The headline difference between [Gemma 2 27B IT](/models/google--gemma-2-27b-it) and Yi 1.5 34B Chat is context length: Gemma 2 tops out at **8K tokens**, while Yi 1.5 34B Chat extends to **200K tokens** — the widest window in this parameter range. That alone routes most long-document workloads to Yi. Parameter count favors Yi 1.5 34B at 34B vs 27B, and that gap shows on benchmarks. Yi 1.5 34B scores approximately 76–77 on MMLU compared to Gemma 2 27B's ~74. The difference is modest in English, but Yi 1.5 was trained heavily on Chinese and English corpora, giving it a clear lead on Chinese-language tasks — often 10+ points on C-Eval benchmarks. On pricing, both models sit in the $0.10–$0.25/M input token range depending on provider, with Gemma 2 27B often slightly cheaper due to its broader availability. Yi 1.5 34B's provider set is narrower, which limits price competition. **Gemma 2 27B IT** is the right call for short-context English workloads: structured extraction, classification, function-calling pipelines where the input reliably fits 8K. Its tighter latency profile and broad provider availability make it easy to deploy cost-effectively. **Yi 1.5 34B Chat** handles anything requiring a large context window — summarizing long contracts, multi-document RAG, or extended agentic sessions — and is the clear choice for Chinese-language inference. Pick [Yi 1.5 34B Chat](/models/01-ai--yi-1.5-34b-chat) if you need 200K context or Chinese-language quality. Pick Gemma 2 27B IT if your context fits 8K and you want the widest provider selection.