[Llama 3.1 8B Instruct](/models/meta--llama-3.1-8b-instruct) is Meta's general-purpose small model with strong benchmark performance and wide provider support. [OLMo 2 7B Instruct](/models/allenai--olmo-2-7b-instruct) is Allen Institute for AI's fully open model — weights, training data, code, and evaluation harness are all public. That transparency distinction matters for some engineering teams, but the performance and cost story favors Llama 3.1 8B in most production scenarios. On quality benchmarks, Llama 3.1 8B outperforms OLMo 2 7B on MMLU, ARC, and instruction-following evals by 4–8 points on average. Both models are priced in the $0.05–$0.20/M input token range, though OLMo 2 7B has fewer hosted providers and may show higher variance in availability. Context window support also differs: Llama 3.1 8B supports 128K tokens; OLMo 2 7B tops out at 4K in most deployments. **Where Llama 3.1 8B wins:** Production inference APIs needing quality, long-context support, and provider choice. For most teams, the benchmark gap and the 128K context window make Llama 3.1 8B the straightforward choice when cost is similar. **Where OLMo 2 7B wins:** Research, compliance-driven, or auditability-sensitive deployments where full training transparency is required. If your team needs to inspect training data for bias audits, reproduce training runs, or publish open-science reproducibility findings, OLMo 2's fully disclosed data pipeline is uniquely valuable. **Bottom line:** Pick Llama 3.1 8B Instruct for production workloads where quality, context length, and provider breadth matter. Pick OLMo 2 7B Instruct when data transparency and open-science auditability are hard requirements.