StarCoder2 15B Instruct prices in at $0.04–0.06/M tokens, roughly 40–60% less than [Qwen 2.5 Coder 32B](/models/alibaba--qwen-2.5-coder-32b-instruct) at $0.07–0.10/M. The gap is modest in dollar terms but compounds fast: at 500M tokens/month the difference exceeds $15K annually. What you're buying with Qwen 2.5 Coder 32B is measurably better pass@1 on HumanEval — around 88% vs StarCoder2 15B's 72% — and significantly stronger instruction-following for multi-step coding tasks. [StarCoder2 15B Instruct](/models/bigcode--starcoder2-15b-instruct) shines on fill-in-the-middle (FIM) completion tasks where BigCode's training corpus pays dividends. It handles single-file completions in C++, Rust, and Go with low latency thanks to its smaller footprint, and throughput scales well on commodity A10 GPUs without requiring A100/H100 class hardware. Qwen 2.5 Coder 32B pulls ahead on complex generation tasks: full function synthesis, test generation from specs, and multi-file context reasoning. Its instruction tuning is also more robust — prompts that require conditional logic or structured output (JSON schema adherence) have a higher success rate without few-shot examples. Pick StarCoder2 15B Instruct if you're running a high-volume FIM autocomplete service on a budget and can tolerate occasional instruction-following failures. Pick Qwen 2.5 Coder 32B if pass@1 accuracy or instruction fidelity is the bottleneck — the ~2× parameter advantage translates into measurably fewer retries in production.