Within the Yi 1.5 family, the 34B and 9B models share architecture and training data but differ in parameter count — and that gap shows up in both cost and quality. [Yi 1.5 34B Chat](/models/01-ai--yi-1.5-34b-chat) typically costs $0.25–0.40/M tokens versus $0.03–0.07/M for the 9B, a 5–8× cost ratio that makes model selection a straightforward cost-quality calculation. On MMLU, the 34B scores approximately 76–78% versus the 9B's 65–68% — a 10+ point gap. Multi-hop reasoning, longer context coherence, and complex instruction adherence all improve substantially with the larger model. For tasks that require holding context across a full document (8K+ tokens) or following multi-constraint instructions, the 34B is noticeably more reliable. [Yi 1.5 9B Chat](/models/01-ai--yi-1.5-9b-chat) handles single-turn Q&A, classification, summarization of short documents, and low-latency chatbot responses efficiently. Its memory footprint (fits on a single A10G with room to spare) means better throughput per dollar on shared GPU infrastructure, and cold-start latency is significantly lower than the 34B. Both models carry 01.AI's strong Mandarin Chinese training, so the language-quality advantage is less of a differentiator within the family; the choice reduces to task complexity and budget. Pick Yi 1.5 9B Chat for high-volume, cost-sensitive workloads where single-turn quality in the 65–68% MMLU range is acceptable. Pick Yi 1.5 34B Chat when multi-step reasoning, longer context, or higher accuracy justify the 5–8× token cost increase.