# Atlas with DeepSeek R1 (0528) in 2026 > DeepSeek R1 (0528) offers a substantial 160K token context window, making it a classic choice for open reasoning models within Atlas. DeepSeek R1 (0528) excels as Atlas's plan agent, providing a fully visible, debuggable reasoning trace for complex coding tasks in 2026. With a 160K token context window, it helps diagnose agent missteps, but its $0.50 per Mtok input and $2.15 per Mtok output pricing, coupled with its verbose nature, means it's best paired with a cheaper build model to manage costs effectively. ## Key takeaways - DeepSeek R1 (0528) provides a substantial 160K token context window via DeepInfra hosting. - Its output pricing is $2.15 per Mtok, making verbose agent loops potentially expensive. - DeepSeek R1 (0528) excels as Atlas's plan agent due to its explicit, debuggable reasoning trace. - The model is 671B total / 37B activated MoE, released under an MIT license. - Expect weaker tool-calling discipline compared to newer agentic models like V3.1 or K2 Thinking. - Pair DeepSeek R1 (0528) with a cheaper build model to optimize cost-efficiency. ## What is DeepSeek R1 (0528) best at inside Atlas? DeepSeek R1 (0528) shines as Atlas's plan agent, offering a unique advantage in 2026 by providing an explicit, readable reasoning trace. This 671B total parameter model allows developers to genuinely debug an Atlas agent when it takes a wrong turn mid-plan, making complex problem-solving more transparent. DeepSeek R1 (0528) is a 671B total / 37B activated MoE model, released under an MIT license, which significantly contributed to making open reasoning weights a real category. Within Atlas, this model's primary strength lies in its ability to emit an explicit, readable reasoning trace. This feature is invaluable for developers in 2026, as it allows for genuine debugging when an Atlas agent, particularly the read-only plan agent, takes a wrong turn mid-plan. Unlike models that provide only a hidden summary, DeepSeek R1 (0528) offers full transparency into its thought process, enabling users to understand and correct agent missteps. This makes it an excellent choice for complex problem-solving and for scenarios where understanding the agent's decision-making is critical. Furthermore, being hosted in the US on DeepInfra means you get the benefits of R1 without routing prompts through api.deepseek.com, ensuring direct access and potentially lower latency. ## What are the cost and context tradeoffs for DeepSeek R1 (0528) in Atlas? DeepSeek R1 (0528) provides a generous 160K token context window, hosted on DeepInfra, but its output pricing of $2.15 per Mtok can lead to rapid cost accumulation. This model is verbose by construction, meaning agent loops can become expensive quickly, especially compared to other options available in 2026. The 160K token context window offered by DeepSeek R1 (0528) through DeepInfra hosting provides ample space for complex coding tasks and extensive code analysis within Atlas. However, this model comes with significant cost considerations. Its pricing is $0.50 per Mtok for input and a notably higher $2.15 per Mtok for output. This output rate is over five times that of DeepSeek Chat. DeepSeek R1 (0528) is also verbose by construction, meaning it tends to generate longer responses to provide its detailed reasoning trace. This verbosity, combined with the $2.15 per Mtok output cost, means that agent loops, especially those involving multiple turns or extensive output generation, can become expensive very quickly. Developers must weigh the value of its debuggable reasoning against these higher operational costs when integrating DeepSeek R1 (0528) into their Atlas workflows. ## When should I choose a different model over DeepSeek R1 (0528) for Atlas? While DeepSeek R1 (0528) offers strong reasoning, its tool-calling discipline is weaker than models trained specifically for agentic use after its 0528 checkpoint, such as V3.1, V3.2, or K2 Thinking. In 2026, if your Atlas agent requires highly precise and consistent tool interactions over long runs, you should expect more malformed tool calls with R1. While DeepSeek R1 (0528) excels in providing transparent reasoning, its tool-calling discipline is weaker compared to models specifically trained for agentic use that emerged after its 0528 checkpoint, such as V3.1, V3.2, or K2 Thinking. For Atlas agents that rely heavily on precise and consistently formatted tool calls for tasks like code modification, file system interactions, or using Atlas's Axis, the hybrid semantic and keyword code search, this can lead to more malformed tool calls in long runs. This can introduce friction and require more manual intervention or retry logic. Therefore, if your primary need is highly reliable and disciplined tool execution for the build agent phase, a different model might be more suitable. DeepSeek R1 (0528)'s price-to-value ratio is best when it is used as Atlas's plan agent, generating a detailed plan, which is then handed off to a cheaper, potentially more tool-disciplined, build model for execution. This strategy helps mitigate the high output costs associated with R1's verbosity while still leveraging its strong reasoning capabilities. ## Setup 1. Export your DEEPINFRA_API_KEY environment variable or run `atlas login` and select DeepInfra as your provider. 2. Identify the model ID by running `atlas models deepinfra`; the correct ID is `deepseek-ai/DeepSeek-R1-0528`. 3. Configure Atlas by adding the entry `"model": "deepinfra/deepseek-ai/DeepSeek-R1-0528"` to your `atlas.json` file. 4. To manage costs effectively, set your `"small_model"` configuration in `atlas.json` to a cheaper alternative, preventing DeepSeek R1's output rate from being billed for chat titles. 5. For optimal price-to-value, designate DeepSeek R1 (0528) as Atlas's plan agent. 6. Hand the detailed plan generated by DeepSeek R1 (0528) to a more cost-effective build model for execution. ## FAQ ### What is the context window for DeepSeek R1 (0528) in Atlas? DeepSeek R1 (0528) offers a 160K token context window when hosted on DeepInfra for use with Atlas. ### How much does DeepSeek R1 (0528) cost to use with Atlas? DeepSeek R1 (0528) is priced at $0.50 per Mtok for input and $2.15 per Mtok for output when hosted by DeepInfra for Atlas. ### Why choose DeepSeek R1 (0528) for Atlas's plan agent? DeepSeek R1 (0528) is ideal for Atlas's plan agent because it emits an explicit, readable reasoning trace, making agent missteps genuinely debuggable during complex planning in 2026. ### What are the main drawbacks of DeepSeek R1 (0528) for Atlas? The primary drawbacks are its high output cost of $2.15 per Mtok, its verbose nature leading to expensive agent loops, and weaker tool-calling discipline compared to newer agent-specific models. ### Can DeepSeek R1 (0528) be used for both planning and building in Atlas? While possible, it is recommended to use DeepSeek R1 (0528) primarily as Atlas's plan agent and hand the resulting plan to a cheaper build model to mitigate the high $2.15 per Mtok output cost. ### Is DeepSeek R1 (0528) an open-source model? Yes, DeepSeek R1 (0528) is a 671B total / 37B activated MoE model released under an MIT license, which made open reasoning weights a real category. ### Where is DeepSeek R1 (0528) hosted for Atlas users? DeepSeek R1 (0528) is hosted in the US on DeepInfra, ensuring that Atlas users do not route prompts through api.deepseek.com. --- Canonical HTML: https://seashell.sh/resources/models/deepseek-r1-0528 Source of truth: aeo_pages row `/resources/models/deepseek-r1-0528` (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.