DeepSeek V4 Pro 1.6T — B200 vs GB300 NVL72 Performance per Dollar
Cost per million tokens of B200 (NVIDIA Blackwell) versus GB300 NVL72 (NVIDIA Blackwell) on DeepSeek V4 Pro 1.6T. Owning-hyperscaler TCO normalized by output tokens — performance per dollar across LLM workloads. Pick the more cost-efficient SKU at every target interactivity level. Use the chart controls below to switch sequences, precisions, and metrics — same interactions as the main inference chart.
B200: $0.07 per million tokens. GB300 NVL72: $0.07. Both at 66 tok/s/user on DeepSeek V4 Pro 1.6T, with GB300 NVL72 12% cheaper.
Around the middle of the 13–226 tok/s/user interactivity band — at 120 tok/s/user — B200 runs $0.31 per million tokens on DeepSeek V4 Pro 1.6T while GB300 NVL72 runs $0.16. GB300 NVL72 is the cheaper choice by 91%.
On DeepSeek V4 Pro 1.6T at 173 tok/s/user, the per-million math comes out to $0.79 for B200 and $0.72 for GB300 NVL72; GB300 NVL72 delivers 11% more output per dollar. (Numbers reflect the default 8k/1k · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
Chip pricing (owning hyperscaler): B200 $1.73/chip/hr · GB300 NVL72 $2.31/chip/hr. Source: SemiAnalysis Market July 2026 Pricing Surveys & AI Cloud TCO Model.
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Dollar per Million Tokens | B200:$0.073GB300 NVL72:$0.065 | B200:$0.315GB300 NVL72:$0.165 | B200:$0.795GB300 NVL72:$0.717 |
| Concurrency | B200:~935GB300 NVL72:~1026 | B200:~133GB300 NVL72:~320 | B200:~32GB300 NVL72:~29 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.