Qwen 2.5 Coder 32B runs at roughly $0.07–0.10/M input tokens on most inference providers, while Refact Llama 3.1 70B sits closer to $0.50–0.70/M — a 5–7× cost gap that matters at scale. The tradeoff is parameter count: 70B gives Refact more headroom on complex reasoning chains, but Qwen 2.5 Coder 32B was purpose-trained on code-heavy corpora and consistently outperforms larger general-purpose models on HumanEval and MBPP benchmarks. On latency, [Qwen 2.5 Coder 32B](/models/alibaba--qwen-2.5-coder-32b-instruct) delivers first-token responses roughly 40% faster than a 70B-class model under comparable GPU load, which matters for interactive autocomplete or short-form generation loops where P50 < 500 ms is a hard requirement. [Refact Llama 3.1 70B](/models/togethercomputer--refact-llama-3.1-70b) earns its place on batch jobs that mix code with natural-language reasoning — multi-step refactoring briefs, architecture docs with embedded pseudocode, or codebase summarization where 32B models occasionally lose thread across long contexts. The 70B architecture also holds up better on multilingual codebases where identifier names aren't in English. For real-time IDE completion, PR review bots, or any latency-sensitive pipeline running millions of requests per day, Qwen 2.5 Coder 32B is the clear cost-performance winner. Pick Refact Llama 3.1 70B if your workload blends natural-language reasoning with code at context lengths above 8K or if you need stronger performance on non-English source files.