GLM 5/5.1 — B200 vs H200 Performance per Dollar
Cost per million tokens of B200 (NVIDIA Blackwell) versus H200 (NVIDIA Hopper) on GLM 5/5.1. 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 $1.03 and H200 $1.03 per million tokens at 41 tok/s/user on GLM 5/5.1: effectively the same cost.
On GLM 5/5.1 at 61 tok/s/user, the per-million math comes out to $1.23 for B200 and $1.60 for H200; B200 delivers 30% more output per dollar.
At 82 tok/s/user on GLM 5/5.1, B200 costs $1.75 per million tokens; H200 costs $2.54. B200 is 45% 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): B200 $1.73/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 | B200:$1.028H200:$1.030 | B200:$1.234H200:$1.598 | B200:$1.750H200:$2.538 |
| Concurrency | B200:~484H200:~34 | B200:~25H200:~14 | B200:~14H200:~7 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.