[Llama 3.1 8B Instruct](/models/meta--llama-3.1-8b-instruct) and [Qwen 3 8B Instruct](/models/alibaba--qwen-3-8b-instruct) are matched in parameter count but differ substantially in capability profile and architecture choices. Both run in the $0.05–$0.20/M input token range with similar throughput — 100–180 tok/s on A10G hardware. The key differentiator is Qwen 3's hybrid thinking mode: the model can dynamically allocate chain-of-thought compute at inference time, controlled by a `enable_thinking` flag, without requiring a separate endpoint deployment. On reasoning benchmarks — MATH, AIME, and similar — Qwen 3 8B outperforms Llama 3.1 8B by 8–15 points when thinking mode is enabled. Standard (non-thinking) mode performance is broadly comparable on general instruction tasks, with Llama 3.1 8B retaining an edge on English instruction-following evals and Qwen 3 8B stronger on East Asian language tasks. **Where Llama 3.1 8B wins:** English-dominant deployments where instruction-following precision matters and the broader Western provider ecosystem is an operational requirement. Llama's toolchain maturity and fine-tuning recipe availability also make it easier to adapt for domain-specific use cases. **Where Qwen 3 8B wins:** Math-intensive pipelines, multilingual APIs serving Chinese or Japanese users, and any workload that benefits from on-demand reasoning without running a separate large model. The thinking mode makes it a practical budget alternative to models 3–5× its size. **Bottom line:** Pick Llama 3.1 8B Instruct for English-first, general-purpose APIs. Pick Qwen 3 8B Instruct when you need stronger mathematical reasoning or multilingual quality at the same cost tier.