DeepSeek V4 Pro 1.6T — B200 vs B300 Performance per Dollar
Cost per million tokens of B200 (NVIDIA Blackwell) versus B300 (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. B300: $0.19. Both at 60 tok/s/user on DeepSeek V4 Pro 1.6T, with B200 188% cheaper.
Around the middle of the 6–226 tok/s/user interactivity band — at 116 tok/s/user — B200 runs $0.29 per million tokens on DeepSeek V4 Pro 1.6T while B300 runs $0.44. B200 is the cheaper choice by 52%.
On DeepSeek V4 Pro 1.6T at 171 tok/s/user, the per-million math comes out to $0.79 for B200 and $0.89 for B300; B200 delivers 12% 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 · B300 $2.26/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:$0.065B300:$0.188 | B200:$0.289B300:$0.438 | B200:$0.791B300:$0.886 |
| Concurrency | B200:~2668B300:~94 | B200:~146B300:~7 | B200:~31B300:~2 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.