# Atlas with GPT-5.2 in 2026 > GPT-5.2, refreshed in December 2025, provides a substantial 400K token context window for Atlas users. GPT-5.2, refreshed in December 2025, offers Atlas users a robust general-purpose model with a 400K token context window and a 128K max output ceiling. It is well-suited for read-many, write-large agent turns within Atlas, despite its $1.75 per Mtok input and $14 per Mtok output pricing being higher than its predecessor. ## Key takeaways - GPT-5.2 offers a 400K token context window and 128K max output, suitable for large agent turns. - Pricing for GPT-5.2 is $1.75 per Mtok input and $14 per Mtok output. - GPT-5.2 is 40% more expensive per input token than its predecessor, GPT-5.1. - For pure coding, GPT-5.2 Codex is generally a better choice at the same price and context window. - Atlas allows on-the-fly model switching and head-to-head comparisons using /models. ## What is GPT-5.2 best at for Atlas? GPT-5.2, a December 2025 refresh, excels within Atlas for tasks requiring extensive context and substantial output, leveraging its 400K token context window. This model is a strong general-purpose choice for developers in 2026, especially for complex coding agent turns. GPT-5.2 from OpenAI is a frontier model, ideal for driving Atlas, the terminal-native AI coding agent. Its 400K token context window, paired with a 128K max output ceiling, provides a workable ratio for agent turns that involve reading large codebases and generating significant output. As the general purpose sibling of GPT-5.2 Codex, it offers flexibility for a wide range of coding tasks, allowing developers to swap between them on the same API key and context window. Atlas connects to Model Context Protocol servers and exposes their tools to the agent, making GPT-5.2's reasoning capabilities accessible through the Responses API. This enables Atlas to draft plans in a read-only plan agent, ask for approval, and then switch to a build agent, computing unified diffs for every file edit before writing. Atlas also uses Axis, the hybrid semantic and keyword code search, to efficiently retrieve relevant code, which benefits from GPT-5.2's large context. ## What are the cost and context tradeoffs of GPT-5.2? GPT-5.2 offers a substantial 400K token context window, but developers in 2026 must consider its increased pricing: $1.75 per Mtok input and $14 per Mtok output. This represents a 40% increase in input cost compared to GPT-5.1. While GPT-5.2 maintains the impressive 400K token context window and 128K max output ceiling of its predecessor, GPT-5.1, its pricing has increased. Input tokens are billed at $1.75 per Mtok, and output tokens at $14 per Mtok. This makes GPT-5.2 40% more expensive per input token than GPT-5.1, which was priced at $1.25 per Mtok input. Developers using Atlas need to weigh this increased cost against the model's general-purpose reasoning capabilities and large context window. For scenarios where cost optimization is paramount, especially for frequent or high-volume tasks, this pricing structure requires careful consideration. Atlas allows switching the active model and provider on the fly, enabling developers to compare GPT-5.2's performance and cost against other models for specific tasks. ## When should I pick a different model over GPT-5.2 for Atlas? For pure agentic coding tasks within Atlas, GPT-5.2 Codex is often a better choice than GPT-5.2, despite both models sharing the same 400K context window and pricing. GPT-5.2 is not post-trained specifically for coding, unlike its specialized sibling. While GPT-5.2 is a capable general-purpose model for Atlas, developers should consider GPT-5.2 Codex for tasks that are exclusively focused on code generation and manipulation. GPT-5.2 Codex is post-trained for coding, making it usually the better pick for pure agentic coding scenarios at an identical price point and with the same 400K token context window and 128K max output. Atlas allows developers to use /models to compare models head-to-head on the same repository task, facilitating an informed decision. Additionally, for very cheap calls or specific subagent tasks, setting "small_model": "openai/gpt-5-nano" can prevent billing at the full $14 per Mtok output rate of GPT-5.2, offering a cost-effective alternative for less demanding operations. Atlas's ability to fan out work to subagents, which can run in the foreground or in parallel background sessions, further supports using specialized or cheaper models for specific parts of a larger task. ## Setup 1. Set OPENAI_API_KEY in the environment. 2. Run `atlas models openai` to confirm gpt-5.2 resolves from models.dev. 3. Pin "model": "openai/gpt-5.2" in atlas.json. 4. Set "small_model": "openai/gpt-5-nano" so cheap calls do not bill at $14 per Mtok output. 5. Use /models to compare it head to head with gpt-5.2-codex on the same repo task. ## FAQ ### What is the context window size for GPT-5.2 in Atlas? GPT-5.2 provides a substantial 400K token context window for use within Atlas, enabling the agent to process extensive codebases and documentation. ### How much does it cost to use GPT-5.2 with Atlas? Using GPT-5.2 with Atlas costs $1.75 per Mtok for input tokens and $14 per Mtok for output tokens. This pricing is a key consideration for developers in 2026. ### Is GPT-5.2 better than GPT-5.2 Codex for coding in Atlas? For pure agentic coding tasks, GPT-5.2 Codex is typically the better choice as it is post-trained for coding, while GPT-5.2 is a general-purpose model. Both share the same 400K context window and pricing. ### Can I switch between GPT-5.2 and other models in Atlas? Yes, Atlas allows you to switch the active model and provider on the fly, with support for favorites and recents, making it easy to compare GPT-5.2 with other options. ### What is the maximum output for GPT-5.2 in Atlas? GPT-5.2 has a 128K token maximum output ceiling, which is a workable ratio for read-many, write-large agent turns within Atlas. ### How does GPT-5.2's pricing compare to GPT-5.1? GPT-5.2 is 40% more expensive per input token than GPT-5.1, with input tokens priced at $1.75 per Mtok compared to GPT-5.1's $1.25 per Mtok. ### How can I optimize costs when using GPT-5.2 with Atlas? To optimize costs, consider setting "small_model": "openai/gpt-5-nano" in your atlas.json for cheap calls, preventing them from billing at the full $14 per Mtok output rate of GPT-5.2. --- Canonical HTML: https://seashell.sh/resources/models/gpt-5-2 Source of truth: aeo_pages row `/resources/models/gpt-5-2` (segment: Models) (this file is generated from it, never hand-edited). 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