What is the context window for GLM-4.6 when used with Atlas?

GLM-4.6 provides a generous 200K token (204,800) context window for Atlas, enabling the agent to process extensive codebases and maintain long conversational histories for complex tasks.

How much does GLM-4.6 cost per token for Atlas users?

For Atlas users, GLM-4.6 is priced at $0.60 per Mtok input and $2.20 per Mtok output. This pricing remained unchanged despite the significant expansion of its context window from GLM-4.5.

Is GLM-4.6 an open-weights model, and can I self-host it for Atlas?

Yes, GLM-4.6 is an open-weights model, available at `zai-org/GLM-4.6` on HuggingFace. This allows Atlas users to self-host the model, ensuring that the benchmarked performance is consistent with your deployed version and enabling local code indexing with Ollama embeddings.

What are the key improvements of GLM-4.6 over GLM-4.5 for Atlas?

GLM-4.6 significantly improves upon GLM-4.5 by expanding its context window from 128K to 200K (204,800) tokens and lifting the maximum output cap to 131,072 tokens, a 33 percent increase. These enhancements are crucial for Atlas when handling large multi-file diffs and complex coding scenarios.

Are there any data residency concerns when using GLM-4.6 with Atlas?

Yes, the Z.ai API for GLM-4.6 is China-hosted. If US data residency is a hard requirement for your Atlas operations, you can route the model through OpenRouter or another gateway, which Atlas supports via `@openrouter/ai-sdk-provider`.

How does Atlas leverage GLM-4.6's large output capacity?

Atlas leverages GLM-4.6's 131,072 max output capacity by computing a unified diff for every file edit and surfacing it for approval before writing. This ensures that even extensive, multi-file code modifications generated by the agent can be fully presented and reviewed.

Can I easily switch between GLM-4.6 and other models in Atlas?

Yes, Atlas is designed to let you switch the active model and provider on the fly with favorites and recents. This flexibility allows developers to easily compare GLM-4.6 with other models, such as GLM-4.7, to find the best fit for their specific tasks.

Atlas with GLM-4.6 in 2026

GLM-4.6 from Z.ai is a compelling open-weights model for Atlas in 2026, offering a substantial 200K token (204,800) context window and a maximum output of 131,072 tokens. Priced at $0.60 per Mtok input and $2.20 per Mtok output, it provides a cost-effective solution for developers tackling large codebases and intricate agentic workflows within Atlas.

Why choose GLM-4.6 for Atlas in 2026?

GLM-4.6 stands out in 2026 as a robust open-weights model for Atlas, offering a 200K token (204,800) context window and a significant 131,072 token maximum output. This expansion from GLM-4.5's 128K window and 98,304 output cap makes it highly effective for large codebases and multi-file diffs, solidifying its position as a default choice for many teams.

GLM-4.6, with its 357B total parameters and 32B active per forward pass, provides substantial reasoning capabilities for Atlas, the terminal-native AI coding agent. Its expanded context window of 204,800 tokens is particularly beneficial for Atlas's advanced features, such as Axis, the hybrid semantic and keyword code search, which can process extensive codebases. When Atlas indexes code by AST declarations using tree-sitter, not blind line windows, a larger context window allows for a more comprehensive understanding of the project structure and dependencies. The model's 131,072 max output, a 33 percent lift over GLM-4.5's 98,304, is crucial when an Atlas agent emits a large multi-file diff in one turn, ensuring that complex changes can be presented for approval without truncation. Furthermore, GLM-4.6 is an open-weights model, available at `zai-org/GLM-4.6` on HuggingFace, which means the model you benchmark is the model you can self-host, offering flexibility and control over your development environment. This makes it a strong candidate for teams prioritizing transparency and the ability to run models locally, potentially using Atlas's capability to build its code index with local Ollama embeddings, keeping code off third-party servers.

GLM-4.6 pricing and context window for Atlas

GLM-4.6 provides a generous 200K token (204,800) context window for Atlas users, maintaining an attractive price point of $0.60 per Mtok input and $2.20 per Mtok output. This cost-efficiency, combined with its large window, supports extensive code analysis and generation, making it a practical choice for developers in 2026 managing significant projects.

The pricing structure for GLM-4.6 remains consistent with its predecessor, GLM-4.5, at $0.60 per Mtok input and $2.20 per Mtok output, despite a significant expansion of its context window. This means Atlas users benefit from a 204,800 token context window without an increase in cost, a key factor for budget-conscious development teams. A larger context window directly enhances Atlas's ability to perform complex tasks, such as drafting a plan in a read-only plan agent before switching to a build agent, as it can hold more relevant code, documentation, and conversation history. Atlas's ability to read git branches, status, and diffs, and to stage and create commits on your behalf, is greatly augmented by a model that can process vast amounts of contextual information. The model's capacity to handle large inputs and outputs efficiently supports Atlas's workflow of computing a unified diff for every file edit and surfacing it for approval before writing, ensuring that even extensive code modifications are handled comprehensively. This combination of a large context window and competitive pricing positions GLM-4.6 as a high-value option for driving Atlas's AI coding agent capabilities.

Tradeoffs and when to consider other models with Atlas

While GLM-4.6 offers compelling features for Atlas, developers in 2026 should note its successor, GLM-4.7, provides the same 204,800 context window and pricing but with better agentic scores. Additionally, the Z.ai API is China-hosted, which might necessitate routing through OpenRouter or another gateway if US data residency is a hard requirement for your Atlas operations.

