What is the context window size for GLM-4.5-Air with Atlas?

GLM-4.5-Air provides a substantial 128K tokens (131,072) context window when used with Atlas. This allows the agent to process and understand large sections of code and project context effectively for various development tasks.

How much does GLM-4.5-Air cost per token?

GLM-4.5-Air is priced at $0.20 per Mtok for input tokens and $1.10 per Mtok for output tokens. This makes it a very economical choice for driving Atlas, especially for frequent interactions and code exploration.

Can I self-host GLM-4.5-Air for use with Atlas?

Yes, GLM-4.5-Air is MIT licensed, and its 106B total / 12B active MoE architecture makes quantized self-hosting on a single high-VRAM node genuinely plausible. You can pull the `zai-org/GLM-4.5-Air` weights and register Ollama as a custom provider in Atlas, specifying the `context: 131072` and `output: 98304` limits.

What are the main tradeoffs of using GLM-4.5-Air compared to other models?

The main tradeoff for GLM-4.5-Air is its performance on hard multi-step debugging tasks, where its 12B active parameters may drop steps that models with 32B active parameters, like GLM-4.5, would retain. Additionally, GLM-4.7-FlashX now offers a larger 200,000 token context and lower pricing ($0.07 in / $0.40 out) in 2026.

Is GLM-4.5-Air suitable for complex coding tasks in Atlas?

GLM-4.5-Air is suitable for many routine and moderately complex coding tasks within Atlas, leveraging its 128K token context. However, for highly intricate, multi-step debugging or reasoning challenges, its 12B active parameters may lead to dropped steps, and a more powerful model might be preferred.

How does GLM-4.5-Air compare to GLM-4.5 in terms of cost and context?

GLM-4.5-Air offers the same 128K tokens (131,072) context window as GLM-4.5 but at a significantly lower input cost of $0.20 per Mtok, which is one-third of GLM-4.5's $0.60 per Mtok. The output cost for GLM-4.5-Air is $1.10 per Mtok, which is half the output cost of GLM-4.5. The main difference is GLM-4.5-Air's 12B active parameters versus GLM-4.5's 32B, impacting performance on complex reasoning.

Can Atlas switch between GLM-4.5-Air and other models?

Yes, Atlas is designed to let you switch the active model and provider on the fly. You can easily configure favorites and recents to transition between GLM-4.5-Air for cost-effective daily tasks and other models for specific, more demanding scenarios.

Atlas with GLM-4.5-Air in 2026

In 2026, GLM-4.5-Air stands out as a highly cost-effective model for driving Atlas, the terminal-native AI coding agent. It provides a substantial 128K token (131,072) context window at an input cost of just $0.20 per Mtok and an output cost of $1.10 per Mtok, making it ideal for routine coding tasks, code search, and initial planning within Atlas's agentic workflows. Its open-weights nature also enables plausible self-hosting.

What is GLM-4.5-Air best for with Atlas?

GLM-4.5-Air excels as a cost-efficient workhorse for Atlas in 2026, particularly for tasks that benefit from its generous 128K token context window without requiring the deepest reasoning. Its $0.20 per Mtok input price makes it an excellent choice for frequent interactions and code exploration within the agent.

GLM-4.5-Air is optimally suited for a range of common development tasks within Atlas. Its 128K token (131,072) context window allows Atlas to process large codebases effectively, supporting features like Axis, the hybrid semantic and keyword code search, and providing ample context for drafting plans in the read-only plan agent. Developers can leverage GLM-4.5-Air for tasks such as understanding code, generating initial drafts, reviewing unified diffs for file edits, and managing git operations like staging and creating commits. The model's 12B active parameters are sufficient for many routine coding challenges, offering a balance of capability and cost-efficiency. Its open-weights nature and MIT license also mean that the identical weights can be run locally, ensuring consistent performance whether hosted or self-hosted.

GLM-4.5-Air Cost and Context Window Tradeoffs

GLM-4.5-Air offers a compelling cost-to-context ratio in 2026, featuring a 128K token (131,072) context window for $0.20 per Mtok input and $1.10 per Mtok output. This pricing is significantly more economical than its larger sibling, GLM-4.5, which costs $0.60 per Mtok input.

The primary strength of GLM-4.5-Air lies in its aggressive pricing and substantial context window. At $0.20 per Mtok for input and $1.10 per Mtok for output, it provides a cost-effective solution for developers using Atlas. This input cost is one-third of GLM-4.5's $0.60 per Mtok, while maintaining the same impressive 128K tokens (131,072) context window and a 98,304 token output cap. This makes it an attractive option for scenarios where high throughput and large context are needed without incurring premium costs. However, the tradeoff is its performance on highly complex, multi-step debugging tasks. With 12B active parameters compared to GLM-4.5's 32B, GLM-4.5-Air may drop steps that its larger counterpart would retain, requiring more user intervention or a switch to a more capable model for intricate problems. Despite this, its MIT license is a significant advantage, allowing users to self-host the identical weights without a crippled hosted-only SKU, offering flexibility and control over data privacy, especially when building Atlas's code index with local Ollama embeddings.

