Grok Code Fast 1 — xAI’s new low-cost, high-speed coding model

August 28, 2025 — xAI today introduced Grok Code Fast 1, a coding-focused variant in the Grok family designed to prioritize low latency and low cost for IDE integrations, agentic coding workflows, and large-codebase reasoning.The model is appearing as an opt-in public preview inside GitHub Copilot (VS Code) and is also available through xAI’s API and CometAPI.

Grok Code Fast 1 is a purpose-built code assistant that xAI positions as a speed-first, budget-conscious model for generating, debugging, and interacting with code. It supports function calling and structured outputs, exposes reasoning traces in responses for better steerability, and accepts very long contexts to handle large codebases in one session.

What it does — key features at a glance

• Massive context window (256,000 tokens): designed to keep very large codebases, long histories, or multi-file projects in memory so prompts and tools can reason across more of a project at once.
• Agentic coding & tool use: supports function calling and structured outputs so it can act as an “agent” that calls tools, returns structured data, or chains actions inside developer flows. built to integrate with toolchains and return machine-friendly responses (JSON, structured diffs, etc.).
• Speed and throughput: positioned as a fast, real-time friendly model with Can process up to 92 tokens per secondthroughput and high RPM/Tokens-per-minute limits for interactive IDE use.
• Developer-oriented outputs: optimized for code generation, debugging, and stepwise reasoning traces (which make the model’s internal “thinking” more steerable for advanced workflows).the model surfaces intermediate reasoning steps so developers and agent systems can inspect and steer its behavior.
• Competitive pricing (input/output token tiers) — published list prices are inexpensive compared with many “frontier” models: roughly \$0.20 per 1M input tokens and \$1.50 per 1M output tokens (xAI’s published pricing). Cached tokens are cheaper when applicable.

Grok Code Fast 1 demonstrated excellent accuracy across several key areas. It achieved perfect scores in Morality, Email Classification, and General Knowledge, often ranking among the most accurate models at its price and speed. It also performed strongly in Coding (93.0% accuracy) and Instruction Following (75.0% accuracy), ranking 90th and 87th, respectively. While its Reasoning Accuracy (80.0%) was robust, it fell slightly short of the top performers in other categories. The model’s key strengths lie in its high accuracy across a variety of knowledge and classification tasks, as well as its excellent reliability. Its main area for improvement is speed, which, while moderate, doesn’t justify its “fast” designation across all benchmarks.

How it compares to other coding models

In Tooling & agent integration , with explicit support for function calling and structured outputs, Grok Code Fast 1 competes directly with other code-specialized offerings (OpenAI’s code-tuned models, Anthropic’s Claude code variants, Google’s Gemini Code). The visible reasoning traces are a differentiator for teams wanting explainability in automated code agents.

Grok Code Fast 1’s 256k token window sits above many mainstream models (GPT-4o historically offered 128k) but below some high-context offerings that advertise 1M tokens (e.g., recent GPT-4.1 / Claude/selected vendor options). That makes Grok Code Fast 1 especially well suited for large but not extreme single-file or repo contexts.

Grok Code Fast 1 positions itself differently from large generalist models by focusing on latency, token economics, and agent/tool competency rather than raw multi-task bench scores:

• Price / throughput: Community and gateway listings show Grok Code Fast 1 undercuts many premium coding/assistant models on token cost, making it attractive for high-volume automated runs (e.g., CI, codegen agents).
• Latency and responsiveness: Early user reports and previews emphasize speed as a primary win compared with heavier, higher-cost alternatives; for interactive coding and agent loops this is often more important than absolute benchmark accuracy.
• Capabilities vs. higher-capacity chat models: Models like Anthropic’s and large OpenAI offerings often aim for broader conversational or multimodal capabilities; Grok Code Fast 1 is tuned to be economical when the task is code- and tool-centric.

Concrete use cases — where to pick Grok Code Fast 1

Best fits

• Agentic developer tools / coding agents:Multi-step agents that call linters, tests, and apply programmatic edits benefit from low latency, structured outputs and large context.
• Large refactors / codebase migrations: Feed long files, diffs and test output into a single prompt to generate consistent, repository-wide changes. The large context reduces repeated retrieval complexity.
• CI automation & batch code generation: High-throughput, repeated tasks (code scaffolding, template generation, automated fixes) where cost per token and speed materially lower running costs.
• In-editor assist where low latency matters: Teams who want very snappy completions and long-file context in editors (via Copilot) will see practical UX benefits.

Less suitable / avoid for

• Tasks requiring real-time internet facts or live search: Since Grok Code Fast 1 lacks built-in live web search, don’t use it as a primary source for up-to-the-minute facts without adding retrieval.
• Highly creative, non-code multimodal tasks: If you need advanced multimodal generation (images, audio) or broad conversational intelligence, pick a model optimized for those domains.
• Safety-critical production decisions without human review: Use human-in-the-loop for security reviews, critical bug fixes, or anything that could cause production outages.

Access: how to try Grok Code Fast 1 today

• GitHub Copilot (public preview opt-in):Grok Code Fast 1 is rolling out as an opt-in public preview for Copilot Pro, Pro+, Business and Enterprise users inside Visual Studio Code. Admins in organizations must enable the policy for Business/Enterprise accounts; individuals can opt in via the model picker. xAI models are also reachable in some Copilot plans via Bring Your Own Key (BYOK).
• Direct xAI API / SDK: xAI’s docs include a grok-code-fast-1 model endpoint and quickstart instructions for API access and tooling integration.
• Third-party gateways : Grok Code Fast 1 is accessible through providers such as OpenRouter and the CometAP. Several developer platforms (Cursor, Kilo Code and others) announced temporary free access or trial windows to let users test the model.

Getting Started

CometAPI is a unified API platform that aggregates over 500 AI models from leading providers—such as OpenAI’s GPT series, Google’s Gemini, Anthropic’s Claude, Midjourney, Suno, and more—into a single, developer-friendly interface. By offering consistent authentication, request formatting, and response handling, CometAPI dramatically simplifies the integration of AI capabilities into your applications. Whether you’re building chatbots, image generators, music composers, or data‐driven analytics pipelines, CometAPI lets you iterate faster, control costs, and remain vendor-agnostic—all while tapping into the latest breakthroughs across the AI ecosystem.

Developers can access  Grok 4 through CometAPI, the latest models version listed are as of the article’s publication date. To begin, explore the model’s capabilities in the Playground and consult the API guide for detailed instructions. Before accessing, please make sure you have logged in to CometAPI and obtained the API key. CometAPI offer a price far lower than the official price to help you integrate.

The latest integration Grok Code Fast 1 will soon appear on CometAPI, so stay tuned！Ready to Get Started editing images? → Sign up for CometAPI today !

Bottom line

Grok Code Fast 1 arrives as a focused, developer-centric model that trades maximum single-response accuracy for very large context handling, high throughput, and an IDE-friendly cost/speed profile. For teams wrestling with large codebases, multi-file workflows, or needing fast interactive completions inside VS Code, it’s a compelling new option — and GitHub Copilot’s opt-in preview plus BYOK and third-party gateways make it straightforward to test. As with any new model, measure accuracy, cost, and security against your use cases before full adoption.