Kimi K2.5/K2.6/K2.7-Code 1T — B200 vs GB300 NVL72 Performance per Dollar
Cost per million tokens of B200 (NVIDIA Blackwell) versus GB300 NVL72 (NVIDIA Blackwell) on Kimi K2.5/K2.6/K2.7-Code 1T. 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 Kimi K2.5/K2.6/K2.7-Code 1T at 63 tok/s/user, the per-million math comes out to $0.14 for B200 and $0.07 for GB300 NVL72; GB300 NVL72 delivers 90% more output per dollar.
At 103 tok/s/user on Kimi K2.5/K2.6/K2.7-Code 1T, B200 costs $0.30 per million tokens; GB300 NVL72 costs $0.44. B200 is 44% more cost-efficient at this operating point.
B200 edges GB300 NVL72 at 143 tok/s/user on Kimi K2.5/K2.6/K2.7-Code 1T — $0.93 per million tokens versus $1.00, a 8% 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.)
GPU pricing (owning hyperscaler): B200 $1.95/GPU/hr · GB300 NVL72 $2.65/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:$0.137GB300 NVL72:$0.072 | B200:$0.305GB300 NVL72:$0.438 | B200:$0.926GB300 NVL72:$0.998 |
| Concurrency | B200:~204GB300 NVL72:~1202 | B200:~77GB300 NVL72:~68 | B200:~10GB300 NVL72:~26 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.