[Qwen 2.5 Coder 7B](/models/alibaba--qwen-2.5-coder-7b-instruct) and [StarCoder2 15B Instruct](/models/bigcode--starcoder2-15b-instruct) are priced similarly — both land in the $0.03–0.06/M token range depending on provider — but they differ substantially in architecture focus. StarCoder2 15B carries roughly 2× the parameters, trained with BigCode's permissively licensed corpus emphasizing fill-in-the-middle (FIM). Qwen 2.5 Coder 7B is denser and instruction-tuned with strong emphasis on chat-style prompting and structured output. On standard code-gen benchmarks (HumanEval, MBPP), the two models trade wins: StarCoder2 15B scores higher on FIM-specific evaluations while Qwen 2.5 Coder 7B's instruction tuning gives it an edge on zero-shot function synthesis from natural-language specs. In practice, Qwen 2.5 Coder 7B is faster at inference due to lower parameter count — expect 30–40% better throughput per GPU for latency-sensitive workloads. StarCoder2 15B Instruct is the better fit for IDE autocomplete pipelines where FIM is the primary prompt format, or for teams using BigCode's permissive licensing terms to avoid Alibaba IP constraints. Its wider training corpus also helps on niche languages like Fortran, Julia, or Scala. Pick Qwen 2.5 Coder 7B if you're building chatbot-style coding assistants, generating tests from prose specs, or running at scale where latency and cost per token drive the decision. Pick StarCoder2 15B Instruct if FIM completion quality or licensing provenance is the deciding factor.