What is the context window size for GPT-5.6 Terra in Atlas?

GPT-5.6 Terra provides a substantial 1.05M tokens (1,050,000) context window, allowing Atlas to handle very large codebases and complex project requirements effectively.

How much does GPT-5.6 Terra cost per token for Atlas users?

For Atlas users, GPT-5.6 Terra is priced at $2.50 per Mtok for input tokens and $15 per Mtok for output tokens, offering a balanced cost profile.

Is GPT-5.6 Terra the most powerful model in the GPT-5.6 line for Atlas?

No, GPT-5.6 Terra is the mid-tier variant of the July 2026 GPT-5.6 line. While powerful, GPT-5.6 Sol and the full GPT-5.6 are higher-effort variants offering even greater capabilities, albeit at a higher price point.

How does Atlas handle reasoning state with GPT-5.6 Terra?

Atlas routes OpenAI models, including GPT-5.6 Terra, through `sdk.responses()`. This ensures that reasoning state is kept alive and persistent across multiple tool calls within the agent loop.

When should I consider GPT-5.6 Luna instead of Terra for Atlas?

You should consider GPT-5.6 Luna when cost-efficiency is your top priority for tasks that do not require the deep reasoning capabilities of Terra. Luna is priced at $1 per Mtok input, making it significantly cheaper for input-heavy operations.

Can Atlas use GPT-5.6 Terra for code indexing?

Yes, Atlas can leverage GPT-5.6 Terra's large context window and reasoning capabilities for tasks like indexing code by AST declarations using tree-sitter, and performing hybrid semantic and keyword retrieval for code searches.

What is the maximum output token limit for GPT-5.6 Terra?

GPT-5.6 Terra supports a maximum output of 128K tokens, which is consistent with the full GPT-5.6 line, allowing for extensive responses from Atlas agents.

Atlas with GPT-5.6 Terra (2026)

GPT-5.6 Terra is an excellent choice for Atlas users in 2026 seeking a balance of deep reasoning capabilities and cost-efficiency. It provides a substantial 1.05M token context window, enabling Atlas to handle large codebases effectively, at a competitive price point of $2.50 per Mtok input and $15 per Mtok output. This makes it ideal for complex coding tasks where full GPT-5.6 Sol pricing is too high, offering a compelling mid-tier option.

What is GPT-5.6 Terra best for in Atlas?

GPT-5.6 Terra, released in July 2026, excels within Atlas for tasks requiring significant reasoning depth without the premium cost of the top-tier GPT-5.6 Sol. Its 1.05M token context window allows Atlas to process extensive codebases and complex project requirements, making it a strong default choice for developers.

GPT-5.6 Terra is designed for Atlas users who need advanced problem-solving and code generation capabilities across large projects. Atlas leverages its 1.05M token context window to perform comprehensive code searches using hybrid semantic and keyword retrieval, index code by AST declarations using tree-sitter, and draft detailed plans in a read-only plan agent. Because Atlas keeps reasoning state alive across the tool call loop via the Responses API, GPT-5.6 Terra can maintain complex task understanding over multiple interactions. This model is particularly effective when Atlas needs to fan out work to subagents that can run in the foreground or in parallel background sessions, or compute unified diffs for file edits, ensuring thoroughness and accuracy in development workflows. Its ability to read git branches, status, and diffs, and stage and create commits on your behalf, is significantly enhanced by Terra's reasoning depth.

What are the cost and context tradeoffs for GPT-5.6 Terra?

GPT-5.6 Terra provides a substantial 1.05M token context window, identical to the full GPT-5.6 line, but at half the input price of GPT-5.6 Sol, costing $2.50 per Mtok input and $15 per Mtok output. This mid-tier pricing strategy offers a compelling balance for developers in 2026.

The primary tradeoff for GPT-5.6 Terra is its positioning as the mid-tier variant of the July 2026 GPT-5.6 line. While it offers the same impressive 1.05M token context window (1,050,000 tokens) and 128K max output as the higher-tier models, its input pricing is $2.50 per Mtok, compared to $5 for GPT-5.6 Sol. This makes it significantly more cost-effective for input-heavy tasks. However, it is important to remember that long context prompts still bill per token; loading a 1M token context will incur a $2.50 charge before any output is generated. Developers must weigh the benefit of its reasoning depth against the per-token cost, especially for tasks that frequently utilize the full context window. The output pricing of $15 per Mtok is also a consideration, sitting between Luna's $6 and Sol's $30.

When should I choose a different model over GPT-5.6 Terra?

While GPT-5.6 Terra is a strong default, developers in 2026 might consider other models when extreme cost-efficiency or the absolute peak of the GPT-5.6 line is required. For instance, Luna offers a significantly lower input price of $1 per Mtok, making it 2.5 times cheaper for input tokens.

