Qwen 3.5 397B-A17B — B200 vs GB300 NVL72 Performance per Dollar
Cost per million tokens of B200 (NVIDIA Blackwell) versus GB300 NVL72 (NVIDIA Blackwell) on Qwen 3.5 397B-A17B. 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: $0.06 per million tokens. GB300 NVL72: $0.03. Both at 113 tok/s/user on Qwen 3.5 397B-A17B, with GB300 NVL72 85% cheaper.
Around the middle of the 44–320 tok/s/user interactivity band — at 182 tok/s/user — B200 runs $0.10 per million tokens on Qwen 3.5 397B-A17B while GB300 NVL72 runs $0.06. GB300 NVL72 is the cheaper choice by 51%.
On Qwen 3.5 397B-A17B at 252 tok/s/user, the per-million math comes out to $0.16 for B200 and $0.13 for GB300 NVL72; GB300 NVL72 delivers 28% 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:$0.061GB300 NVL72:$0.033 | B200:$0.095GB300 NVL72:$0.063 | B200:$0.163GB300 NVL72:$0.127 |
| Concurrency | B200:~21GB300 NVL72:~1030 | B200:~7GB300 NVL72:~459 | B200:~4GB300 NVL72:~91 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.