One general-purpose 8B, one code-specialist 7B, and a 15B built for auditability — three models serving different engineering needs at the small end of the hosting cost curve. Llama 3.1 8B Instruct is Meta's widely-deployed 8-billion-parameter model from July 2024, available across virtually every major inference provider and typically priced at the low end of the 7–8B tier. It handles instruction following, summarization, classification, and light coding tasks without specialization. Its strength is breadth and ecosystem: if you need a single model to cover diverse task types at low cost, Llama 3.1 8B is the default starting point. The Llama 3 community license permits commercial use with standard attribution requirements. Qwen 2.5 Coder 7B Instruct, released by Alibaba in November 2024, is designed specifically for IDE-embedded code completion and generation. At 7 billion parameters it delivers HumanEval performance competitive with DeepSeek Coder 6.7B, while the 131K context window lets it accept full file trees without chunking. Hosted pricing typically runs below $0.20 per million tokens, making tab-completion-at-scale economically viable. For workloads that are code-only or heavily code-dominant, the code-specialized fine-tuning makes a measurable quality difference over a generalist 8B. The Qwen license permits commercial deployment. StarCoder2 15B Instruct, from the BigCode collaboration between HuggingFace and ServiceNow, has 15 billion parameters with a 16K context window released September 2024. On HumanEval it trails Qwen 2.5 Coder 7B despite being more than twice the size — the training-data constraint and architecture differences account for this. The model's case is training-data provenance: The Stack v2 is restricted to permissively licensed code, and that auditability matters for enterprise IP teams. BigCode OpenRAIL-M is commercially usable with narrow restrictions. Pick Llama 3.1 8B for general-purpose tasks and broad ecosystem support. Pick Qwen 2.5 Coder 7B when your workload is code-first and you want specialized benchmark performance with long-context support. Pick StarCoder2 15B when training-data traceability is a hard compliance requirement that outweighs raw benchmark standings.