GLM 5/5.1 — B200 vs GB300 NVL72 Performance per Dollar
Cost per million tokens of B200 (NVIDIA Blackwell) versus GB300 NVL72 (NVIDIA Blackwell) 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.
On GLM 5/5.1 at 43 tok/s/user, the per-million math comes out to $1.05 for B200 and $0.36 for GB300 NVL72; GB300 NVL72 delivers 193% more output per dollar.
At 67 tok/s/user on GLM 5/5.1, B200 costs $1.36 per million tokens; GB300 NVL72 costs $1.30. GB300 NVL72 is 5% more cost-efficient at this operating point.
B200 edges GB300 NVL72 at 91 tok/s/user on GLM 5/5.1 — $2.04 per million tokens versus $3.61, a 77% 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.)
Chip pricing (owning hyperscaler): B200 $1.73/chip/hr · GB300 NVL72 $2.31/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:$1.047GB300 NVL72:$0.357 | B200:$1.359GB300 NVL72:$1.297 | B200:$2.039GB300 NVL72:$3.608 |
| Concurrency | B200:~424GB300 NVL72:~3177 | B200:~21GB300 NVL72:~307 | B200:~11GB300 NVL72:~79 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.