Atlas users should consider alternatives to GPT-5.6 Terra when their primary concern is minimizing cost for less complex tasks or when they require the absolute highest performance available. For tasks where reasoning depth is less critical and cost is paramount, GPT-5.6 Luna, priced at $1 per Mtok input and $6 per Mtok output, offers a more economical option. Conversely, if the project demands the absolute current of the GPT-5.6 line and budget is not a constraint, the full GPT-5.6 Sol variant provides the highest effort capabilities, albeit at $5 per Mtok input and $30 per Mtok output. GPT-5.6 Terra sits between Luna and Sol as the default GPT-5.6 pick when you want reasoning depth without Sol pricing, but it is not the top of the 5.6 line, meaning Sol and full GPT-5.6 are the higher effort variants.

Setup

01Export your OPENAI_API_KEY environment variable, or run `atlas login` and choose OpenAI as your provider.
02Run `atlas models openai` in your terminal and confirm that `gpt-5.6-terra` resolves correctly.
03Set your active model by adding or updating `"model": "openai/gpt-5.6-terra"` in your `atlas.json` configuration file.
04Atlas routes OpenAI models through `sdk.responses()`, so no extra flag is needed to persist reasoning between tool calls with GPT-5.6 Terra.
05Use the `/models` command in the Atlas TUI to A/B test `gpt-5.6-terra` against `gpt-5.6-luna` on the same task and compare the cost in the session footer.

Frequently asked questions

What is the context window size for GPT-5.6 Terra in Atlas?: GPT-5.6 Terra provides a substantial 1.05M tokens (1,050,000) context window, allowing Atlas to handle very large codebases and complex project requirements effectively.
How much does GPT-5.6 Terra cost per token for Atlas users?: For Atlas users, GPT-5.6 Terra is priced at $2.50 per Mtok for input tokens and $15 per Mtok for output tokens, offering a balanced cost profile.
Is GPT-5.6 Terra the most powerful model in the GPT-5.6 line for Atlas?: No, GPT-5.6 Terra is the mid-tier variant of the July 2026 GPT-5.6 line. While powerful, GPT-5.6 Sol and the full GPT-5.6 are higher-effort variants offering even greater capabilities, albeit at a higher price point.
How does Atlas handle reasoning state with GPT-5.6 Terra?: Atlas routes OpenAI models, including GPT-5.6 Terra, through `sdk.responses()`. This ensures that reasoning state is kept alive and persistent across multiple tool calls within the agent loop.
When should I consider GPT-5.6 Luna instead of Terra for Atlas?: You should consider GPT-5.6 Luna when cost-efficiency is your top priority for tasks that do not require the deep reasoning capabilities of Terra. Luna is priced at $1 per Mtok input, making it significantly cheaper for input-heavy operations.
Can Atlas use GPT-5.6 Terra for code indexing?: Yes, Atlas can leverage GPT-5.6 Terra's large context window and reasoning capabilities for tasks like indexing code by AST declarations using tree-sitter, and performing hybrid semantic and keyword retrieval for code searches.
What is the maximum output token limit for GPT-5.6 Terra?: GPT-5.6 Terra supports a maximum output of 128K tokens, which is consistent with the full GPT-5.6 line, allowing for extensive responses from Atlas agents.

Try SeaShell in your terminal

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

Install SeaShell

Related guides

Atlas for SQL in 2026

Atlas is a terminal-native AI coding agent for SQL in 2026. Run it in a repo with your migrations or .sql files, optimize a query, and review the diff before applying.

Atlas vs Cline: Terminal AI Coding Agents in 2026

Atlas and Cline, top AI coding agents in 2026, present distinct workflows. Atlas is terminal-native with permission-gated tools. Cline, a VS Code agent, offers Plan/Act modes and checkpoint rollbacks.

Atlas vs Magic.dev: Terminal AI Coding Agents in 2026

Compare Atlas, the terminal-native AI coding agent, with Magic.dev's research claims in 2026. Evaluate product availability, code safety, and pricing for developers.

Atlas for Symfony in 2026

Atlas is a terminal-native AI coding agent for Symfony in 2026. It reads autowired services and Doctrine mappings, and shows migration SQL before anything runs.

Atlas for Axum in 2026

Atlas is a terminal-native AI coding agent for Axum in 2026. It decodes tower trait-bound errors, adds IntoResponse types, and runs cargo nextest run.

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 for Erlang in 2026

Atlas is a terminal-native AI coding agent for Erlang/OTP in 2026. Run it in an app with a rebar.config, map supervisors and gen_server modules, review every diff.

Atlas vs Traycer: Terminal AI Coding Agents in 2026

Compare Atlas, a terminal-native AI coding agent, with Traycer, an orchestration layer for existing agents, in 2026. Evaluate features, pricing, and workflow.

Browse this resource hub