Three small open-weights models with genuinely different positions on the openness-to-production spectrum. Gemma 2 9B IT from Google DeepMind is the most commercially accessible of the group, with Groq-hosted inference delivering some of the lowest latency numbers available at this parameter class. Benchmark performance is roughly on par with Llama 3.1 8B for general-purpose tasks, and pricing typically lands below $0.20 per million tokens. The 8K context window is where it falls short — both peers offer dramatically longer windows. Granite 3.1 8B Instruct expanded to 128K context in its December 2024 3.1 release, immediately making it more capable than Gemma 2 9B for document-heavy RAG workloads at similar parameter counts. IBM's enterprise tuning adds structured-output and function-calling quality that shows up in agentic evaluations. Apache 2.0 license. Primary hosting is watsonx.ai, which may add operational friction for teams already committed to AWS or GCP. OLMo 2 7B Instruct is Allen AI's most open release — weights, training data, optimizer states, and evaluation code all under Apache 2.0. Benchmark numbers are competitive with Llama 3.1 8B on standard suites, but the 4K context ceiling is a genuine limitation. The audience for OLMo 2 is primarily researchers: teams that need a fully auditable training pipeline, want to reproduce results, or are building fine-tunes where data provenance is a first-class concern. Production deployment coverage is sparse compared to the other two. Pick Gemma 2 9B for low-latency hosted inference where 8K context suffices. Pick Granite 3.1 8B when long-context RAG and structured outputs are required. Pick OLMo 2 7B only when complete training-data transparency is a hard requirement — it is not the right production choice for most teams.