When to choose a different model for Atlas

While GLM-4.5-Air is a strong contender for many Atlas workflows in 2026, its 12B active parameters mean it's not always the optimal choice for every task. For hard multi-step debugging, where it may drop critical steps, a more robust model is recommended.

Developers should consider switching from GLM-4.5-Air when facing particularly challenging, multi-step debugging scenarios. The model's 12B active parameters, while efficient for many tasks, can lead to dropped steps in complex reasoning chains compared to models with larger active parameter counts, such as GLM-4.5 with its 32B active parameters. Furthermore, the competitive landscape has evolved; GLM-4.7-FlashX is now available at an even lower cost of $0.07 per Mtok input and $0.40 per Mtok output, and it boasts a larger 200,000 token context window. This makes GLM-4.5-Air a harder sell for pure cost-efficiency and maximum context, especially if the slight performance difference on complex tasks is not a primary concern. Atlas's ability to let you switch the active model and provider on the fly with favorites and recents makes it easy to leverage GLM-4.5-Air for daily tasks and then direct transition to a more powerful or even cheaper model when specific needs arise.

Self-Hosting GLM-4.5-Air with Atlas

One of GLM-4.5-Air's unique advantages in 2026 is the genuine plausibility of self-hosting its 106B total / 12B active MoE architecture on a single high-VRAM node. This is a significant benefit, especially for developers prioritizing data privacy and local execution within Atlas.

The open-weights nature and relatively compact active parameter count of GLM-4.5-Air make it a viable candidate for self-hosting, a capability not often seen with models of this scale. Unlike the much larger 355B GLM-4.5, GLM-4.5-Air's 106B total parameters with only 12B active can be quantized and run on a single high-VRAM node. This is particularly beneficial for Atlas users who wish to keep their code entirely off third-party servers, leveraging Atlas's ability to build its code index with local Ollama embeddings. By pulling the MIT-licensed `zai-org/GLM-4.5-Air` weights and registering Ollama as a custom provider in Atlas with the correct context and output limits (`"limit": { "context": 131072, "output": 98304 }`), developers gain full control over their model deployment. This setup ensures that sensitive code remains within the local environment, aligning with Atlas's permission-gated tool calls and unified diff approvals for every file edit, enhancing security and compliance.

Setup

01Export your ZHIPU_API_KEY environment variable or run `atlas login` and choose Z.ai as your provider.
02Run `atlas models zai` to list available Z.ai models and locate `glm-4.5-air`.
03Configure Atlas to use GLM-4.5-Air as a cost-effective option by adding `"small_model": "zai/glm-4.5-air"` to your `atlas.json` configuration file.
04For local self-hosting, pull the MIT-licensed `zai-org/GLM-4.5-Air` weights to your local machine.
05Register Ollama as a custom provider in your Atlas configuration for local use, specifying the model's limits: `"limit": { "context": 131072, "output": 98304 }`.

Frequently asked questions

What is the context window size for GLM-4.5-Air with Atlas?: GLM-4.5-Air provides a substantial 128K tokens (131,072) context window when used with Atlas. This allows the agent to process and understand large sections of code and project context effectively for various development tasks.
How much does GLM-4.5-Air cost per token?: GLM-4.5-Air is priced at $0.20 per Mtok for input tokens and $1.10 per Mtok for output tokens. This makes it a very economical choice for driving Atlas, especially for frequent interactions and code exploration.
Can I self-host GLM-4.5-Air for use with Atlas?: Yes, GLM-4.5-Air is MIT licensed, and its 106B total / 12B active MoE architecture makes quantized self-hosting on a single high-VRAM node genuinely plausible. You can pull the `zai-org/GLM-4.5-Air` weights and register Ollama as a custom provider in Atlas, specifying the `context: 131072` and `output: 98304` limits.
What are the main tradeoffs of using GLM-4.5-Air compared to other models?: The main tradeoff for GLM-4.5-Air is its performance on hard multi-step debugging tasks, where its 12B active parameters may drop steps that models with 32B active parameters, like GLM-4.5, would retain. Additionally, GLM-4.7-FlashX now offers a larger 200,000 token context and lower pricing ($0.07 in / $0.40 out) in 2026.
Is GLM-4.5-Air suitable for complex coding tasks in Atlas?: GLM-4.5-Air is suitable for many routine and moderately complex coding tasks within Atlas, leveraging its 128K token context. However, for highly intricate, multi-step debugging or reasoning challenges, its 12B active parameters may lead to dropped steps, and a more powerful model might be preferred.
How does GLM-4.5-Air compare to GLM-4.5 in terms of cost and context?: GLM-4.5-Air offers the same 128K tokens (131,072) context window as GLM-4.5 but at a significantly lower input cost of $0.20 per Mtok, which is one-third of GLM-4.5's $0.60 per Mtok. The output cost for GLM-4.5-Air is $1.10 per Mtok, which is half the output cost of GLM-4.5. The main difference is GLM-4.5-Air's 12B active parameters versus GLM-4.5's 32B, impacting performance on complex reasoning.
Can Atlas switch between GLM-4.5-Air and other models?: Yes, Atlas is designed to let you switch the active model and provider on the fly. You can easily configure favorites and recents to transition between GLM-4.5-Air for cost-effective daily tasks and other models for specific, more demanding scenarios.

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