What is the context window size for GLM-4.7 in Atlas?

GLM-4.7 provides a substantial 204,800 token context window, allowing Atlas to process large codebases and extensive project context effectively.

How much does it cost to use GLM-4.7 with Atlas?

GLM-4.7 is priced at $0.60 per Mtok for input and $2.20 per Mtok for output. This output price is competitive against other high-context models.

Can GLM-4.7 generate long responses for Atlas?

Yes, GLM-4.7 has a maximum output token limit of 131,072, enabling Atlas to generate very long reasoning traces and comprehensive unified diffs without truncation.

Is GLM-4.7 the most powerful reasoning model available for Atlas?

While GLM-4.7 is a strong reasoner and a value pick, the GLM-5 line, particularly GLM-5.2, offers even stronger reasoning capabilities and a larger 1,000,000 token context window, albeit at a higher price.

What are the data residency implications of using GLM-4.7 with Atlas?

GLM-4.7 is served from Z.ai's China infrastructure. This is a critical consideration for users with regulated codebases or specific data sovereignty requirements, who may need to choose a different model provider.

How does GLM-4.7 compare to cheaper models in the same family for Atlas?

GLM-4.7 sits above GLM-4.7-Flash (free) and GLM-4.7-FlashX ($0.07 per Mtok input, $0.40 per Mtok output). You can easily configure Atlas to use GLM-4.7 for complex tasks and GLM-4.7-FlashX for simpler, cheaper operations.

Can Atlas switch between GLM-4.7 and other models easily?

Yes, Atlas allows you to switch the active model and provider on the fly. You can favorite models like `zai/glm-4.7` and `zai/glm-4.7-flashx` using the `/models` command in the TUI for quick switching.

Atlas with GLM-4.7 in 2026

GLM-4.7 is an excellent choice for driving Atlas in 2026, offering a strong balance of reasoning capability and cost-effectiveness. With a substantial 204,800 token context window and a competitive output price of $2.20 per Mtok, it excels at handling complex coding tasks, generating detailed plans, and processing large codebases within Atlas's agent framework.

What is GLM-4.7 best for in Atlas?

GLM-4.7, released in December 2025, is ideal for Atlas users requiring robust reasoning and extensive output at a competitive price point. Its 131,072 max output tokens enable Atlas to generate comprehensive plans and detailed code changes, making it a strong value pick for complex development workflows.

GLM-4.7's strong reasoning capabilities make it well-suited for Atlas's advanced features. When Atlas drafts a plan in its read-only plan agent, GLM-4.7 can process extensive context to formulate precise strategies before switching to a build agent. Its large 204,800 token context window allows Atlas to leverage Axis, the hybrid semantic and keyword code search, to retrieve and analyze large sections of code indexed by AST declarations using tree-sitter. This enables GLM-4.7 to understand complex project structures and generate accurate, permission-gated tool calls. The model's ability to produce 131,072 max output tokens means Atlas can present a long reasoning trace alongside a substantial unified diff for every file edit, which is then surfaced for approval before writing. This ensures developers have full transparency and control over the changes proposed by Atlas, even for large refactoring tasks or feature implementations. For users who prioritize detailed output and deep contextual understanding without the premium cost of the absolute top-tier models, GLM-4.7 provides significant value.

What are the cost and context tradeoffs for GLM-4.7?

GLM-4.7 offers a compelling cost-to-performance ratio in 2026, featuring a 204,800 token context window and an output price of $2.20 per Mtok. This pricing strategy positions it as a strong competitor, undercutting rivals like Kimi K2.6 and MiniMax M2.7-highspeed while matching their context capabilities.

While GLM-4.7 provides a generous 204,800 token context window, it is important to consider its position within the broader model landscape. Its input pricing is $0.60 per Mtok and output pricing is $2.20 per Mtok. This output price is notably competitive, undercutting Kimi K2.6 at $4.00 per Mtok and MiniMax M2.7-highspeed at $2.40 per Mtok, all while offering a comparable or superior context window. However, GLM-4.7 is not the ceiling choice for reasoning power. The newer GLM-5 line, with models like GLM-5.2 offering a 1,000,000 token window and stronger reasoning, comes at a higher price point of $1.00 per Mtok input and $3.20 per Mtok output. Therefore, GLM-4.7 represents a strategic price choice for Atlas users, balancing advanced capabilities with budget considerations. A significant tradeoff to note is that GLM-4.7 is served from Z.ai's China infrastructure. This might be a non-starter for organizations handling regulated codebases or those with specific data residency requirements, necessitating careful evaluation before deployment.

