Stable Code Instruct 3B is one of the cheapest deployable code models at roughly $0.01–0.02/M tokens, while [Qwen 2.5 Coder 7B](/models/alibaba--qwen-2.5-coder-7b-instruct) runs at $0.03–0.05/M — a 2–3× cost premium. That premium buys a meaningful accuracy jump: Qwen 2.5 Coder 7B scores around 73% on HumanEval pass@1 versus approximately 52% for a 3B-class model, a 21-point gap that translates directly into fewer fallback calls in production pipelines. On raw throughput, [Stable Code Instruct 3B](/models/stability-ai--stable-code-instruct-3b) is fast — small enough to run on a single A10G at 60+ tokens/sec, making it viable for on-device or edge deployments where memory is under 8 GB. If you're building a self-hosted assistant for an air-gapped environment or targeting consumer-grade GPUs, the 3B fits where 7B won't. Qwen 2.5 Coder 7B handles multi-line completions, docstring generation, and instruction-following prompts with noticeably better consistency. It also maintains quality across longer contexts (4K–8K tokens), whereas 3B models tend to degrade on files exceeding 500 lines. Pick Stable Code Instruct 3B if memory budget is the hard constraint — edge deployment, CI runners with limited VRAM, or extreme cost optimization where 52% pass@1 is acceptable. Pick Qwen 2.5 Coder 7B if you need reliable instruction-following and multi-line synthesis at moderate scale, and can absorb a 2–3× token cost increase.