"xAI introduced grok-code-fast-1, a model developed specifically for agentic coding workflows. The architecture was built from the ground up, with a pre-training corpus composed of programming-related data and a post-training set drawn from real pull requests and practical coding tasks. The model includes optimization for tool usage commands like grep, terminal operations, and file editing, and is meant to integrate smoothly with coding environments and IDEs."
"It also supports several programming languages, including TypeScript, Python, Java, Rust, C++, and Go. It is positioned to handle a range of everyday developer tasks, from project scaffolding and codebase inquiries to precise bug fixes with minimal supervision. Performance was measured on the SWE-Bench-Verified benchmark, where the model scored 70.8 percent using xAI's internal evaluation suite. Beyond benchmarks, xAI also incorporated human evaluations and automated assessments to guide development, focusing on real-world usability."
"To support rapid interaction, the model uses a 256 k token context window, enabling it to process larger codebases in context. Internally, it uses a mixture-of-experts architecture with an estimated 314 billion parameters, designed to balance speed with coding capability. In practical usage, throughput is approximately 92 tokens per second, enabling interactive pacing during development sessions. In comparison with other coding-focused large language models, grok-code-fast-1 places its emphasis on speed and integration with tools rather than maximum benchmark accuracy."
grok-code-fast-1 is an agentic coding model trained on programming-related pre-training data and post-trained on real pull requests and practical coding tasks. The model is optimized for tool commands such as grep, terminal operations, and file editing to integrate with development environments and IDEs. Serving techniques and prompt caching improve responsiveness, with reported cache hit rates above 90 percent in partner workflows. The model supports TypeScript, Python, Java, Rust, C++, and Go and targets tasks from scaffolding and codebase inquiries to precise bug fixes. A 256k token context window and a mixture-of-experts architecture (estimated 314 billion parameters) enable large-context handling and high throughput (~92 tokens/sec). Measured performance reached 70.8 percent on SWE-Bench-Verified, supplemented by human and automated evaluations, with an emphasis on speed and integration over peak benchmark accuracy.
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