Arctic Instruct and DBRX Instruct share a similar MoE design philosophy but land in very different spots on the capability-cost curve. Arctic runs 480B total parameters with roughly 17B active per token — Snowflake's bet on near-zero marginal cost through aggressive sparsity. DBRX activates 36B of its 132B total parameters per forward pass, giving it a denser, more expressive representation at the cost of higher VRAM pressure and typically higher per-token pricing. On standard instruction benchmarks, DBRX Instruct edges out Arctic on reasoning-heavy tasks — it trades blows with early Mixtral 8x7B numbers on MMLU while Arctic's scores cluster around smaller-dense-model territory. If you're paying per token, the gap matters: Arctic has historically priced below $1/1M tokens on commodity providers, while DBRX sits closer to $0.60–1.20/1M depending on the host. Check current provider rates on [Arctic Instruct's model page](/models/snowflake--arctic-instruct) before committing. Arctic's sweet spot is high-throughput, cost-sensitive classification or lightweight summarization where you're firing millions of requests and every tenth of a cent compounds. The sparse activation keeps latency low under load. DBRX performs better on multi-step reasoning, structured extraction, and enterprise Q&A that benefits from richer internal representations. It also ships with a 32K context window and a fully permissive Apache 2.0 license — useful if you need to redistribute outputs or fine-tune. See the [DBRX Instruct model page](/models/databricks--dbrx-instruct) for provider availability. **Pick Arctic** if throughput volume and cost floor matter more than reasoning depth. **Pick DBRX** if you need stronger reasoning and can absorb slightly higher inference costs.