OLMo 2 13B and Phi-3 Medium 128K are both ~13–14B dense models, but they represent different design philosophies. Phi-3 Medium was trained on a heavily curated "textbook-quality" dataset, yielding strong MMLU scores (~78) and coding performance that punches well above its parameter count. OLMo 2 13B prioritizes full transparency — Apache 2.0 weights, fully documented training data — with MMLU around 63. On price, both models occupy a similar band: $0.18–$0.35/M tokens depending on provider, though Phi-3 Medium's 128K context window can trigger premium pricing at long context on some platforms. The 128K context is Phi-3 Medium's defining advantage. For workloads that involve long documents, multi-turn chat histories, or large codebases passed in-context, this removes the chunking overhead that OLMo 2 13B's shorter context (typically 4K–8K effective) forces on you. **Where OLMo 2 13B wins:** scenarios requiring full model transparency, on-prem deployment with zero license restrictions, or research pipelines where auditable training data matters. The Apache 2.0 license has no commercial restrictions whatsoever. **Where Phi-3 Medium 128K wins:** long-document summarization, retrieval-free Q&A over large corpora, or coding tasks where quality-per-parameter efficiency matters. The curated training data consistently surfaces better reasoning on structured tasks. Pick [OLMo 2 13B Instruct](/models/allenai--olmo-2-13b-instruct) when openness, reproducibility, or on-prem licensing are requirements. Pick [Phi-3 Medium 128K](/models/microsoft--phi-3-medium-128k) when you need a long context window or better benchmark quality at comparable cost.