## Llama 3.3 70B Instruct vs Qwen 3 72B Instruct Both models are sub-$1/1M tokens at most providers — a meaningful floor for 70B-class inference. [Llama 3.3 70B Instruct](/models/meta--llama-3.3-70b-instruct) runs $0.20–$0.40/1M tokens; [Qwen 3 72B Instruct](/models/alibaba--qwen-3-72b-instruct) is priced comparably at $0.25–$0.50/1M tokens. The decision comes down to language coverage and benchmark profile, not cost. Llama 3.3 has stronger instruction-following on English MMLU — it scores above 90% on IFEval and consistently outperforms Qwen 3 72B on English-language instruction benchmarks by 3–5 points. Meta's RLHF pipeline is particularly well-tuned for English conversational tasks and structured output generation. Qwen 3 72B has measurably better multilingual performance across CJK (Chinese, Japanese, Korean) and Arabic. On multilingual MMLU and language-specific benchmarks, Qwen 3 72B leads by 6–12 points in these language families. For any application with significant non-English traffic, that gap directly affects end-user quality. **Where Llama 3.3 70B wins:** English-only or English-primary applications — customer support, document processing, code assistance — where instruction fidelity and refusal calibration matter. Provider coverage is broader, simplifying redundancy planning. **Where Qwen 3 72B wins:** Multilingual products serving CJK or Arabic markets, translation pipelines, and content moderation over non-English corpora. The multilingual training depth is reflected in output coherence, not just benchmark numbers. Pick Llama 3.3 70B for English-first workloads. Pick Qwen 3 72B if your user base is meaningfully multilingual or CJK/Arabic-primary.