Qwen 3.5 397B-A17B — B300 vs H200 Performance per Dollar
Cost per million tokens of B300 (NVIDIA Blackwell) versus H200 (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.
B300: $0.21 per million tokens. H200: $0.69. Both at 68 tok/s/user on Qwen 3.5 397B-A17B, with B300 224% cheaper.
Around the middle of the 30–182 tok/s/user interactivity band — at 106 tok/s/user — B300 runs $0.40 per million tokens on Qwen 3.5 397B-A17B while H200 runs $0.95. B300 is the cheaper choice by 138%.
On Qwen 3.5 397B-A17B at 144 tok/s/user, the per-million math comes out to $0.60 for B300 and $1.19 for H200; B300 delivers 97% 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.)
Chip pricing (owning hyperscaler): B300 $2.26/chip/hr · H200 $1.22/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:$0.213H200:$0.691 | B300:$0.401H200:$0.954 | B300:$0.604H200:$1.188 |
| Concurrency | B300:~87H200:~29 | B300:~31H200:~13 | B300:~15H200:~8 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.