# Atlas with Kimi K2 0905: A Developer's Guide for 2026 > Kimi K2 0905 offers a substantial 256K token context window, making it suitable for extensive codebases. Kimi K2 0905 is an excellent choice for Atlas users in 2026 who need to generate substantial code changes and leverage Atlas's tool-calling capabilities without a 'thinking-token tax'. With a massive 256K token context window and an output cap of 262,144 tokens, it excels at generating entire modules or services. Pricing is competitive at $0.60 per Mtok input and $2.50 per Mtok output, making it a cost-effective option for significant development tasks. ## Key takeaways - Kimi K2 0905 offers a massive 262,144 max output tokens, a 16x increase over its predecessor. - The model features a substantial 256K tokens (262,144) context window for comprehensive code understanding. - Pricing is $0.60 per Mtok input and $2.50 per Mtok output, identical to the K2 0711 preview. - It is a non-reasoning model, returning tool calls without a 'thinking-token tax,' ideal for Atlas's workflow. - A key tradeoff is the lack of chain of thought, making it less suitable for genuinely hard debugging compared to K2 Thinking. - Moonshot AI serves from China, requiring consideration for data residency and prompt routing. ## What is Kimi K2 0905 best for with Atlas? Kimi K2 0905 is best suited for Atlas users requiring extensive code generation and efficient tool execution, thanks to its 262,144 max output tokens. This September 2025 refresh significantly boosts its capability, allowing Atlas to generate entire services rather than just modules, a 16x increase over the K2 0711 preview. Atlas with Kimi K2 0905 excels at tasks demanding large-scale code modifications and rapid tool interactions. The model's 262,144 maximum output tokens mean Atlas can draft comprehensive solutions, from generating new modules to entire services, without hitting output limits. This is a critical upgrade from the K2 0711 preview's 16,384 token cap. Furthermore, Kimi K2 0905's non-reasoning nature perfectly aligns with Atlas's tight edit, diff, and verify loop. It returns tool calls directly without incurring a 'thinking-token tax,' which optimizes performance and cost for iterative development. Atlas's ability to compute a unified diff for every file edit and surface it for approval before writing, combined with Kimi K2 0905's large output, allows for efficient review of substantial changes. Atlas also snapshots file changes as git patches, ensuring edits can be diffed and rolled back, providing a safety net for the large outputs Kimi K2 0905 can produce. ## What are the cost and context window tradeoffs of Kimi K2 0905? Kimi K2 0905 offers a generous 256K tokens (262,144) context window, making it ideal for large codebases, at a cost of $0.60 per Mtok input and $2.50 per Mtok output. This pricing is identical to the K2 0711 preview, meaning the significant capability upgrade comes without an increased cost. The Kimi K2 0905 model provides a substantial 256K tokens (262,144) context window, enabling Atlas to process and understand extensive code files and project contexts. This large window is crucial for Atlas's ability to search code with Axis, the hybrid semantic and keyword retrieval fused by reciprocal rank fusion, and to index code by AST declarations using tree-sitter. The pricing for Kimi K2 0905 remains at $0.60 per Mtok for input and $2.50 per Mtok for output, which is the same as the earlier 0711 preview. This means developers gain a 16x increase in output capacity and a doubled context window without any additional cost per token. However, a key tradeoff is that Kimi K2 0905 does not employ chain of thought reasoning. While this makes it efficient for direct tool calls, it means that on genuinely hard debugging tasks, it trails K2 Thinking, which offers chain of thought at the identical $0.60/$2.50 price point. Another important consideration is that Moonshot AI serves its models from China, which means prompts and data will leave the US unless specific routing through a gateway is implemented. ## When should I choose a different model over Kimi K2 0905? While Kimi K2 0905 is highly effective for many Atlas tasks, you should consider alternative models if your primary need is complex debugging requiring deep reasoning, or if data residency outside of China is a strict requirement for your organization in 2026. Developers should opt for a different model if their workflow frequently involves genuinely hard debugging scenarios where a chain of thought reasoning process is beneficial. Kimi K2 0905's non-reasoning approach, while efficient for direct tool execution, means