Grok API: xAI Reasoning and Coding Models

The Grok API gives developers xAI's Grok models on Atlas Cloud through one OpenAI-compatible key. It covers Grok 4.3, an advanced reasoning model with a 1M token context window, and Grok Build 0.1, a coding-focused model for software workflows. Both run alongside 300+ other models on the same account, so you can reach Grok and switch between providers without separate integrations.

Grok 4.3 is xAI's flagship reasoning model, built for accuracy-critical work like logic, analysis, and multi-step agentic tasks, with a 1M token context window. Grok Build 0.1 is tuned specifically for agentic coding: generation, debugging, and refactoring across developer workflows, with a 256K token context. Use Grok 4.3 for general reasoning and Grok Build 0.1 when the job is primarily writing or fixing code.

Grok 4.3 accepts text and image inputs and returns text, with a context window of up to 1M tokens, enough for long documents, large codebases, and extended agent sessions. Grok Build 0.1 handles text and image input with a 256K token context. The wide context lets you pass full source material in a single request instead of splitting it into retrieved fragments.

Yes. The Grok API on Atlas Cloud uses OpenAI-compatible endpoints, so an app already built on the OpenAI SDK can switch by changing the base URL and model name. There is no need to rewrite your request logic or manage a separate client, which keeps migration to a few lines of code.

Grok 4.3 performs step-by-step reasoning before it answers, which is what drives its accuracy on complex logic and analysis. Reasoning behavior can vary by model and configuration, so whether it can be reduced or disabled per request depends on the parameters exposed for that model. Check the model settings in the Atlas Cloud console for the current reasoning options before relying on a specific mode in production.

Yes. The Grok API supports function calling, so the model can invoke your tools and APIs mid-task, and it returns structured JSON that conforms to a schema you define. Together these let you build agents that take actions and feed clean, parseable data straight into downstream code, rather than parsing free-form text.

Prompt caching reuses a repeated context prefix, such as a long system prompt or shared instructions, at a reduced input token rate on later calls. For chatbots and agents that resend the same setup on every request, this lowers repeated input cost without changing your code. Put static content at the start of the prompt and variable user content at the end so the cache applies.

Rate limits and concurrency vary by account tier, so add exponential backoff and a retry on a 429 response, and queue requests during traffic spikes. For large offline jobs, batch processing keeps bulk work off your real-time limits. A common hidden cost at scale is resending full conversation history on every call, so pass a compact summary instead of the entire thread, and contact support to raise limits as you grow.

The Grok API uses pay-as-you-go billing based on token usage, with input and output tokens metered per request and no subscription required. Running Grok next to 300+ other models on Atlas Cloud means one account and one bill rather than separate contracts per provider. Prompt caching and batch processing can reduce effective cost on repetitive or offline workloads.

Create an account on Atlas Cloud, generate an API key, and point your existing OpenAI-compatible client at the Atlas endpoint with the Grok model name. Send your first request to Grok 4.3 for reasoning or Grok Build 0.1 for coding, then scale up as needed. Because the same key reaches 300+ models, you can test other models without any extra setup.