DeepSeek V4 Pro vs Qwen3.8-Max

DeepSeek V4 Pro in brief

DeepSeek V4 Pro specs and pricing: the MIT-licensed open-weights flagship with a 1M-token context, peak and off-peak API prices, and how it compares with Qwen3.8-Max.

• Open weights under the permissive MIT licence
• Very low API prices with 50% off-peak discounts
• 1M-token context

Qwen3.8-Max in brief

Qwen3.8-Max specs and pricing: Alibaba's 2.4T-parameter open-weights MoE flagship with a 1M-token context and $2/$6 API pricing, vs DeepSeek V4 Pro.

• Largest open-weights language model released to date
• 1M-token context
• 95B active parameters per token

Which should you choose?

Choose DeepSeek V4 Pro if you need: teams that need to self-host or fine-tune a frontier-class model; cost-sensitive api use; research on open models.

Choose Qwen3.8-Max if you need: organisations that want a frontier-scale model they can host; multilingual and chinese-language applications; research on very large open models.

For most teams the best test is your own workload: run the same 20–50 real prompts through both models and compare quality, speed and cost. Benchmarks published by labs use different settings and rarely match everyday use.