Qwen3.5 122B-A10B is an excellent choice for Atlas users in 2026 tackling significant code refactoring and large-scale module rewrites, thanks to its 256K token context window and enhanced reasoning. It costs $0.40 per Mtok input and $3.20 per Mtok output, offering a balance of capability and cost for demanding tasks.
What is Qwen3.5 122B-A10B best for with Atlas?
Qwen3.5 122B-A10B excels within Atlas for complex code refactoring and whole-module rewrites in 2026, leveraging its 262,144 token context window. This model's 10B active parameters provide roughly 3x the active capacity of its 35B-A3B sibling, enabling superior reasoning for multi-package changes.
Qwen3.5 122B-A10B, with its 10B active parameters out of 122B total, is specifically designed for reasoning-intensive tasks within Atlas. This capability pairs exceptionally well with Atlas's plan agent, which drafts a plan in a read-only state and asks for approval before switching to a build agent, especially for refactors that span several packages. The model's generous 256K tokens (262,144) context window, coupled with a 65,536 token output ceiling, is sized to handle whole-module rewrites in a single turn. Atlas further enhances this by computing a unified diff for every file edit and surfacing it for approval before writing, ensuring precise control over the model's extensive outputs. Atlas's ability to search code with Axis, the hybrid semantic and keyword retrieval fused by reciprocal rank fusion, and its indexing of code by AST declarations using tree-sitter, ensures that Qwen3.5 122B-A10B receives highly relevant and structured context, maximizing its 10B active parameters for accurate and effective code generation and modification.
What are the cost and context window tradeoffs for Qwen3.5 122B-A10B?
Qwen3.5 122B-A10B offers a substantial 256K token context window for Atlas users in 2026, priced at $0.40 per Mtok input and $3.20 per Mtok output. While its 10B active parameters provide 3x the capacity of the 35B-A3B tier, the output cost is a significant consideration.
The Qwen3.5 122B-A10B model provides a robust 262,144 token context window, which is a significant advantage for developers working on large codebases within Atlas. Its pricing is set at $0.40 per Mtok for input and $3.20 per Mtok for output. A key strength is its 10B active parameters, which offer roughly 3x the active capacity of the 35B-A3B tier for only 60 percent more input cost, making it a compelling option for tasks requiring enhanced reasoning. However, developers must consider the output cost: at $3.20 per Mtok, it is notably close to the 397B-A17B flagship's $3.60 per Mtok output. This proximity in output pricing can weaken the value argument for Qwen3.5 122B-A10B if the project frequently generates very long outputs, potentially making the flagship model a more cost-effective choice for maximum capability in such scenarios.
When should I choose a different model over Qwen3.5 122B-A10B for Atlas?
While Qwen3.5 122B-A10B offers robust reasoning and a 256K token context window for Atlas in 2026, developers should consider alternatives if cost efficiency for simple tasks or maximum reasoning power is paramount. Its $3.20 per Mtok output cost is a key factor, nearing the 397B-A17B flagship's $3.60.
Developers using Atlas should consider alternatives to Qwen3.5 122B-A10B in specific scenarios. For routine tasks that do not demand its 10B active parameters or 262,144 token context window, a more economical option like `alibaba/qwen-flash` (which can be pinned as `small_model` at $0.05 per Mtok input) would be more cost-effective for generating titles and summaries. Conversely, if the absolute highest reasoning capability is required for extremely complex problems, and budget permits, the 397B-A17B flagship model, despite its slightly higher $3.60 per Mtok output cost, might offer a better return on investment due to its superior capacity. Another crucial factor is that Qwen3.5 122B-A10B is not open-weights. This means there is no local Ollama fallback for this tier, which could be a deciding point for users who prioritize keeping code off third-party servers and prefer Atlas to build its code index with local Ollama embeddings.
Setup
- 01Run `atlas login`, choose Alibaba, and paste your DashScope key.
- 02List the family with `atlas models alibaba` and confirm `qwen3.5-122b-a10b`.
- 03Set `"model": "alibaba/qwen3.5-122b-a10b"` in your `atlas.json` configuration file.
- 04Pin `"small_model": "alibaba/qwen-flash"` at $0.05 per Mtok input so titles and summaries stay cheap.
Frequently asked questions
- What is the context window for Qwen3.5 122B-A10B in Atlas?
- Qwen3.5 122B-A10B offers a substantial 256K tokens (262,144) context window within Atlas, enabling extensive code analysis and generation.
- How much does Qwen3.5 122B-A10B cost to use with Atlas?
- Using Qwen3.5 122B-A10B with Atlas costs $0.40 per Mtok for input and $3.20 per Mtok for output.
- Is Qwen3.5 122B-A10B suitable for large code refactors in Atlas?
- Yes, Qwen3.5 122B-A10B is well-suited for large code refactors in Atlas, especially those spanning several packages, due to its reasoning capabilities and 262,144 token context window.
- Can I use Qwen3.5 122B-A10B with local Ollama embeddings in Atlas?
- No, Qwen3.5 122B-A10B is not open-weights, so it does not support local Ollama fallback for building Atlas's code index.
- How does Qwen3.5 122B-A10B compare to the 35B-A3B tier for Atlas?
- Qwen3.5 122B-A10B provides roughly 3x the active capacity with 10B active parameters compared to the 35B-A3B tier, for 60 percent more input cost, offering significantly better reasoning.
- What is the output token ceiling for Qwen3.5 122B-A10B in Atlas?
- Qwen3.5 122B-A10B has a 65,536 token output ceiling, designed to accommodate whole-module rewrites in a single turn within Atlas.
- How do I configure Atlas to use Qwen3.5 122B-A10B?
- Configure Atlas by running `atlas login` for Alibaba, confirming `qwen3.5-122b-a10b` with `atlas models alibaba`, and setting `"model": "alibaba/qwen3.5-122b-a10b"` in `atlas.json`.
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