Both DBRX and Mixtral 8x22B use mixture-of-experts architectures, but the numbers diverge meaningfully. Mixtral 8x22B activates 39B of its 141B total parameters per token; DBRX activates 36B of 132B. Near-identical active parameter counts, but Mixtral 8x22B tends to benchmark slightly higher on reasoning tasks — on MMLU it scores around 77–78%, while DBRX sits a few points lower. The gap isn't dramatic, but Mixtral 8x22B also ships with a 65K context window versus DBRX's 32K, which is a practical advantage for document-heavy workloads. Mixtral 8x22B also has broader provider availability and typically runs at lower cost per token due to Mistral's open licensing and the competitive market it's spawned. Check current rates for both options on [DBRX Instruct's model page](/models/databricks--dbrx-instruct). For long-context RAG pipelines — where you're feeding 40–60K tokens of retrieved enterprise documents into a single prompt — Mixtral 8x22B's larger context window is a direct functional advantage. You can batch more retrieved chunks per request, reducing round trips and improving coherence. DBRX Instruct's advantage is its tight integration with the Databricks ecosystem. If your inference stack runs on Databricks Model Serving or Unity Catalog, DBRX benefits from native optimization that generic providers may not replicate. For teams already in the Databricks data platform, that operational simplicity can outweigh raw benchmark differences. Review Mixtral 8x22B's provider coverage on [its model page](/models/mistralai--mixtral-8x22b-instruct). **Pick Mixtral 8x22B Instruct** for broader provider choice, longer context, and slightly stronger general benchmarks. **Pick DBRX Instruct** if you're already on the Databricks platform.