Grok Code Fast 1
Version: 1
Grok Code Fast 1, developed by xAI, is a purpose-built AI model for agentic coding, launched on August 28, 2025. It uses a new lightweight transformer-based architecture optimized for speed and cost-efficiency, trained on a pre-training corpus rich with programming content and fine-tuned with real-world pull requests and coding tasks. Unlike generalist models, it prioritizes low-latency responses and tool integration (e.g., grep, terminal, file editing), making it ideal for iterative coding workflows in IDEs like GitHub Copilot and Cursor. Its 256,000-token context window supports large codebases, and prompt caching achieves over 90% hit rates, enhancing responsiveness.
Post-training focused on aligning the model for practical coding tasks, with human evaluations by developers ensuring usability. The model excels in languages like TypeScript, Python, Java, Rust, C++, and Go, and supports structured outputs and function calling for seamless integration with development tools. It differs from larger models like Grok 4 by prioritizing speed and cost over broad reasoning capabilities.
Model developer: xAI
Supported languages: English
Model Release Date: August 28, 2025
Intended Use
Alignment approach
Post-training alignment used high-quality datasets reflecting real-world coding tasks, such as pull requests and bug fixes, to enhance practical utility. Safety alignment targeted reliability and usability, with human evaluations by experienced developers to refine behavior in agentic workflows. Techniques included supervised fine-tuning and reinforcement learning to ensure accurate code generation and tool use, with a focus on minimizing errors in iterative coding scenarios. Safety objectives included preventing disallowed content (e.g., harmful or copyrighted code) and ensuring compliance with developer workflows.Primary Use Cases
Grok Code Fast 1 is designed for agentic coding tasks, excelling in rapid prototyping, bug fixing, and navigating large codebases with minimal oversight. It integrates seamlessly with IDEs like GitHub Copilot and Cursor, supporting developers in tasks like code snippet generation, project setup, and automated edits in TypeScript, Python, Java, Rust, C++, and Go. Its speed and low-cost API make it ideal for high-throughput tasks like CI automation and batch code generation.Out-of-scope Use Cases
The model is not suited for complex, mission-critical projects requiring extensive reasoning or multimodal inputs beyond text. It may underperform in non-coding tasks or non-English languages due to its coding-focused training. Prohibited uses include generating harmful, illegal, or copyrighted content, as outlined in xAI’s acceptable use policy.Input formats
Preferred input is structured text prompts, including code snippets or natural language instructions. Example:- Write a Python function to calculate Fibonacci numbers up to n.
- The model expects clear, task-specific prompts for optimal performance, as detailed in xAI’s Prompt Engineering Guide.
Responsible AI considerations
The model may produce errors in complex coding scenarios, requiring developer verification for critical applications. It is optimized for English and major programming languages, potentially underperforming in niche or non-English contexts. Risks include generating incomplete or incorrect code, mitigated by encouraging small, focused prompts and human oversight. Developers must comply with xAI’s acceptable use policy, avoiding harmful or illegal outputs. For high-risk use cases, implement robust testing and validation to ensure reliability.Safety evaluation and red-teaming
Safety evaluations included automated tests and human reviews to assess disallowed content (e.g., sexual, violent, or copyrighted material) and jailbreak risks. Collaboration with launch partners like GitHub Copilot refined tool-use safety. Red-teaming focused on coding-specific risks, ensuring compliance with developer workflows. No public details on specific risk categories or outcomes were disclosed.Data Overview
Training, testing, and validation datasets
The training dataset comprises a large pre-training corpus of programming-related content (e.g., open-source code, documentation) and post-training datasets of real-world pull requests and coding tasks. Sources include public code repositories and curated synthetic data, with no user data or private third-party data disclosed. The dataset scale is not specified, but it emphasizes diversity in programming languages and tasks. Testing and validation used internal benchmarks and human evaluations by developers. No public data summary is available.Long context
The 256,000-token context window supports large codebases, enabling tasks like repository-wide refactors and multi-file edits. Compared to GPT-4o (128,000 tokens), it handles larger contexts but trails models with 1M-token windows. Performance excels in single-session codebase reasoning, reducing retrieval complexity.Grok Code Fast 1 Benchmark Performance Overview
Grok Code Fast 1 scored 70.8% on SWE-Bench Verified (internal harness), competitive with smaller models like GPT-5-nano but trailing larger models in complex reasoning. It excels in coding accuracy (93.0%) and instruction following (75.0%), with 100% reliability across seven benchmarks. Human evaluations prioritized developer experience in agentic workflows, complementing benchmarks like SWE-Bench. Limitations include reduced accuracy in complex tasks, mitigated by encouraging iterative prompting. The model’s speed (up to 160 tokens/second) outperforms rivals like Claude Sonnet in coding efficiency.Appendix
Benchmarking used SWE-Bench Verified with standardized prompts for fair comparison. Human evaluations supplemented quantitative metrics, focusing on real-world coding tasks. No prompt adaptations were allowed to ensure consistency. Further details on methodology are not publicly available.Model Specifications
Context Length256000
LicenseCustom
Last UpdatedSeptember 2025
Input TypeText
Output TypeText
PublisherxAI
Languages1 Language