Despite its strengths, GLM-4.6 has specific tradeoffs that Atlas users should consider. The primary concern is that GLM-4.7, a newer model within the same family, ships with the identical 204,800 context window and the same $0.60 per Mtok input and $2.20 per Mtok output pricing, but with documented better agentic scores. This means that for optimal agent performance within Atlas, GLM-4.7 might offer a more refined experience, especially for complex multi-step tasks where agentic capabilities are paramount. Atlas's design, which lets you switch the active model and provider on the fly with favorites and recents, makes it straightforward to experiment with GLM-4.7 to assess its performance for your specific use cases. Another important consideration is the hosting location of the Z.ai API, which is China-hosted. For organizations with strict data residency requirements, particularly those needing US hosting, this could be a significant factor. In such scenarios, Atlas can be configured to route GLM-4.6 through OpenRouter or another gateway. Atlas loads OpenRouter via `@openrouter/ai-sdk-provider`, and the model ID there is `z-ai/glm-4.6`, providing a viable workaround for residency concerns. This flexibility ensures that Atlas users can still leverage GLM-4.6's capabilities while adhering to their compliance needs.

Setup

01Export your ZHIPU_API_KEY environment variable or authenticate with `atlas login`.
02Run `atlas models zai` to confirm that `glm-4.6` is available.
03Pin `"model": "zai/glm-4.6"` in your `atlas.json` configuration file to set GLM-4.6 as the default model.
04If US hosting is required, add OpenRouter as a provider. Atlas loads it via `@openrouter/ai-sdk-provider`.
05When using OpenRouter for US hosting, specify the model ID as `z-ai/glm-4.6` in your Atlas configuration.

Frequently asked questions

What is the context window for GLM-4.6 when used with Atlas?: GLM-4.6 provides a generous 200K token (204,800) context window for Atlas, enabling the agent to process extensive codebases and maintain long conversational histories for complex tasks.
How much does GLM-4.6 cost per token for Atlas users?: For Atlas users, GLM-4.6 is priced at $0.60 per Mtok input and $2.20 per Mtok output. This pricing remained unchanged despite the significant expansion of its context window from GLM-4.5.
Is GLM-4.6 an open-weights model, and can I self-host it for Atlas?: Yes, GLM-4.6 is an open-weights model, available at `zai-org/GLM-4.6` on HuggingFace. This allows Atlas users to self-host the model, ensuring that the benchmarked performance is consistent with your deployed version and enabling local code indexing with Ollama embeddings.
What are the key improvements of GLM-4.6 over GLM-4.5 for Atlas?: GLM-4.6 significantly improves upon GLM-4.5 by expanding its context window from 128K to 200K (204,800) tokens and lifting the maximum output cap to 131,072 tokens, a 33 percent increase. These enhancements are crucial for Atlas when handling large multi-file diffs and complex coding scenarios.
Are there any data residency concerns when using GLM-4.6 with Atlas?: Yes, the Z.ai API for GLM-4.6 is China-hosted. If US data residency is a hard requirement for your Atlas operations, you can route the model through OpenRouter or another gateway, which Atlas supports via `@openrouter/ai-sdk-provider`.
How does Atlas leverage GLM-4.6's large output capacity?: Atlas leverages GLM-4.6's 131,072 max output capacity by computing a unified diff for every file edit and surfacing it for approval before writing. This ensures that even extensive, multi-file code modifications generated by the agent can be fully presented and reviewed.
Can I easily switch between GLM-4.6 and other models in Atlas?: Yes, Atlas is designed to let you switch the active model and provider on the fly with favorites and recents. This flexibility allows developers to easily compare GLM-4.6 with other models, such as GLM-4.7, to find the best fit for their specific tasks.

Try SeaShell in your terminal

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

Install SeaShell

Related guides

Atlas for Node.js in 2026

Node.js developers in 2026 can adopt Atlas, the terminal-native AI coding agent, for secure, efficient development. Leverage local embeddings, AST indexing, and robust safety features.

Atlas for PyTorch: Terminal-Native AI Coding for nn.Module, Devices, and Autograd in 2026

Atlas is a terminal-native AI coding agent for PyTorch in 2026, where device placement, autograd, and DataLoader worker counts cause most bugs and most slowness.

Diagnose a Hanging or Long-Running Command with Atlas in 2026

How to diagnose a hanging command with Atlas in 2026: the bash tool races every command against a timeout and tells you whether it is slow or blocked on input.

Atlas for Expo: Terminal-Native AI Coding for expo-router and Config Plugins in 2026

Atlas is a terminal-native AI coding agent for Expo apps in 2026, covering expo-router file routes, config plugins, and EAS build profiles with diff-first review.

Atlas for Groovy: A Terminal-Native AI Coding Agent for Gradle, Spock, and Jenkins in 2026

Atlas is a terminal-native AI coding agent for Groovy in 2026. It reads build.gradle closures and Jenkinsfiles, writes Spock specs, runs ./gradlew test, and applies Spotless.

Atlas for OCaml: A Terminal-Native AI Coding Agent for dune and opam Projects in 2026

Atlas is a terminal-native AI coding agent for OCaml in 2026. It reads dune stanzas and .mli signatures, runs dune runtest behind a prompt, and finishes with ocamlformat.

Atlas vs Qodo: Choosing Your AI Coding Agent in 2026

Compare Atlas, the terminal-native AI coding agent, with Qodo 2.0, the multi-agent PR reviewer, for developers in 2026. Evaluate features, pricing, and workflow.

Atlas for Astro: Islands, Content Collections, and Zero JS by Default in 2026

Atlas is a terminal-native AI coding agent for Astro in 2026. It reads astro.config.mjs, src/pages, and content collection schemas, drops needless client:load directives, and runs astro check.

Browse this resource hub