When should I pick a different model for Atlas?

While GLM-4.7 is a strong value pick, Atlas users might consider alternative models in 2026 depending on specific needs. For maximum reasoning power or an even larger context window, the GLM-5 line, with its 1,000,000 token window in GLM-5.2, offers superior capabilities at a higher cost.

Atlas's flexibility allows users to switch the active model and provider on the fly, making it easy to choose the right tool for the job. If your primary concern is absolute cost savings for simpler tasks, GLM-4.7-Flash (free) or GLM-4.7-FlashX ($0.07 per Mtok input, $0.40 per Mtok output) are excellent alternatives within the same GLM-4.7 family. These models are ideal for quick, less complex operations where the full reasoning power or extensive context of GLM-4.7 is not required. Conversely, for tasks demanding the absolute strongest reasoning capabilities or an even larger context window than GLM-4.7's 204,800 tokens, the GLM-5 line is a better fit. GLM-5.2, for instance, provides a 1,000,000 token context window, though at a higher price of $1.00 per Mtok input and $3.20 per Mtok output. Additionally, if your codebase is subject to regulations that prohibit data processing through Z.ai's China infrastructure, then GLM-4.7 would be unsuitable, and you would need to select a model from a different provider to drive Atlas.

Setup

01Export your ZHIPU_API_KEY environment variable or run `atlas login` and select Z.ai as your provider.
02Run `atlas models zai` in your terminal and confirm that `glm-4.7` resolves correctly.
03Edit your `atlas.json` configuration file to set `"model": "zai/glm-4.7"` for your primary model.
04For cost-effective fallback, also set `"small_model": "zai/glm-4.7-flashx"` in `atlas.json` to keep the cheap slot in the same family.
05Use the `/models` command within the Atlas TUI to favorite both `zai/glm-4.7` and `zai/glm-4.7-flashx` so you can cycle between the reasoning tier and the FlashX tier without editing config.

Frequently asked questions

What is the context window size for GLM-4.7 in Atlas?: GLM-4.7 provides a substantial 204,800 token context window, allowing Atlas to process large codebases and extensive project context effectively.
How much does it cost to use GLM-4.7 with Atlas?: GLM-4.7 is priced at $0.60 per Mtok for input and $2.20 per Mtok for output. This output price is competitive against other high-context models.
Can GLM-4.7 generate long responses for Atlas?: Yes, GLM-4.7 has a maximum output token limit of 131,072, enabling Atlas to generate very long reasoning traces and comprehensive unified diffs without truncation.
Is GLM-4.7 the most powerful reasoning model available for Atlas?: While GLM-4.7 is a strong reasoner and a value pick, the GLM-5 line, particularly GLM-5.2, offers even stronger reasoning capabilities and a larger 1,000,000 token context window, albeit at a higher price.
What are the data residency implications of using GLM-4.7 with Atlas?: GLM-4.7 is served from Z.ai's China infrastructure. This is a critical consideration for users with regulated codebases or specific data sovereignty requirements, who may need to choose a different model provider.
How does GLM-4.7 compare to cheaper models in the same family for Atlas?: GLM-4.7 sits above GLM-4.7-Flash (free) and GLM-4.7-FlashX ($0.07 per Mtok input, $0.40 per Mtok output). You can easily configure Atlas to use GLM-4.7 for complex tasks and GLM-4.7-FlashX for simpler, cheaper operations.
Can Atlas switch between GLM-4.7 and other models easily?: Yes, Atlas allows you to switch the active model and provider on the fly. You can favorite models like `zai/glm-4.7` and `zai/glm-4.7-flashx` using the `/models` command in the TUI for quick switching.

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