Grok Code Fast 1
Version: 1
Direct from Azure models
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Key capabilities
About this model
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.Responsible AI considerations
Safety techniques
Because models developed by xAI push the frontier of AI capabilities, xAI seeks to mitigate their risks through both evaluating model behaviors and implementing safeguards. Details about the evaluation and mitigation for top RAI risks are included in the xAI Grok Code Fast 1 model card.Safety evaluations
Microsoft's safety and responsible AI evaluations found Grok Code Fast 1 to be less safe than other models evaluated and offered through Azure Direct. In particular, the review found that the model brings (i) higher risks of producing potentially harmful content (e.g., content including hate and unfairness, sexual, violent, and glorification of self-harm) and (ii) higher risks of successful jailbreak attacks. Grok Code Fast 1 benchmarks and system evaluations are detailed in the xAI Grok Code Fast 1 model card. We have also made safety benchmarks available for this model in the model card benchmark tab and the tradeoff chart, which illustrates the significantly higher jailbreak success rates on Grok Code Fast 1 compared to other models.Known limitations and considerations
We require that customers use both system safety messages and Azure AI Content Safety (AACS) service to manage model behavior and comply with the Microsoft Enterprise AI Services Code of Conduct, but note that integration of these required safeguards will probably not mitigate all the risks. Given Microsoft's safety and responsible AI evaluation found Grok Code Fast 1 to be less safe than other models offered through Azure Direct (see "Safety evaluations" section above), there may be categories of harm this model can produce that are not covered by Azure AI Content Safety evaluation and mitigations. Therefore, customers should conduct their own evaluations according to their intended use cases and implement appropriate mitigations before deploying Grok Code Fast 1 in production systems.Acceptable use policy
Review the xAI model card for additional information on system evaluations, expected behavior, and safety systems. Customers are required to use both system safety messages and Azure AI Content Safety (AACS) service to manage model behavior and comply with the Microsoft Enterprise AI Services Code of Conduct. See the "Out of scope use cases" section for scenarios where we do not recommend deploying Grok Code Fast 1.Key model capabilities
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 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. Grok Code Fast 1 is designed for agentic coding tasks, excelling in rapid prototyping, bug fixing, and navigating large codebases with minimal oversight. Its speed and low-cost API make it ideal for high-throughput tasks like CI automation and batch code generation.Use cases
See Responsible AI for additional considerations for responsible use.Key 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
While the required system safety message and AACS are expected to mitigate some of the risks with responsible AI, customers should still expect higher likelihood of generation of problematic content with this model, especially given the higher attack success rate with jailbreaks. Thus, we cannot recommend using Grok Code Fast 1 for high-risk use cases, including but not limited to systems that are intended to provide health or mental health related advice, generate legal content, and systems that might be used by minors in any capacity.Pricing
Pricing is based on a number of factors, including deployment type and tokens used. See pricing details here.Technical specs
It uses a new lightweight transformer-based architecture optimized for speed and cost-efficiency. The model's speed (up to 160 tokens/second) outperforms rivals like Claude Sonnet in coding efficiency. It excels in coding accuracy (93.0%) and instruction following (75.0%), with 100% reliability across seven benchmarks.Training cut-off date
The provider has not supplied this information.Training time
The provider has not supplied this information.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.
Output formats
The model supports structured outputs and function calling for seamless integration with development tools.Supported languages
EnglishSample JSON response
The provider has not supplied this information.Model architecture
It uses a new lightweight transformer-based architecture optimized for speed and cost-efficiency.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.Optimizing model performance
The provider has not supplied this information.Additional assets
The provider has not supplied this information.Training disclosure
Training, testing and validation
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.Distribution
Distribution channels
The provider has not supplied this information.More information
The provider has not supplied this information.Responsible AI considerations
Safety techniques
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. 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 evaluations
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.Known limitations
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. Limitations include reduced accuracy in complex tasks, mitigated by encouraging iterative prompting. 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.Acceptable use
Acceptable use policy
Prohibited uses include generating harmful, illegal, or copyrighted content, as outlined in xAI's acceptable use policy.Quality and performance evaluations
Source: xAI 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. Microsoft's safety and responsible AI evaluations found the behavior of Grok Code Fast 1 to be less safe than other models evaluated and offered through Azure Direct. In particular, there are (i) higher risks that the model will produce harmful content that communicates hate, unfairness, sexualization, violence and (ii) higher risks of successful jailbreak attacks. We have also made safety benchmarks available for this model in the model card benchmark tab and the tradeoff chart.Benchmarking methodology
Source: xAI 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. 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.Public data summary
Source: xAI The provider has not supplied this information.Model Specifications
Context Length256000
Quality Index0.68
LicenseCustom
Last UpdatedMay 2026
Input TypeText
Output TypeText
ProviderxAI
Languages1 Language
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