Qwen 3.5 397B-A17B — B200 vs H100 Performance per Dollar
Cost per million tokens of B200 (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.
On Qwen 3.5 397B-A17B at 56 tok/s/user, the per-million math comes out to $0.05 for B200 and $0.15 for H100; B200 delivers 198% more output per dollar.
At 102 tok/s/user on Qwen 3.5 397B-A17B, B200 costs $0.07 per million tokens; H100 costs $0.21. B200 is 197% more cost-efficient at this operating point.
B200 edges H100 at 147 tok/s/user on Qwen 3.5 397B-A17B — $0.09 per million tokens versus $0.30, a 215% cost-per-token gap. (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.)
Chip pricing (owning hyperscaler): B200 $1.73/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 | B200:$0.052H100:$0.154 | B200:$0.071H100:$0.211 | B200:$0.094H100:$0.297 |
| Concurrency | B200:~79H100:~37 | B200:~33H100:~14 | B200:~17H100:~7 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.