Qwen 3.5 397B-A17B — B300 vs H100 Performance per Dollar
Cost per million tokens of B300 (NVIDIA Blackwell) versus H100 (NVIDIA Hopper) 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.
Near the low end of the 30–171 tok/s/user interactivity band — at 65 tok/s/user — B300 runs $0.20 per million tokens on Qwen 3.5 397B-A17B while H100 runs $0.77. B300 is the cheaper choice by 280%.
On Qwen 3.5 397B-A17B at 100 tok/s/user, the per-million math comes out to $0.37 for B300 and $1.04 for H100; B300 delivers 182% more output per dollar.
At 136 tok/s/user on Qwen 3.5 397B-A17B, B300 costs $0.56 per million tokens; H100 costs $1.40. B300 is 149% more cost-efficient at this operating point. (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.)
Chip pricing (owning hyperscaler): B300 $2.26/chip/hr · H100 $1.17/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 | B300:$0.204H100:$0.775 | B300:$0.368H100:$1.036 | B300:$0.561H100:$1.397 |
| Concurrency | B300:~96H100:~27 | B300:~36H100:~13 | B300:~18H100:~7 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.