What is the context window size for Qwen3.5 397B-A17B in Atlas?

Qwen3.5 397B-A17B provides a substantial 256K tokens (262,144) context window for Atlas. This allows the model to process and understand extensive codebases and complex project requirements, facilitating comprehensive planning and execution of tasks.

How much does it cost to use Qwen3.5 397B-A17B with Atlas?

The pricing for Qwen3.5 397B-A17B in Atlas is $0.60 per Mtok for input and $3.60 per Mtok for output. While the input cost is highly competitive for a frontier model, developers should monitor output token usage, especially in agentic loops that generate frequent diffs.

Is Qwen3.5 397B-A17B suitable for repo-wide code changes in Atlas?

Yes, Qwen3.5 397B-A17B is well-suited for repo-wide code changes. Its 262,144 token context window and 65,536 tokens of output are sufficient for Atlas to plan and then emit a comprehensive, repo-wide diff, leveraging its deep reasoning capabilities for complex modifications.

How does Qwen3.5 397B-A17B compare to other Qwen models for coding tasks?

Qwen3.5 397B-A17B offers the deepest reasoning in the Qwen3.5 family with 17B active parameters. However, for pure coding agentics, Qwen3-Coder 480B-A35B has 35B active parameters, which might offer an advantage in specific coding-focused scenarios. Atlas allows switching models to best fit the task.

What are the setup steps to use Qwen3.5 397B-A17B with Atlas?

To set up Qwen3.5 397B-A17B, you need to set your DASHSCOPE_API_KEY or store it with `atlas login` under the Alibaba provider. Confirm `qwen3.5-397b-a17b` resolves with `atlas models alibaba`, then pin it in atlas.json with `"model": "alibaba/qwen3.5-397b-a17b"`. It is recommended to run the plan agent first and keep bash on ask for initial sessions.

Does Atlas use Qwen3.5 397B-A17B for planning before making changes?

Yes, Atlas leverages Qwen3.5 397B-A17B's deep reasoning to draft a plan in a read-only plan agent. This plan is then presented for approval before Atlas switches to a build agent to execute the changes. This ensures thoughtful execution and allows developers to review the proposed unified diff.

Can Atlas keep my code private when using Qwen3.5 397B-A17B?

Atlas can build its code index with local Ollama embeddings, which helps keep your code off third-party servers. This feature, combined with Atlas's permission-gated tool calls, provides a robust framework for managing code privacy even when interacting with external models like Qwen3.5 397B-A17B.

Atlas with Qwen3.5 397B-A17B in 2026

Qwen3.5 397B-A17B is Alibaba's flagship MoE model, offering deep reasoning capabilities within Atlas for complex, repo-wide coding tasks. In 2026, developers can leverage its expansive 262,144 token context window for comprehensive planning, all while benefiting from a competitive $0.60 per Mtok input price, making it a strong contender for strategic agentic workflows.

Why choose Qwen3.5 397B-A17B for Atlas?

In 2026, Qwen3.5 397B-A17B stands out as Alibaba's top open-architecture reasoning tier, offering 397B total parameters with 17B active per token. This model provides Atlas with deep reasoning capabilities and a vast 262,144 token context window, all at an input price of $0.60 per Mtok.

Qwen3.5 397B-A17B is the largest Qwen3.5 tier, delivering the deepest reasoning in its family, making it ideal for complex problem-solving within Atlas. Its 262,144 token context window, combined with 65,536 tokens of output, is sufficient for Atlas to plan and then emit a repo-wide diff, handling extensive codebases. The model's input pricing of $0.60 per Mtok is an order of magnitude below comparable Western flagship models, offering frontier-scale reasoning at a highly competitive rate. Atlas enhances this capability by searching code with Axis, the hybrid semantic and keyword retrieval fused by reciprocal rank fusion, and indexing code by AST declarations using tree-sitter, not blind line windows. This synergy allows for precise and context-aware code modifications.

Understanding Qwen3.5 397B-A17B's Cost and Context Tradeoffs

While Qwen3.5 397B-A17B offers an impressive 262,144 token context window for Atlas, its output pricing of $3.60 per Mtok can accumulate rapidly. This model provides frontier-scale reasoning at a competitive input cost of $0.60 per Mtok, but developers must consider the implications of frequent diff generation.

The primary tradeoff for Qwen3.5 397B-A17B within Atlas is its output pricing. At $3.60 per Mtok output, costs can add up quickly in an agentic loop that generates diffs on every turn, especially for highly iterative development workflows. While the 262,144 token context window is a significant advantage for understanding large codebases and planning extensive changes, developers should be mindful of the output volume. Atlas computes a unified diff for every file edit and surfaces it for approval before writing, which helps manage output, but the per-token cost remains a factor. For scenarios requiring extensive output generation, careful planning and review are essential to optimize costs.

