A comparison defined more by licensing philosophy than by raw benchmark gaps. Mistral 7B Instruct v0.3 is Mistral AI's May 2024 final iteration of the 7B series, adding native function-calling support and an extended vocabulary over earlier releases. At around 7 billion parameters with a 32K context window, it remains one of the most cost-effective options for classification, summarization, and lightweight agentic pipelines. Pricing across hosted providers typically sits below $0.10 per million tokens. Apache 2.0 license makes it usable for fine-tuning and redistribution without legal overhead. On raw quality it has been surpassed by Llama 3.1 8B and Qwen 3 8B, but the function-calling support at this price floor still justifies it for specific use cases. OLMo 2 13B Instruct and OLMo 2 7B Instruct are Allen AI's November 2024 releases, both fully open under Apache 2.0 with weights, Dolma training corpus, and training code all published. They represent the most reproducible option in the sub-15B class. The 13B variant benchmarks slightly above the 7B on standard evaluations, but neither can compete with Mistral 7B v0.3 on general-chat quality at equivalent parameter counts. Both carry a 4K context ceiling, which is the binding constraint for any workload beyond short-form tasks. Hosted provider coverage for both OLMo models is thin. The clearest split: if you need production inference at the lowest possible cost with function-calling support, Mistral 7B v0.3 wins. If you need a fully auditable model for research, fine-tuning studies, or reproducibility requirements, OLMo 2 wins — and the 13B variant is worth the extra compute if your evaluation suite rewards the quality gap. Pick Mistral 7B v0.3 for production inference workloads requiring function calling at sub-$0.10 pricing. Pick OLMo 2 13B when parameter count and training provenance both matter for your research pipeline. Pick OLMo 2 7B for the most compact fully-open baseline available.