[Llama 3.1 8B Instruct](/models/meta--llama-3.1-8b-instruct) and [Yi 1.5 9B Chat](/models/01-ai--yi-1.5-9b-chat) are both sub-10B models priced at $0.05–$0.20/M input tokens. The 1B parameter difference is largely irrelevant to serving cost, but the architectural gap matters: Llama 3.1 8B supports a 128K context window while Yi 1.5 9B Chat tops out at 4K tokens in most hosted deployments. For any workload involving documents, transcripts, or long multi-turn conversations, that gap is decisive before you even compare quality. On English benchmarks, Llama 3.1 8B scores higher across MMLU and instruction-following tasks. Yi 1.5 9B Chat's strength is bilingual quality: 01.AI's training corpus is heavily weighted toward Chinese, and the model consistently outperforms comparably sized Western-origin models on Chinese language tasks. Throughput is comparable — both achieve 110–170 tok/s on A10G hardware. **Where Llama 3.1 8B wins:** Long-context applications, English-language APIs, and deployments requiring broad provider choice. The 128K context window alone rules out Yi 1.5 9B Chat for document-heavy workloads, and Llama's provider breadth gives better SLA and pricing competition. **Where Yi 1.5 9B Chat wins:** Chinese-language products or bilingual assistants where 01.AI's training depth in Chinese produces measurably better fluency and instruction adherence than general-purpose small models. Provider options are narrower but sufficient for production use. **Bottom line:** Pick Llama 3.1 8B Instruct for most production workloads — especially anything English-dominant or requiring long context. Pick Yi 1.5 9B Chat specifically for Chinese-language or bilingual applications where language quality outweighs context length limitations.