

GPT-4.1 nano is an excellent choice for Atlas's side tasks in 2026, offering a highly cost-effective solution at just $0.10 per Mtok input and $0.40 per Mtok output. It excels at bulk classification, file triage, commit message drafting, and session titling, leveraging its full 1,047,576 token context window for speed and efficiency.

What is GPT-4.1 nano best for in Atlas?

GPT-4.1 nano, available in 2026, excels at Atlas side tasks like file triage and commit message drafting due to its very low latency and cost-effective $0.10 per Mtok input. It leverages a full 1,047,576 token context window for efficient bulk classification and extraction, making it the cheapest large context option in the 4.1 family.

As a developer using Atlas in 2026, you will find GPT-4.1 nano perfectly suited for tasks that require speed and a large context window without complex reasoning. Its strengths lie in bulk classification and extraction, making it ideal for Atlas functions such as file triage, drafting concise commit messages, and generating informative session titles. The model's very low latency ensures that these background operations are performed quickly, enhancing your workflow without noticeable delays. Atlas can build its code index with local Ollama embeddings, keeping code off third-party servers, and GPT-4.1 nano can assist in processing metadata or summaries derived from this local indexing. This model is designed to handle high volumes of text efficiently, making it a valuable asset for automating routine, non-reasoning-intensive aspects of your coding agent experience.

Cost and context tradeoffs for GPT-4.1 nano

The GPT-4.1 nano model offers a compelling $0.10 per Mtok input price point, making it the cheapest large context option in the 4.1 family. Developers in 2026 can utilize its 1M token window, which is precisely 1,047,576 tokens, but must weigh its lack of reasoning capabilities against this significant cost advantage.

When considering GPT-4.1 nano for Atlas, its pricing structure is a key factor: $0.10 per Mtok for input and $0.40 per Mtok for output. This makes it exceptionally economical for processing large amounts of input data, especially given its expansive 1,047,576 token context window. This large window allows Atlas to feed substantial code snippets or documentation for classification or extraction tasks. However, the primary tradeoff is its complete lack of reasoning capabilities. While it can process and classify information efficiently, it cannot perform multi-step refactors, complex problem-solving, or generate intricate code logic. Its 32,768 token maximum output further limits its utility for generating extensive code blocks or detailed explanations. Therefore, while it excels at cost-effective data handling, it requires pairing with a more capable reasoning model for core development work within Atlas.

When to pick a different model for Atlas

While GPT-4.1 nano provides a cost-effective $0.10 per Mtok input, it is not viable as the main Atlas model for complex operations like multi-step refactors. For such tasks, or even a better cheap slot that includes reasoning, gpt-5-nano, priced at $0.05 in, is often a superior choice for developers in 2026.

You should choose a different model for Atlas when your tasks require any form of reasoning, complex problem-solving, or multi-step code modifications. GPT-4.1 nano is explicitly not a coding agent and will not survive a multi-step refactor or any task demanding logical progression and decision-making. For the core work of an AI coding agent, such as drafting a plan in a read-only plan agent or executing changes in a build agent, you will need a dedicated reasoning model. For instance, pointing Atlas's main 'model' configuration at a reasoning model like openai/gpt-5-codex is essential for real development work. Furthermore, if you need a cheap slot model that *does* offer reasoning, even if minimal, gpt-5-nano is usually a better option. Priced at $0.05 per Mtok input, gpt-5-nano provides reasoning capabilities that GPT-4.1 nano entirely lacks, making it more versatile for certain cost-sensitive, reasoning-enabled tasks within Atlas.

Setup

01Export your OpenAI API key: `export OPENAI_API_KEY="your_key_here"`
02Run `atlas models openai` in your terminal to confirm gpt-4.1-nano is recognized.
03Configure Atlas to use gpt-4.1-nano specifically in the cheap slot by setting `"small_model": "openai/gpt-4.1-nano"` in your Atlas configuration.
04Point your main Atlas 'model' configuration at a reasoning model, such as `"model": "openai/gpt-5-codex"`, for the real development work.
05Confirm the model split in the Atlas TUI: the status line will display your main reasoning model, while session titles and other side tasks will be handled by the `small_model` (gpt-4.1-nano).

Frequently asked questions

What is GPT-4.1 nano best for in Atlas?: GPT-4.1 nano is best for Atlas side tasks that require bulk classification, extraction, and very low latency, such as file triage, drafting commit messages, and titling sessions. It leverages its 1,047,576 token context window for these efficient, non-reasoning tasks.
What is the cost of GPT-4.1 nano for Atlas users?: The cost for GPT-4.1 nano is $0.10 per Mtok for input and $0.40 per Mtok for output. This makes it a highly economical option for processing large inputs within Atlas, especially for its designated side tasks.
What is the context window for GPT-4.1 nano?: GPT-4.1 nano features a substantial 1M token context window, precisely 1,047,576 tokens. This large capacity allows Atlas to provide extensive context for classification and extraction tasks.
Can GPT-4.1 nano handle complex coding tasks in Atlas?: No, GPT-4.1 nano is not viable as the main Atlas model for complex coding tasks. It lacks reasoning capabilities and will not perform multi-step refactors or other operations requiring logical thought. It is a bulk classification and extraction model, not a coding agent.
Why choose GPT-4.1 nano over gpt-5-nano for Atlas?: You would choose GPT-4.1 nano for its extremely low latency and its role as the cheapest large context option in the 4.1 family for tasks that require no reasoning at all. If reasoning is needed, even in a cheap slot, gpt-5-nano ($0.05 in, reasoning enabled) is generally a better choice.
How do I configure Atlas to use GPT-4.1 nano?: To configure Atlas, export your `OPENAI_API_KEY`, run `atlas models openai`, then set `"small_model": "openai/gpt-4.1-nano"` in your Atlas configuration. Ensure your main `"model"` is set to a reasoning model like `openai/gpt-5-codex` for core work.
Does Atlas use GPT-4.1 nano for all tasks by default?: No, Atlas does not use GPT-4.1 nano for all tasks by default. It is intended for the 'cheap slot' or 'small_model' configuration, handling side tasks like session titling. A separate, more capable reasoning model is required for the main agent work.

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