MiniMax M2.5/M2.7 — B200 vs GB300 NVL72 Performance per Dollar
Cost per million tokens of B200 (NVIDIA Blackwell) versus GB300 NVL72 (NVIDIA Blackwell) on MiniMax M2.5/M2.7. 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 MiniMax M2.5/M2.7 at 69 tok/s/user, the per-million math comes out to $0.06 for B200 and $0.07 for GB300 NVL72; B200 delivers 21% more output per dollar.
At 108 tok/s/user on MiniMax M2.5/M2.7, B200 costs $0.17 per million tokens; GB300 NVL72 costs $0.21. B200 is 27% more cost-efficient at this operating point.
B200 edges GB300 NVL72 at 147 tok/s/user on MiniMax M2.5/M2.7 — $0.53 per million tokens versus $0.63, a 19% cost-per-token gap. (Numbers reflect the default 1k/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): 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:$0.060GB300 NVL72:$0.073 | B200:$0.165GB300 NVL72:$0.210 | B200:$0.535GB300 NVL72:$0.634 |
| Concurrency | B200:~711GB300 NVL72:~1024 | B200:~128GB300 NVL72:~121 | B200:~16GB300 NVL72:~22 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.