grok-build-0.1

Overview. Grok Build 0.1 is xAI's dedicated agentic coding model, purpose-built for multi-step software engineering work rather than general chat. It accepts text, image, and file input and returns text, with a 256K-token context window suited to large codebases and long tool-use sessions. Developers reach for it when a task involves iterative debugging, code generation, or orchestrating multiple tool calls in a single workflow rather than a single-shot answer.

Key strengths. Independent benchmark figures put its coding index meaningfully ahead of its general-intelligence index, confirming the model is tuned for code-specific tasks rather than broad reasoning. It always produces a reasoning trace before responding, which shows up in practice as more deliberate, step-by-step debugging and planning behavior. Tool calling is a first-class feature, supporting the kind of MCP-based orchestration that agentic coding workflows depend on.

When to use it. Use Grok Build 0.1 for coding-agent tasks: automated debugging, iterative code edits, and workflows that chain multiple tool calls together, especially where mid-tier reasoning quality with a fast, interactive feel is enough. It's a good fit when you want a model that always shows its reasoning before acting, which helps with auditability in agent pipelines. It's less suited to open-ended agentic tasks outside coding, where its measured agentic performance trails its coding performance.

Limitations. xAI hasn't disclosed parameter count, architecture, or training data cutoff for this model, so those remain unknown. Reasoning is mandatory rather than optional, which adds latency compared to non-reasoning models even though the model is designed to feel responsive in interactive use. Output is capped at 230,400 tokens — about 90% of the full 256K context window — so very long generations may still need to be split across turns.

Sovereignty note. Grok Build 0.1 is built by xAI, a US-based creator, so usage is subject to US CLOUD Act exposure rather than EU data-residency guarantees. No training-on-input policy or endpoint data-residency detail has been published, so those points can't be confirmed either way.