# Atlas with Gemma 4 26B A4B in 2026 > Gemma 4 26B A4B offers a significant 262,144 token context window, making it a strong contender for extensive code analysis within Atlas. Atlas with Gemma 4 26B A4B provides developers in 2026 with a powerful, open-weight model featuring a substantial 256K token context window for complex coding tasks. Its Mixture-of-Experts architecture allows for efficient throughput, tracking a smaller 4B parameter model while offering the capacity of a larger 26B model. Pricing for Gemma 4 26B A4B is not publicly listed, requiring direct provider inquiry for hosted access or local deployment for cost control. ## Key takeaways - Gemma 4 26B A4B offers a 262,144 token context window, double the 128K window typical of many open-weight models. - Its Mixture-of-Experts (MoE) architecture provides 4B active parameters for fast throughput, despite 26B total parameters. - VRAM requirements are based on the full 26B parameter count, not just the active 4B, demanding higher hardware specifications. - Pricing for Gemma 4 26B A4B is not publicly listed, requiring direct inquiry for hosted access. - As an open-weight model, it supports fully local execution with Atlas and Ollama, keeping code off third-party servers. ## What is Gemma 4 26B A4B best for in Atlas? Gemma 4 26B A4B excels within Atlas for developers in 2026 who require deep code understanding and robust reasoning over large codebases, thanks to its 262,144 token context window. Its open-weight nature also allows for complete local execution, ensuring code never leaves your machine. Atlas leverages Gemma 4 26B A4B's reasoning capabilities and expansive context window for tasks demanding comprehensive code analysis. The model's ability to process 262,144 tokens allows Atlas to perform sophisticated code searches using Axis, its hybrid semantic and keyword retrieval fused by reciprocal rank fusion. This deep understanding supports Atlas in drafting detailed plans in its read-only plan agent, and in computing unified diffs for every file edit, which are surfaced for approval. For developers prioritizing data privacy, Gemma 4 26B A4B's open-weight status means it can be run entirely on local hardware with Atlas's local Ollama embeddings, keeping all code off third-party servers. ## What are the tradeoffs for Gemma 4 26B A4B in Atlas? When considering Gemma 4 26B A4B for Atlas in 2026, developers must weigh its substantial memory footprint against its efficient 4B active parameter throughput. While it performs like a smaller model, the full 26B parameter count dictates VRAM requirements, which are higher than a 4B dense model. The primary tradeoff for Gemma 4 26B A4B is its memory footprint. Despite its Mixture-of-Experts (MoE) architecture meaning only about 4B parameters are active per token, the model's total 26B parameters determine its VRAM needs. This means developers will require more robust local hardware than they might for a dense model with a similar active parameter count. Another significant consideration is pricing; Gemma 4 26B A4B's pricing is not publicly listed. For hosted access, this means costs will depend entirely on the specific provider Atlas routes through, potentially introducing variability or requiring direct negotiation rather than transparent per-token rates. ## When should I choose a different model over Gemma 4 26B A4B for Atlas? Developers in 2026 should consider alternative models for Atlas if their hardware cannot accommodate the significant VRAM demands of Gemma 4 26B A4B's 26B total parameters. Additionally, if a publicly listed, predictable per-token price is a critical factor, this model's 'Pricing not publicly listed' status may be a deterrent. If your local development machine lacks the substantial VRAM required to host a 26B parameter model, even one with efficient 4B active parameters like Gemma 4 26B A4B, a smaller dense model might be a more practical choice for local Atlas operations. Similarly, if your project's budget requires transparent and predictable per-token pricing for hosted model access, the 'Pricing not publicly listed' status of Gemma 4 26B A4B could make cost management challenging. In such cases, exploring other models with publicly available pricing or lower hardware requirements would be advisable to ensure smooth and cost-effective integration with Atlas. ## How does Gemma 4 26B A4B's context window benefit Atlas? Gemma 4 26B A4B provides Atlas with an expansive 262,144 token context window, which is double the 128K window typical of many open-weight models in 2026. This allows Atlas to process significantly larger codebases and more extensive diffs, enhancing its ability to understand and modify complex projects. The 262,144 token context window of Gemma 4 26B A4B is a significant advantage for Atlas. This large capacity enables Atlas to maintain a comprehensive understanding of entire codebases, facilitating its ability to index code by AST declarations using tree-sitter, rather than blind line windows. When Atlas reads git branches, status, and diffs, this extensive context ensures it has all necessary information to draft plans and propose edits. The ability to process such a vast amount of information at once means Atlas's plan agent can operate with a richer understanding of the project state, leading to more accurate and contextually relevant suggestions before switching to a build agent. ## Setup 1. For hosted access, export GEMINI_API_KEY and run `atlas models google` to see if gemma-4-26b-a4b-it resolves on your key. 2. For local use, run `ollama pull gemma3` style pulls only for tags you have confirmed exist on ollama.com, then check `atlas device` to see that Ollama and a GPU were detected. 3. Add an ollama block to atlas.json using `"npm": "@ai-sdk/openai-compatible"` with `baseURL http://localhost:11434/v1`. 4. Set the models map limit to `{ "context": 262144, "output": 32768 }` to match the real ceilings for Gemma 4 26B A4B. 5. Verify the entry with `atlas models ollama` before pinning it as `"model"` in your Atlas configuration. ## FAQ ### What is the context window size for Gemma 4 26B A4B in Atlas? Gemma 4 26B A4B provides Atlas with a 256K token context window, specifically 262,144 tokens, allowing for extensive code analysis and understanding across large projects. ### Is Gemma 4 26B A4B an open-weight model? Yes, Gemma 4 26B A4B is an open-weight model from Google, enabling developers to run it entirely on their own hardware with Atlas, keeping code off third-party servers. ### What are the VRAM requirements for Gemma 4 26B A4B? The memory footprint for Gemma 4 26B A4B is determined by its full 26B parameter count, even though only about 4B are active per token. This means VRAM needs are higher than a 4B dense model. ### How does Gemma 4 26B A4B's MoE architecture affect performance in Atlas? Gemma 4 26B A4B's Mixture-of-Experts (MoE) architecture means its throughput tracks a smaller dense model (around 4B active parameters) while its capacity tracks a larger one (26B total parameters), offering a balance of speed and capability within Atlas. ### Can I run Gemma 4 26B A4B locally with Atlas? Yes, as an open-weight model, Gemma 4 26B A4B can be run locally with Atlas using Ollama embeddings, ensuring your code remains on your machine and does not leave for third-party servers. ### What is the pricing for Gemma 4 26B A4B? Pricing for Gemma 4 26B A4B is not publicly listed. Hosted access costs will depend on the specific provider you route through, or you can run it locally to manage costs directly. ### How does Atlas use Gemma 4 26B A4B's large context window for code search? Atlas leverages Gemma 4 26B A4B's 262,144 token context window to power Axis, its hybrid semantic and keyword retrieval code search, allowing for more comprehensive and accurate results across large codebases. ### What is the output token cap for Gemma 4 26B A4B? Gemma 4 26B A4B has a 32,768 token output cap, which Atlas respects when generating responses and code modifications. --- Canonical HTML: https://seashell.sh/resources/models/gemma-4-26b-a4b Source of truth: aeo_pages row `/resources/models/gemma-4-26b-a4b` (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.