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: $0.12 per million tokens. GB300 NVL72: $0.08. Both at 98 tok/s/user on Qwen 3.5 397B-A17B, with GB300 NVL72 61% cheaper.
Around the middle of the 40–273 tok/s/user interactivity band — at 157 tok/s/user — B200 runs $0.18 per million tokens on Qwen 3.5 397B-A17B while GB300 NVL72 runs $0.20. B200 is the cheaper choice by 14%.
On Qwen 3.5 397B-A17B at 215 tok/s/user, the per-million math comes out to $0.24 for B200 and $0.42 for GB300 NVL72; B200 delivers 74% more output per dollar. (Numbers reflect the default 8k/1k · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
GPU pricing (owning hyperscaler): B200 $1.95/GPU/hr · GB300 NVL72 $2.65/GPU/hr. Source: SemiAnalysis Market August 2025 Pricing Surveys & AI Cloud TCO Model.
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Dollar per Million Tokens | B200:$0.123GB300 NVL72:$0.077 | B200:$0.176GB300 NVL72:$0.201 | B200:$0.243GB300 NVL72:$0.422 |
| Concurrency | B200:~22GB300 NVL72:~757 | B200:~10GB300 NVL72:~55 | B200:~5GB300 NVL72:~14 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.