Qwen 3.5 397B-A17B — B200 vs B300 Performance per Dollar
Cost per million tokens of B200 (NVIDIA Blackwell) versus B300 (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: $0.24 per million tokens. B300: $0.30. Both at 87 tok/s/user on Qwen 3.5 397B-A17B, with B200 26% cheaper.
Around the middle of the 31–255 tok/s/user interactivity band — at 143 tok/s/user — B200 runs $0.53 per million tokens on Qwen 3.5 397B-A17B while B300 runs $0.62. B200 is the cheaper choice by 17%.
On Qwen 3.5 397B-A17B at 199 tok/s/user, the per-million math comes out to $0.84 for B200 and $0.96 for B300; B200 delivers 14% more output per dollar. (Numbers reflect the default 1k/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 · B300 $2.34/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:$0.241B300:$0.303 | B200:$0.529B300:$0.620 | B200:$0.838B300:$0.959 |
| Concurrency | B200:~58B300:~51 | B200:~16B300:~15 | B200:~7B300:~7 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.