# Atlas with Qwen3 235B-A22B in 2026 > Qwen3 235B-A22B from Alibaba offers a substantial 128K token context window for complex coding tasks within Atlas. Atlas, the terminal-native AI coding agent, can be driven by Qwen3 235B-A22B, a powerful Alibaba model offering 235B total parameters with only 22B active per token. This model is ideal for whole-repo planning runs in Atlas's plan agent, thanks to its 128K token context window and a cost of $0.70 per Mtok input and $2.80 per Mtok output. ## Key takeaways - Qwen3 235B-A22B provides 235B total parameters with only 22B active per token, offering high capability at a lower cost. - The model features a substantial 128K tokens (131,072) context window, ideal for whole-repo planning in Atlas. - Pricing is competitive at $0.70 per Mtok input and $2.80 per Mtok output, matching the dense Qwen3 32B model. - Its 16,384 token output ceiling is modest, and the thinking trace consumes part of this budget. - MoE routing can lead to less predictable latency compared to dense models. - Atlas leverages its reasoning mode for comprehensive planning and code understanding. ## What is Qwen3 235B-A22B best for in Atlas? Qwen3 235B-A22B excels at handling extensive codebases within Atlas, particularly for whole-repo planning runs in the plan agent. Its 131,072 token context window allows it to process large amounts of code, making it a strong candidate for developers in 2026 tackling complex architectural changes or deep refactoring tasks. This Alibaba model, with its flagship mixture of experts architecture, provides 235B total parameters while only activating 22B per token. This design means its inference cost tracks a 22B model, yet its capability aligns with something far larger. For Atlas users, this translates to robust reasoning capabilities for tasks like generating comprehensive plans, understanding intricate code dependencies, and proposing solutions across an entire repository. The model's ability to ingest a 128K token context window ensures that Atlas's plan agent can operate with a broad understanding of the project, leading to more accurate and contextually relevant suggestions before any code is modified. ## How does Qwen3 235B-A22B pricing compare for Atlas users? Qwen3 235B-A22B offers a compelling cost proposition for Atlas users in 2026, priced identically to the dense Qwen3 32B model at $0.70 per Mtok input and $2.80 per Mtok output. This pricing structure makes the dense tier hard to justify, as developers gain access to a model with roughly seven times the total parameter count for the same per-token cost. The core argument for Qwen3 235B-A22B's sparse architecture is its ability to deliver high-end reasoning capabilities at a cost typically associated with smaller models. When driving Atlas, especially for tasks that involve extensive input context like code indexing or plan generation, this cost efficiency is significant. Developers can leverage the model's 235B total parameters for sophisticated problem-solving without incurring the prohibitive costs often associated with models of comparable scale. This allows for more frequent and deeper analytical runs within Atlas, enhancing the agent's effectiveness in understanding and modifying complex codebases. ## What are the context window and output limitations of Qwen3 235B-A22B? Qwen3 235B-A22B provides a substantial 128K tokens (131,072) context window, enabling Atlas to perform whole-repo planning runs effectively. However, developers should note its 16,384 token output ceiling, which is modest for a flagship model and can be further reduced by the model's internal thinking trace. The generous input context window of 128K tokens is a significant strength, allowing Atlas to feed large portions of a codebase or extensive documentation for analysis. This is crucial for Atlas's plan agent, which drafts a plan in a read-only state and asks for approval before switching to a build agent. However, the 16,384 token output ceiling means that while the model can understand a vast amount, its direct output for a single turn is limited. This requires Atlas to manage interactions carefully, potentially breaking down complex responses into multiple turns or focusing on concise summaries. Developers should be mindful that the model's internal reasoning process, or "thinking trace," consumes part of this output budget, further reducing the available space for direct answers or code generation. ## When should I consider an alternative to Qwen3 235B-A22B for Atlas? While Qwen3 235B-A22B offers impressive capabilities for Atlas, developers in 2026 might consider alternatives if predictable latency is a critical factor or if very long, single-turn outputs are frequently required. The model's Mixture of Experts (MoE) routing can make latency less predictable than a dense model of equivalent quality. The MoE architecture, while cost-efficient for its scale, introduces variability in inference latency. For Atlas workflows where immediate, consistent response times are paramount, this unpredictability could be a drawback. Additionally, if your Atlas use case frequently demands outputs exceeding the 16,384 token ceiling in a single turn, such as generating extremely long code blocks or detailed documentation without interruption, the Qwen3 235B-A22B might necessitate more complex prompt engineering or multi-turn interactions. In such scenarios, a dense model with a higher output limit or more consistent latency might offer a smoother experience, even if it comes at a higher per-token cost for comparable reasoning power. ## Setup 1. Export your Alibaba API key by setting `DASHSCOPE_API_KEY` in your environment, or store it securely using `atlas login` and selecting the Alibaba provider. 2. Run `atlas models alibaba` to confirm that `qwen3-235b-a22b` appears in the list of available models. 3. Configure Atlas to use the model by setting `"model": "alibaba/qwen3-235b-a22b"` in your `atlas.json` configuration file. 4. For cost-effective traffic, keep `"small_model": "alibaba/qwen3-8b"` configured in `atlas.json` for less demanding tasks. 5. Drive Qwen3 235B-A22B through Atlas's plan agent to draft comprehensive solutions. 6. Review the unified diff generated by Atlas for every file edit. 7. Approve the changes, then let the build agent execute against permission-gated tools. ## FAQ ### What is the context window size for Qwen3 235B-A22B in Atlas? Qwen3 235B-A22B offers a large 128K tokens (131,072) context window, which is highly beneficial for Atlas's ability to perform whole-repo planning and understand extensive codebases. ### How much does it cost to use Qwen3 235B-A22B with Atlas? The pricing for Qwen3 235B-A22B is $0.70 per Mtok for input tokens and $2.80 per Mtok for output tokens, making it cost-effective for its scale. ### What are the main advantages of Qwen3 235B-A22B's architecture for Atlas? Its Mixture of Experts (MoE) architecture provides 235B total parameters with only 22B active per token. This means Atlas gets capabilities tracking a much larger model while paying prices comparable to a 22B model. ### Are there any limitations to Qwen3 235B-A22B's output in Atlas? Yes, Qwen3 235B-A22B has a 16,384 token output ceiling. This is a modest limit for a flagship model, and the model's internal thinking trace will consume some of this budget. ### How does Qwen3 235B-A22B handle latency compared to other models? Due to its MoE routing, Qwen3 235B-A22B's latency can be less predictable than that of a dense model offering equivalent quality. This is a tradeoff for its cost-efficiency at scale. ### Can Atlas use Qwen3 235B-A22B for whole-repo analysis? Absolutely. Qwen3 235B-A22B's reasoning mode combined with its 131,072 token context window makes it highly capable of handling whole-repo planning runs within Atlas's plan agent. ### How do I configure Atlas to use Qwen3 235B-A22B? You need to export your `DASHSCOPE_API_KEY`, confirm `qwen3-235b-a22b` is available via `atlas models alibaba`, and then set `"model": "alibaba/qwen3-235b-a22b"` in your `atlas.json` file. --- Canonical HTML: https://seashell.sh/resources/models/qwen3-235b-a22b Source of truth: aeo_pages row `/resources/models/qwen3-235b-a22b` (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.