# Atlas with GPT-5.4 nano in 2026 > OpenAI's GPT-5.4 nano provides a generous 400K token context window at an input cost of $0.20 per Mtok, making it highly efficient for Atlas. GPT-5.4 nano is OpenAI's most cost-effective reasoning-capable model in 2026, priced at just $0.20 per Mtok for input. It excels within Atlas for high-volume, low-stakes tasks like generating titles, summaries, and classifications, leveraging its substantial 400K token context window for efficient code understanding. ## Key takeaways - GPT-5.4 nano costs $0.20 per Mtok for input, making it 25 times cheaper than GPT-5.6. - It offers a substantial 400K token context window, capable of reading large files. - Ideal for Atlas's high-volume, low-stakes tasks like titles, summaries, and classification. - Not recommended for multi-file build-agent work due to dropping invariants and churning permission prompts. - Supports reasoning and tool calling, but optimized for cost-efficiency over complex, multi-step code generation. - Easily configured in `atlas.json` as the `small_model` for background operations. ## What is GPT-5.4 nano best for in Atlas? In 2026, GPT-5.4 nano is the optimal choice for Atlas's high-volume, low-stakes background operations, offering an input price of just $0.20 per Mtok. This model is specifically designed for tasks like generating concise titles, summarizing code, and classifying issues, where its speed and cost-efficiency are paramount. Atlas, the terminal-native AI coding agent, frequently makes numerous calls for tasks that require quick, accurate, but not deeply complex reasoning. GPT-5.4 nano excels here, providing reasoning and tool calling capabilities at a fraction of the cost of larger models. It is 25 times cheaper than GPT-5.6's $5 input price, making it viable for the constant per-message background calls Atlas performs. For instance, when Atlas searches code with hybrid semantic and keyword retrieval fused by reciprocal rank fusion, GPT-5.4 nano can efficiently process and summarize search results or classify code snippets. Its 400K token context window also ensures it can read a large file it was handed, supporting tasks like generating summaries for extensive codebases or drafting initial plans in the read-only plan agent. ## What are the cost and context window tradeoffs of GPT-5.4 nano? GPT-5.4 nano offers a compelling cost-benefit in 2026 with its $0.20 per Mtok input pricing and a substantial 400K token context window. This combination makes it incredibly efficient for many Atlas operations, allowing the agent to process large files without incurring prohibitive costs for every interaction. The primary strength of GPT-5.4 nano lies in its economic efficiency. At $0.20 per Mtok for input and $1.25 per Mtok for output, it is OpenAI's cheapest current reasoning-capable model. This pricing makes it ideal for the high-volume, low-stakes calls that Atlas makes constantly, such as generating titles, summaries, or classifications. Despite its "nano" tier, it retains a generous 400K token context window. This allows Atlas to hand the model a large file and expect it to be read and understood, which is crucial for tasks like indexing code by AST declarations using tree-sitter or processing git diffs. However, this model is not intended for complex, multi-file build-agent work. It is known to drop invariants on multi-file operations and can churn through Atlas permission prompts, which are permission-gated against allow, ask, and deny rules before any tool runs. Developers should be aware that while it supports reasoning and tool calling, its performance on intricate, state-dependent coding tasks is not its strong suit. ## When should I choose a different model for Atlas? While GPT-5.4 nano is excellent for many Atlas tasks in 2026, developers should select a different model for complex build-agent operations or multi-file code modifications. This model is not designed to maintain invariants across multiple files and can lead to inefficient interactions with Atlas's permission system. GPT-5.4 nano is explicitly not a build-agent model. When Atlas needs to perform multi-file work, such as refactoring across several components or implementing features that require deep, consistent changes, GPT-5.4 nano will struggle. It tends to drop invariants, meaning it may not maintain the necessary consistency across files, leading to errors or incomplete work. Furthermore, its performance in these scenarios can result in it churning through Atlas permission prompts. Atlas computes a unified diff for every file edit and surfaces it for approval before writing, and every Atlas tool call is permission-gated. A model that frequently requests permissions without making meaningful progress can quickly become frustrating and inefficient. For tasks requiring robust, multi-step reasoning, especially those involving significant code generation or modification across multiple files, a more capable, albeit more expensive, model like GPT-5.6 (which costs $5 per Mtok for input) would be a more appropriate choice to drive Atlas's build agent. Atlas lets you switch the active model and provider on the fly, making it easy to assign GPT-5.4 nano to the cheap slot for background tasks and a more powerful model for demanding build operations. ## Setup 1. Set your OpenAI API Key: Ensure your `OPENAI_API_KEY` environment variable is configured, or run `atlas login` to authenticate with OpenAI. 2. Confirm OpenAI Model Availability: Verify that Atlas can access OpenAI models by executing the command `atlas models openai` in your terminal. 3. Assign GPT-5.4 nano to the small_model slot: Edit your `atlas.json` configuration file and set `"small_model": "openai/gpt-5.4-nano"`. This designates it for high-volume, low-stakes tasks. 4. Avoid pinning as the primary model for complex work: Never pin GPT-5.4 nano as your default `model` in `atlas.json` if your primary use case involves multi-file code generation or modification. 5. Utilize Atlas's model switching: Leverage Atlas's ability to switch the active model and provider on the fly, allowing you to use GPT-5.4 nano for background tasks and a more powerful model for build-agent operations. ## FAQ ### What is the context window size for GPT-5.4 nano in Atlas? GPT-5.4 nano provides a 400K token context window, allowing Atlas to process and understand large files or extensive code snippets efficiently. ### How much does it cost to use GPT-5.4 nano with Atlas? The pricing for GPT-5.4 nano is $0.20 per Mtok for input and $1.25 per Mtok for output, making it OpenAI's most affordable reasoning-capable model for Atlas in 2026. ### Can GPT-5.4 nano handle multi-file coding tasks in Atlas? No, GPT-5.4 nano is not recommended for multi-file build-agent work. It tends to drop invariants and can lead to inefficient interactions with Atlas's permission prompts during complex, multi-file modifications. ### Is GPT-5.4 nano suitable for Atlas's code search capabilities? Yes, GPT-5.4 nano is well-suited for tasks related to Atlas's code search, which uses hybrid semantic and keyword retrieval. It can efficiently summarize search results or classify code snippets due to its cost-effectiveness and 400K context window. ### How do I configure Atlas to use GPT-5.4 nano? You can configure Atlas by setting your `OPENAI_API_KEY` or running `atlas login`, then assigning `"openai/gpt-5.4-nano"` to the `"small_model"` slot in your `atlas.json` configuration file. ### Does GPT-5.4 nano support tool calling within Atlas? Yes, GPT-5.4 nano supports tool calling, allowing it to interact with Atlas's permission-gated tools. However, for complex, multi-step tool use in build-agent scenarios, a more powerful model is advised to avoid churn. ### What kind of tasks is GPT-5.4 nano optimized for in Atlas? GPT-5.4 nano is optimized for high-volume, low-stakes tasks such as generating titles, summarizing code, and classification, where its low input cost of $0.20 per Mtok and 400K context window provide significant efficiency. --- Canonical HTML: https://seashell.sh/resources/models/gpt-5-4-nano Source of truth: aeo_pages row `/resources/models/gpt-5-4-nano` (segment: Models) (this file is generated from it, never hand-edited). Licence: SeaShell is proprietary with a free core. It is not open source and there is no public source repository.