What is the context window for Atlas with Phi-4 (local via Ollama)?

Atlas with Phi-4 (local via Ollama) operates with a 16K tokens (16,384) context window. This size is a key factor in determining its suitability for various coding tasks within Atlas.

How much does it cost to use Phi-4 (local via Ollama) with Atlas?

Using Phi-4 (local via Ollama) with Atlas is Free (self-hosted). This means there are no direct costs associated with model usage, as it runs entirely on your local hardware.

Can Phi-4 (local via Ollama) handle large codebases in Atlas?

Due to its 16K tokens (16,384) context window, Phi-4 (local via Ollama) is constrained and cannot effectively hold a large Atlas search result, a full file, and a complex diff simultaneously. It is better suited for smaller, focused tasks.

Is Phi-4 (local via Ollama) good for reasoning tasks in Atlas?

Yes, Phi-4 (local via Ollama) is known for punching above its weight on reasoning benchmarks, a direct result of its synthetic-textbook training. This makes it effective for Atlas's planning agent and summarization roles.

What hardware is needed to run Phi-4 (local via Ollama) with Atlas?

Phi-4 (local via Ollama) is roughly 9GB at 4-bit, meaning it fits comfortably on a 12GB GPU alongside an embedding model. This allows for responsive Atlas TUI performance.

Why is Phi-4 (local via Ollama) recommended as a `small_model` for Atlas?

Atlas recommends using Phi-4 (local via Ollama) as a `small_model` for tasks like generating titles and summaries because its 16K tokens (16,384) context window is a binding constraint for more extensive build agent operations.

Atlas with Phi-4 (local via Ollama) in 2026

Atlas with Phi-4 (local via Ollama) provides a powerful, self-hosted solution for developers in 2026 seeking a local AI coding agent. This 14B parameter model excels at reasoning and is ideal for generating concise titles and summaries within Atlas, leveraging its synthetic-textbook training. It is entirely Free (self-hosted), making it a cost-effective choice for local development environments.

What is Phi-4 (local via Ollama) best for with Atlas?

Phi-4 (local via Ollama) is best suited for Atlas's planning and summarization tasks in 2026, leveraging its 14B parameters to deliver strong reasoning capabilities. Its synthetic-textbook training allows it to punch above its weight on math and reasoning benchmarks, making it an excellent choice for generating concise titles and summaries within Atlas.

Atlas can effectively utilize Phi-4 (local via Ollama) as a `small_model` for tasks requiring strong reasoning and summarization, such as drafting initial plans in the read-only plan agent or generating titles for code changes. The model's 14B parameters, trained heavily on synthetic curriculum data, enable it to perform well on reasoning benchmarks. This makes Phi-4 (local via Ollama) a valuable asset for Atlas's ability to search code with Axis, its hybrid semantic and keyword retrieval system, and to process the results into actionable insights. Its local operation also means Atlas can build its code index with local Ollama embeddings, keeping code off third-party servers.

What are the cost and context window tradeoffs for Phi-4 (local via Ollama)?

Phi-4 (local via Ollama) offers a compelling Free (self-hosted) price point in 2026, making it highly accessible for developers. However, its 16K tokens (16,384) context window is a binding constraint, limiting its capacity for large-scale code analysis within Atlas.

The primary advantage of Phi-4 (local via Ollama) is its cost: it is Free (self-hosted), eliminating any per-token charges. This makes it an attractive option for developers running Atlas locally. Regarding hardware, Phi-4 (local via Ollama) is roughly 9GB at 4-bit, allowing it to fit comfortably on a 12GB GPU alongside an embedding model for Atlas's local codebase index. This ensures the Atlas TUI stays responsive, a significant benefit over larger 70B-class local models. The main tradeoff is its 16,384 token context window. This constraint means Phi-4 (local via Ollama) cannot hold a large Atlas search result, a full file, and a complex diff simultaneously, which limits its utility for extensive code modifications or deep dives into large codebases.

When should I choose a different model over Phi-4 (local via Ollama) for Atlas?

Developers in 2026 should consider alternative models for Atlas when their tasks demand a context window larger than 16K tokens (16,384) or require robust performance on messy, real-world code. While Phi-4 (local via Ollama) excels in reasoning, its synthetic-data training has known limitations.

While Phi-4 (local via Ollama) punches above its weight on reasoning benchmarks, it is weaker on messy real-world code than its scores suggest. This is a known criticism of synthetic-data training. For Atlas tasks that involve extensive code modifications, refactoring large files, or debugging complex systems where a broad understanding of the codebase is crucial, a model with a significantly larger context window would be more effective. Atlas's ability to compute a unified diff for every file edit and surface it for approval before writing, or its capacity to read git branches, status, and diffs, benefits greatly from models that can process more context. When Atlas needs to act as a build agent for substantial code changes, a model with a greater context capacity than Phi-4 (local via Ollama)'s 16,384 tokens will be necessary to handle large search results, multiple files, and comprehensive diffs.

Setup

01Pull the Phi-4 model using Ollama: Open your terminal and run `ollama pull phi4`. This command downloads the Phi-4 model to your local machine, making it available for Atlas.
02Configure Atlas to use Ollama: Edit your `atlas.json` configuration file. Locate the `ollama` provider block and add or update the entry for Phi-4, ensuring you specify the context limit.
03Set the context window limit: Within the `atlas.json` configuration for Phi-4, include `"limit": { "context": 16384 }`. This explicitly tells Atlas the model's context window size.
04Select Phi-4 in Atlas: Launch Atlas and navigate to the `/models` interface. From there, you can select Phi-4 (local via Ollama) as your active model.
05Utilize Phi-4 as a small model: Given its 16K tokens (16,384) context window, Atlas recommends using Phi-4 (local via Ollama) as a `small_model` for generating titles and summaries, rather than as the primary build agent for extensive code modifications.

Frequently asked questions

What is the context window for Atlas with Phi-4 (local via Ollama)?: Atlas with Phi-4 (local via Ollama) operates with a 16K tokens (16,384) context window. This size is a key factor in determining its suitability for various coding tasks within Atlas.
How much does it cost to use Phi-4 (local via Ollama) with Atlas?: Using Phi-4 (local via Ollama) with Atlas is Free (self-hosted). This means there are no direct costs associated with model usage, as it runs entirely on your local hardware.
Can Phi-4 (local via Ollama) handle large codebases in Atlas?: Due to its 16K tokens (16,384) context window, Phi-4 (local via Ollama) is constrained and cannot effectively hold a large Atlas search result, a full file, and a complex diff simultaneously. It is better suited for smaller, focused tasks.
Is Phi-4 (local via Ollama) good for reasoning tasks in Atlas?: Yes, Phi-4 (local via Ollama) is known for punching above its weight on reasoning benchmarks, a direct result of its synthetic-textbook training. This makes it effective for Atlas's planning agent and summarization roles.
What hardware is needed to run Phi-4 (local via Ollama) with Atlas?: Phi-4 (local via Ollama) is roughly 9GB at 4-bit, meaning it fits comfortably on a 12GB GPU alongside an embedding model. This allows for responsive Atlas TUI performance.
Why is Phi-4 (local via Ollama) recommended as a `small_model` for Atlas?: Atlas recommends using Phi-4 (local via Ollama) as a `small_model` for tasks like generating titles and summaries because its 16K tokens (16,384) context window is a binding constraint for more extensive build agent operations.
Does Phi-4 (local via Ollama) perform well on real-world code?: While benchmark-strong, Phi-4 (local via Ollama) is weaker on messy real-world code than its scores suggest. This is a known criticism of models trained primarily on synthetic data.

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