[DeepSeek V3.2](/models/deepseek--deepseek-v3.2) is a 671B sparse MoE activating ~37B parameters per token, with roughly 30% lower inference cost than DeepSeek V3 at most providers — typically landing under $0.50/M input tokens on the cheapest tiers. [Llama 3.3 70B Instruct](/models/meta--llama-3.3-70b-instruct) is a dense 70B model representing Meta's strongest instruction-following checkpoint at the 70B scale, available on every major hosted-inference provider with rates often under $0.20/M input tokens. Despite the 10× total-parameter gap, benchmark comparisons are tighter than expected. Llama 3.3 70B substantially outperforms its predecessors on instruction-following, and Meta's training improvements at 70B compressed a lot of reasoning quality into a small footprint. DeepSeek V3.2 holds clear advantages on complex multi-step reasoning, long-context synthesis, and code generation tasks — but the margin shrinks on simpler instruction-following benchmarks where Llama 3.3 70B punches above its weight class. Llama 3.3 70B wins on latency-sensitive workloads: dense 70B inference on modern hardware delivers first-token latency in the tens of milliseconds, and provider coverage is near-universal — you can run it on any hyperscaler, regional inference provider, or local hardware. For real-time chat, low-latency classification, and applications where P95 latency matters more than reasoning depth, the 70B's predictable dense-model performance is a practical advantage. DeepSeek V3.2 is the right call for long-context reasoning, complex code generation, or any task where output quality measurably degrades with the 70B ceiling — at MoE economics that make 671B total capacity financially viable. Pick V3.2 if you need long-context reasoning at MoE economics. Pick Llama 3.3 70B if you need predictable dense-model latency, broad provider coverage, and the Llama license is acceptable.