# Atlas with GLM-4.5-Flash in 2026 > GLM-4.5-Flash provides a 128K token context window at a free price point for Atlas users, making it a compelling option. GLM-4.5-Flash is Z.ai's zero-cost reasoning model, offering a substantial 128K token context window for Atlas users in 2026. It is specifically designed to power Atlas's background operations, such as chat titles and subagent chatter, without incurring any direct cost, though its throughput is rate limited. ## Key takeaways - GLM-4.5-Flash is genuinely free ($0.00) for both input and output on Z.ai's listed pricing. - It offers a substantial 128K tokens (131,072) context window and a 98,304 token output cap. - The model is reasoning enabled, supporting Atlas's multi-step planning capabilities. - Its free tier status means throughput is rate limited, impacting Atlas's parallel subagents. - Best suited for Atlas's `"small_model"` slot to handle background traffic without cost. - GLM-4.7-Flash is a newer free alternative with a larger 200,000 token context window. ## What is GLM-4.5-Flash best for in Atlas? GLM-4.5-Flash excels as the zero-cost reasoning model for Atlas's background operations in 2026. Listed at $0.00 per Mtok for both input and output, it efficiently handles tasks like generating chat titles, summarizing conversations, and managing subagent chatter without direct expense. GLM-4.5-Flash is uniquely positioned to drive Atlas's non-interactive and background processes. Its free pricing, at $0.00 per Mtok for both input and output on Z.ai's listed pricing, means that Atlas's continuous background traffic, including internal chat titles, summaries, and the chatter between subagents, is genuinely free. This model retains the full 131,072 token context window and a 98,304 token output ceiling of the paid GLM-4.5 models, ensuring it is not a 'toy' window. Furthermore, GLM-4.5-Flash is reasoning enabled, allowing it to support Atlas's ability to draft a plan in a read-only plan agent and handle multi-step plans, rather than being limited to single-shot completions. This makes it an excellent choice for the `small_model` slot in your Atlas configuration, providing intelligent processing where throughput is less critical than cost. ## What are the cost and context tradeoffs of GLM-4.5-Flash? GLM-4.5-Flash offers a compelling $0.00 cost per Mtok for both input and output, coupled with a generous 128K token context window. However, this free tier comes with a significant tradeoff: throughput is rate limited, which can impact Atlas's parallel subagents. The primary advantage of GLM-4.5-Flash is its cost: it is entirely free, listed at $0.00 per Mtok for both input and output on Z.ai's pricing. This makes it an unparalleled option for cost-conscious developers using Atlas. It also maintains a substantial 128K tokens (131,072) context window, along with a 98,304 token output cap, ensuring it can process large codebases or extensive conversational histories within Atlas. The key tradeoff, however, is its rate-limited throughput. Because it is a free tier offering, Atlas's architecture, which fans out work to subagents that can run in parallel background sessions, will hit these rate limits much faster than with a paid model. This means while individual background tasks might be free, the cumulative demand from multiple parallel subagents could lead to delays or stalls if GLM-4.5-Flash is used for high-throughput interactive operations. ## When should I choose a different model over GLM-4.5-Flash for Atlas? While GLM-4.5-Flash is a zero-cost option with a 128K token context, its rate limits mean it is not suitable for all Atlas operations. In 2026, if your interactive loop requires consistent, high-throughput performance, a paid model is essential for your primary Atlas configuration. You should consider a different model for Atlas when consistent, uninterrupted performance is paramount, especially for your interactive development loop. GLM-4.5-Flash's free status comes with rate limits that can cause Atlas's interactive loop to stall, particularly when Atlas is performing complex tasks or fanning out work to multiple subagents simultaneously. For this reason, it is strongly recommended to keep a paid model configured in Atlas's main `"model"` slot to ensure your interactive experience remains fluid and responsive. Furthermore, if you are not specifically tied to the GLM-4.5-Flash checkpoint, Z.ai offers GLM-4.7-Flash as a newer free option. GLM-4.7-Flash boasts an even larger 200,000 token context window, which might be a better fit if maximum context is your priority and you are still seeking a free tier model. ## Setup 1. Sign up for an account at Z.ai to gain access to their model offerings. 2. Authenticate Atlas with Z.ai by either exporting your `ZHIPU_API_KEY` environment variable or running `atlas login` and selecting Z.ai as your provider. 3. Verify that `glm-4.5-flash` is available by running the command `atlas models zai` in your terminal. 4. Configure Atlas to use GLM-4.5-Flash as the free background slot by adding `"small_model": "zai/glm-4.5-flash"` to your `atlas.json` configuration file. 5. To prevent interactive loop stalls when GLM-4.5-Flash hits its rate limits, ensure you keep a paid model configured in the main `"model"` slot of your `atlas.json`. ## FAQ ### Is GLM-4.5-Flash truly free for Atlas users? Yes, GLM-4.5-Flash is listed at $0.00 per Mtok for both input and output on Z.ai's pricing, making it a genuinely free option for Atlas's background traffic and subagent chatter. ### What is the context window size for GLM-4.5-Flash in Atlas? GLM-4.5-Flash provides a full 128K tokens (131,072) context window, matching the capacity of the paid GLM-4.5 models, ensuring ample space for code and conversation history. ### Can GLM-4.5-Flash handle complex tasks in Atlas? Yes, GLM-4.5-Flash is reasoning enabled, allowing it to support Atlas in drafting multi-step plans and executing complex operations, rather than being limited to simple completions. ### What are the limitations of using GLM-4.5-Flash with Atlas? The primary limitation is its rate-limited throughput due to its free tier status. This can cause Atlas's parallel subagents to hit usage limits faster, potentially stalling interactive workflows if not managed with a separate paid model. ### How does GLM-4.5-Flash compare to GLM-4.7-Flash for Atlas? GLM-4.7-Flash is a newer free option from Z.ai that offers a larger 200,000 token context window. GLM-4.5-Flash is primarily recommended if you specifically require this particular checkpoint or its established performance characteristics. ### How do I configure Atlas to use GLM-4.5-Flash? After signing up at Z.ai and setting your `ZHIPU_API_KEY`, you configure Atlas by adding `"small_model": "zai/glm-4.5-flash"` to your `atlas.json` file. It is crucial to also maintain a paid model in your main `"model"` slot to prevent interactive stalls. ### Why should I use GLM-4.5-Flash in the `small_model` slot? Using GLM-4.5-Flash in the `small_model` slot leverages its $0.00 cost for Atlas's background traffic, such as chat titles and subagent communications, while reserving your primary `"model"` slot for a paid, higher-throughput model to ensure a smooth interactive experience. --- Canonical HTML: https://seashell.sh/resources/models/glm-4-5-flash Source of truth: aeo_pages row `/resources/models/glm-4-5-flash` (segment: Models) (this file is generated from it, never hand-edited). Licence: SeaShell is proprietary with a free core. It is not open source and there is no public source repository.