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 61 tok/s/user, the per-million math comes out to $0.16 for B200 and $1.43 for H100; B200 delivers 808% more output per dollar.
At 78 tok/s/user on Qwen 3.5 397B-A17B, B200 costs $0.21 per million tokens; H100 costs $1.78. B200 is 740% more cost-efficient at this operating point.
B200 edges H100 at 94 tok/s/user on Qwen 3.5 397B-A17B — $0.28 per million tokens versus $2.38, a 760% cost-per-token gap. (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 · H100 $1.30/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:$0.157H100:$1.429 | B200:$0.212H100:$1.779 | B200:$0.277H100:$2.381 |
| Concurrency | B200:~123H100:~17 | B200:~70H100:~10 | B200:~46H100:~7 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.