[Qwen 3 32B Instruct](/models/alibaba--qwen-3-32b-instruct) and [Yi 1.5 34B Chat](/models/01-ai--yi-1.5-34b-chat) occupy the same parameter tier, but Qwen 3 is a newer generation model with measurably stronger benchmark scores. On MMLU, Qwen 3 32B scores approximately 82–85% versus Yi 1.5 34B Chat's 76–78% — a 6–7 point gap that reflects Alibaba's more recent training run. Pricing is similar: both land in the $0.20–0.40/M token range, though Yi 1.5 34B Chat is often cheaper on commodity providers by $0.05–0.10/M. Yi 1.5 34B Chat has strong Chinese-language performance — 01.AI's training corpus prioritizes Mandarin alongside English, making it a solid option for bilingual Chinese/English applications. It also has a large community of fine-tunes and adapters, particularly for roleplay and document summarization tasks. If you're building on provider ecosystems with strong Yi support (several Asian cloud providers have optimized Yi serving), you may see better operational SLAs. Qwen 3 32B Instruct outperforms on reasoning chains, math, and code generation. Its instruction-following is tighter, with fewer off-format responses on structured output tasks. For agentic systems where the model needs to follow multi-step tool-use protocols reliably, the generation quality difference is measurable in production. Pick Yi 1.5 34B Chat if Chinese-language quality, cost minimization, or an existing Yi-optimized provider is the driver. Pick Qwen 3 32B Instruct if you need the higher benchmark ceiling for reasoning-heavy or structured-output workloads.