Qwen 3 8B Instruct is the more recent architecture and typically runs $0.05–0.10/1M tokens cheaper than [Gemma 2 9B IT](/models/google--gemma-2-9b-it) at most providers — a meaningful gap when you're doing millions of daily inference calls. Qwen 3 8B also ships with a 32K native context versus Gemma 2 9B's 8K, which matters before you hit chunking overhead. On MMLU, both models land in the 71–74% range; the gap is real but not decisive for general-purpose tasks. Gemma 2 9B IT earns its keep on structured-output workloads. Its bidirectional attention design reduces hallucination rates on extraction tasks — pulling entities, filling schemas, or running NER over noisy documents — compared to Qwen 3's decoder-only default. Teams running document-processing pipelines at 10M+ tokens/day have reported measurably lower retry rates on JSON schema validation. Qwen 3 8B Instruct wins on multilingual coverage: it was trained on a substantially larger multilingual corpus, and it shows on non-English instruction-following benchmarks. If you're routing Chinese, Japanese, Arabic, or Spanish traffic, [Qwen 3 8B Instruct](/models/alibaba--qwen-3-8b-instruct) is the obvious pick. It also handles longer agentic chains better — tool-call accuracy holds up past 8 turns where Gemma 2 9B starts drifting. **Pick Gemma 2 9B IT** if your workload is English-only structured extraction, JSON output, or classification and you want tighter schema adherence. **Pick Qwen 3 8B Instruct** if you need multilingual support, longer contexts, or agentic pipelines — and you want to save $0.05–0.10/1M tokens doing it.