LLMs have moved beyond simple autocomplete to become core infrastructure for software development. Whether you are generating unit tests, refactoring legacy modules, or orchestrating multi-file agents, production-grade code generation depends on three factors: model capability, context management, and inference economics. Token-based billing can make repository-level workflows prohibitively expensive, which is why the underlying platform matters as much as the prompt.
Model Selection for Code Tasks
Not all code generation is the same. Inline completion requires speed and low latency, while architectural planning benefits from deep reasoning. Oxlo.ai offers specialized models across this spectrum. For pure coding tasks, Qwen 3 Coder 30B, DeepSeek Coder, and Oxlo.ai Coder Fast provide strong performance on structured languages and scripting. When you need reasoning about system design or complex debugging, models like DeepSeek R1 671B MoE, Kimi K2.6, and Llama 3.3 70B offer advanced chain-of-thought capabilities.
Because Oxlo.ai is fully OpenAI SDK compatible, you can switch between these models without rewriting client logic. The unified endpoint at https://api.oxlo.ai/v1 lets you benchmark a fast coder against a heavy reasoning model using the same request shape.
Structuring Prompts and Context Windows
Code is context-heavy. A single function often depends on imports, type definitions, and business logic scattered across a repository. Effective prompts include relevant file headers, dependency trees, and style guides in the system message. The challenge is that this context can quickly reach tens of thousands of tokens.
On token-based providers, long system prompts directly inflate cost. Oxlo.ai uses request-based pricing, so the price per call stays flat regardless of prompt length. That makes it practical to pass full file contents or directory summaries into the context window without micromanaging token budgets. You can view the exact structure of flat per-request pricing on the Oxlo.ai pricing page.
Tool Use and Agentic Workflows
Modern coding agents do not just write text. They run linters, execute tests, query documentation,
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