Three distinct philosophies packed into roughly the same parameter budget. Gemma 2 9B IT is Google DeepMind's mid-tier entry from July 2024 — nine billion parameters, sub-$0.20-per-million-token pricing, and Groq-class latency for classification and extraction pipelines. The 8K context window is the genuine constraint: anything that needs to process more than a few thousand tokens in a single pass is going to struggle. Mistral 7B Instruct v0.3, the final iteration of Mistral's original 7B series, ships with a 32K context window and native function-calling support added in May 2024. It consistently lands below $0.10 per million tokens on hosted providers, making it the price-floor option in this group. General-chat benchmarks show it trailing the other two by a modest but real margin, and the function-calling addition is what keeps it relevant for lightweight agentic pipelines. Qwen 3 8B Instruct is the most capable of the three on current benchmark suites, particularly in multilingual evaluation sets covering CJK and Arabic. The 131K context window is a structural advantage the other two cannot match — Qwen 3 8B can process entire documents where Gemma 2 9B and Mistral 7B both need chunking strategies. At below $0.10 per million tokens on most platforms, it undercuts Gemma 2 9B on cost too. Pick Qwen 3 8B when multilingual coverage or long-context document processing matters. Pick Mistral 7B v0.3 when you need a battle-tested function-calling backbone at the absolute lowest cost tier and already have adapters tuned to its tokenizer. Pick Gemma 2 9B when Groq-hosted sub-10B latency is the primary constraint and 8K context is enough.