A general-purpose 70B, an efficient 14B reasoning model, and a code-specialized 32B are not usually grouped together — but all three offer 131K context windows and have meaningful overlap for code generation and analysis tasks. Llama 3.1 70B Instruct is Meta's July 2024 model that established 131K context as a standard expectation at the 70B tier. For coding tasks it performs competently on HumanEval and general code completion, though it is not specialized. MMLU around 79–80, broad provider coverage, Llama 3 community license. Note that Llama 3.3 70B has improved instruction-following at the same footprint and should be preferred for new deployments where no version pinning is required. Phi-3 Medium 128K at 14B is the cost-efficiency pick. The synthetic-data training helps on structured reasoning problems including algorithmic QA, but HumanEval performance lags specialized code models. For teams doing prompt-heavy code analysis or extraction where model behavior is predictable, the lower per-token cost and MIT license are real advantages. Qwen 2.5 Coder 32B Instruct is the specialist in this group — code-tuned across 92 programming languages on a 131K context window, with HumanEval, LiveCodeBench, and MultiPL-E scores that match DeepSeek Coder V2 in its class. The specialization shows in fill-in-middle tasks, code review, and multi-file context analysis. It is hosted primarily on DeepInfra and a handful of other providers; the Qwen license permits commercial deployment. At 32B parameters it sits between Phi-3 Medium on cost and Llama 3.1 70B on capability, but for code specifically it outperforms the 70B generalist. Pick Qwen 2.5 Coder 32B when code is the primary workload and HumanEval-class performance matters. Pick Llama 3.1 70B for general-purpose tasks that include code but also require broader reasoning and instruction following. Pick Phi-3 Medium 128K when cost is the constraint and code tasks are structured enough that benchmark depth is secondary.