When to Consider an Alternative to Qwen3.5 397B-A17B

For pure coding agentics, Qwen3.5 397B-A17B's 17B active parameters are fewer than Qwen3-Coder 480B-A35B's 35B, suggesting the Coder line might offer an edge in specific scenarios. Additionally, the $3.60 per Mtok output cost can be a significant factor for highly iterative tasks within Atlas.

Developers might consider an alternative to Qwen3.5 397B-A17B if their primary focus is on pure coding agentics where a higher number of active parameters is beneficial. For instance, Qwen3-Coder 480B-A35B boasts 35B active parameters, which could potentially outperform Qwen3.5 397B-A17B's 17B active parameters on tasks demanding specialized coding proficiency. Furthermore, if the development workflow involves a high volume of small, iterative changes that frequently generate output, the $3.60 per Mtok output cost of Qwen3.5 397B-A17B could become prohibitive. Atlas allows you to switch the active model and provider on the fly with favorites and recents, providing flexibility to choose the most cost-effective or performant model for specific tasks.

Leveraging Qwen3.5 397B-A17B's Deep Reasoning in Atlas

Qwen3.5 397B-A17B, with its 397B total parameters and 17B active per token, provides Atlas with its deepest reasoning capabilities within the Qwen3.5 family. This allows Atlas to draft comprehensive plans in a read-only plan agent, ensuring thoughtful execution before switching to a build agent.

The deep reasoning of Qwen3.5 397B-A17B is particularly effective when Atlas drafts a plan in a read-only plan agent, asking for approval before switching to a build agent. This structured approach ensures that complex changes are thoroughly considered and approved, leveraging the model's ability to process a 262,144 token context window for a holistic understanding of the codebase. Every Atlas tool call is permission-gated against allow, ask, and deny rules before it runs, providing an additional layer of control. Atlas can also build its code index with local Ollama embeddings, keeping sensitive code off third-party servers, which is a crucial privacy feature when working with powerful frontier models like Qwen3.5 397B-A17B.

Setup

01Set DASHSCOPE_API_KEY, or store the key with `atlas login` under the Alibaba provider.
02Confirm with `atlas models alibaba` that `qwen3.5-397b-a17b` resolves.
03Pin it: `"model": "alibaba/qwen3.5-397b-a17b"` in atlas.json.
04Run the plan agent first, review the unified diff, and keep bash on ask rather than allow for the first few sessions.

Frequently asked questions

What is the context window size for Qwen3.5 397B-A17B in Atlas?: Qwen3.5 397B-A17B provides a substantial 256K tokens (262,144) context window for Atlas. This allows the model to process and understand extensive codebases and complex project requirements, facilitating comprehensive planning and execution of tasks.
How much does it cost to use Qwen3.5 397B-A17B with Atlas?: The pricing for Qwen3.5 397B-A17B in Atlas is $0.60 per Mtok for input and $3.60 per Mtok for output. While the input cost is highly competitive for a frontier model, developers should monitor output token usage, especially in agentic loops that generate frequent diffs.
Is Qwen3.5 397B-A17B suitable for repo-wide code changes in Atlas?: Yes, Qwen3.5 397B-A17B is well-suited for repo-wide code changes. Its 262,144 token context window and 65,536 tokens of output are sufficient for Atlas to plan and then emit a comprehensive, repo-wide diff, leveraging its deep reasoning capabilities for complex modifications.
How does Qwen3.5 397B-A17B compare to other Qwen models for coding tasks?: Qwen3.5 397B-A17B offers the deepest reasoning in the Qwen3.5 family with 17B active parameters. However, for pure coding agentics, Qwen3-Coder 480B-A35B has 35B active parameters, which might offer an advantage in specific coding-focused scenarios. Atlas allows switching models to best fit the task.
What are the setup steps to use Qwen3.5 397B-A17B with Atlas?: To set up Qwen3.5 397B-A17B, you need to set your DASHSCOPE_API_KEY or store it with `atlas login` under the Alibaba provider. Confirm `qwen3.5-397b-a17b` resolves with `atlas models alibaba`, then pin it in atlas.json with `"model": "alibaba/qwen3.5-397b-a17b"`. It is recommended to run the plan agent first and keep bash on ask for initial sessions.
Does Atlas use Qwen3.5 397B-A17B for planning before making changes?: Yes, Atlas leverages Qwen3.5 397B-A17B's deep reasoning to draft a plan in a read-only plan agent. This plan is then presented for approval before Atlas switches to a build agent to execute the changes. This ensures thoughtful execution and allows developers to review the proposed unified diff.
Can Atlas keep my code private when using Qwen3.5 397B-A17B?: Atlas can build its code index with local Ollama embeddings, which helps keep your code off third-party servers. This feature, combined with Atlas's permission-gated tool calls, provides a robust framework for managing code privacy even when interacting with external models like Qwen3.5 397B-A17B.

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