GLM 5/5.1 — GB200 NVL72 vs GB300 NVL72 Performance per Dollar
Cost per million tokens of GB200 NVL72 (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 67 tok/s/user, the per-million math comes out to $0.06 for GB200 NVL72 and $0.06 for GB300 NVL72; GB200 NVL72 delivers 11% more output per dollar.
At 104 tok/s/user on GLM 5/5.1, GB200 NVL72 costs $0.14 per million tokens; GB300 NVL72 costs $0.15. GB200 NVL72 is 2% more cost-efficient at this operating point.
GB300 NVL72 edges GB200 NVL72 at 141 tok/s/user on GLM 5/5.1 — $0.77 per million tokens versus $0.82, a 7% cost-per-token gap. (Numbers reflect the default 8k/1k · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
Chip pricing (owning hyperscaler): GB200 NVL72 $1.86/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 | GB200 NVL72:$0.057GB300 NVL72:$0.063 | GB200 NVL72:$0.143GB300 NVL72:$0.146 | GB200 NVL72:$0.820GB300 NVL72:$0.766 |
| Concurrency | GB200 NVL72:~698GB300 NVL72:~749 | GB200 NVL72:~231GB300 NVL72:~276 | GB200 NVL72:~62GB300 NVL72:~23 |
Inference Performance
Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.