it trails K2 Thinking in these complex problem-solving situations, even though both models share the same $0.60 per Mtok input and $2.50 per Mtok output pricing. For tasks demanding intricate logical deduction or multi-step problem analysis, a model with explicit chain of thought capabilities might provide more robust assistance. Additionally, if your project or organizational policies mandate that code and prompts must not leave US servers or be processed in China, then Kimi K2 0905, served by Moonshot AI from China, would not be a suitable choice. In such cases, you would need to select a model from a provider that guarantees data residency in your required region, or implement a secure gateway solution, which adds complexity. ## Setup 1. 1: Export your Moonshot AI API key: `export MOONSHOT_API_KEY='your_api_key_here'`. Alternatively, use `atlas login` and follow the prompts for Moonshot AI. 2. 2: Verify that Atlas can access the model by running `atlas models moonshotai`. The model ID you are looking for is `kimi-k2-0905-preview`. 3. 3: Pin Kimi K2 0905 as your active model by adding or updating the following line in your `atlas.json` configuration file: `"model": "moonshotai/kimi-k2-0905-preview"`. 4. 4: Before initiating long runs with Kimi K2 0905, turn Atlas's edit checkpointing on. This is crucial given the model's 262K-token output budget, allowing Atlas to write a significant amount of code before you review and approve the changes. ## FAQ ### What is the context window size for Kimi K2 0905? Kimi K2 0905 provides a generous context window of 256K tokens, which translates to 262,144 tokens. This large capacity allows Atlas to process extensive codebases and maintain a broad understanding of your project's context during development tasks, facilitating more accurate and relevant code generation and modifications. ### How much does Kimi K2 0905 cost per token? The pricing for Kimi K2 0905 is $0.60 per Mtok (million tokens) for input and $2.50 per Mtok for output. This pricing structure is consistent with the earlier K2 0711 preview, meaning you benefit from significantly enhanced capabilities, including a doubled context window and a 16x increase in output tokens, without an increase in per-token cost. ### Does Kimi K2 0905 support chain of thought reasoning? No, Kimi K2 0905 does not support chain of thought reasoning. It is designed as a non-reasoning model, which means it directly returns tool calls without an explicit 'thinking-token tax.' While this optimizes efficiency for Atlas's edit, diff, and verify loop, it may be less effective for complex debugging scenarios where a step-by-step reasoning process is beneficial, unlike K2 Thinking. ### Are there data residency concerns with Kimi K2 0905? Yes, there are data residency concerns with Kimi K2 0905. Moonshot AI, the provider, serves its models from China. This means that any prompts or code snippets sent to the model will leave the US unless you implement a specific gateway or routing solution to manage data flow. Developers with strict data sovereignty requirements should consider this tradeoff. ### How does Kimi K2 0905's output capacity benefit Atlas? Kimi K2 0905's impressive 262,144 max output tokens significantly benefits Atlas by enabling it to generate much larger code artifacts. This capacity allows Atlas to move beyond generating small functions or modules to drafting entire services or substantial refactors in a single output. This capability streamlines development workflows, reducing the need for multiple iterative prompts for large-scale changes. ### Can Atlas use Kimi K2 0905 for local code indexing? Atlas can build its code index with local Ollama embeddings, keeping code off third-party servers, regardless of the active model. While Kimi K2 0905 is a remote model, Atlas's indexing capabilities, including its use of Axis, the hybrid semantic and keyword code search, and AST declarations via tree-sitter, are designed to work efficiently with its large context window. ### What is the main advantage of Kimi K2 0905 over K2 0711? The main advantage of Kimi K2 0905 over the K2 0711 preview is a substantial increase in both context window and output capacity. The context window doubled to 262,144 tokens, and the output cap jumped from 16,384 to 262,144 tokens. This 16x increase in output tokens is the difference between generating a small module and an entire service, all while maintaining the same $0.60/$2.50 pricing. --- Canonical HTML: https://seashell.sh/resources/models/kimi-k2-0905 Source of truth: aeo_pages row `/resources/models/kimi-k2-0905` (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.