<cite index="1-1">Grok Code Fast 1 is a speedy and economical reasoning model that excels at agentic coding.</cite>
<cite index="2-1">Released in late August 2025, the xAI Grok Code Fast 1 model is a coding-focused AI model that excels at common, high-volume coding task and is designed especially for agentic coding workflows.</cite> <cite index="1-6,1-7,1-8">Built from scratch with a brand-new model architecture, it was trained on a pre-training corpus rich with programming-related content, and curated high-quality datasets that reflect real-world pull requests and coding tasks.</cite> <cite index="1-23">The model is particularly adept at TypeScript, Python, Java, Rust, C++, and Go.</cite> <cite index="1-13">The model is generally available via the xAI API, priced at $0.20 / 1M input tokens, $1.50 / 1M output tokens, and $0.02 / 1M cached input tokens.</cite>
Bottom line: *A lightweight, cost-efficient reasoning model optimized for speed in agentic coding workflows, trading some accuracy for rapid iteration.*
<cite index="1-13">Standard API pricing: $0.20 per 1M input tokens, $1.50 per 1M output tokens, $0.02 per 1M cached input tokens</cite>
<cite index="1-20">Free access for a limited time through select launch partners including GitHub Copilot, Cursor, Cline, Roo Code, Kilo Code, opencode, and Windsurf</cite>
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Grok Code Fast 1 has been reliable for quick code refactors in my workflow. It nails small Python edits in under 2 seconds, which makes iterating on tests feel instant. The one caveat is that it sometimes over-trims comments when compressing code, so review diffs before committing.
Agreed — the comment-trimming issue bit me too. I now always run git diff before pushing.
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Released in late August 2025, the xAI Grok Code Fast 1 model is a coding-focused AI model that excels at common, high-volume coding task and is designed especially for agentic coding workflows. With its speed, efficiency, and low cost, this model is built to handle the loop of modern software development (planning, writing, testing, and debugging), offers real-time, summarized trace of its reasoning, and is proficient in TypeScript, Python, Java, Rust, C++, and Go. Internally, it uses a mixture-of-experts architecture with an estimated 314 billion parameters, designed to balance speed with coding capability, and delivers approximately 92 tokens per second in practical usage. The model addresses the challenge that powerful AI models often don’t feel purpose-built for agentic coding workflows where loops of reasoning and tool calls can feel frustratingly slow, as engineers saw room for a more nimble, responsive solution optimized for day-to-day tasks.
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