Kimi K2.5/K2.6/K2.7-Code 1T — B300 vs GB200 NVL72 Performance per Dollar
Cost per million tokens of B300 (NVIDIA Blackwell) versus GB200 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.
GB200 NVL72 edges B300 at 62 tok/s/user on Kimi K2.5/K2.6/K2.7-Code 1T — $0.05 per million tokens versus $0.21, a 289% cost-per-token gap.
Push Kimi K2.5/K2.6/K2.7-Code 1T to 99 tok/s/user and B300 lands at $0.36 per million tokens against GB200 NVL72's $0.33 — GB200 NVL72 pulls ahead by 8%.
B300: $0.79 per million tokens. GB200 NVL72: $0.61. Both at 136 tok/s/user on Kimi K2.5/K2.6/K2.7-Code 1T, with GB200 NVL72 29% cheaper. (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): B300 $2.26/chip/hr · GB200 NVL72 $1.86/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 | B300:$0.213GB200 NVL72:$0.055 | B300:$0.362GB200 NVL72:$0.334 | B300:$0.792GB200 NVL72:$0.614 |
| Concurrency | B300:~21GB200 NVL72:~1229 | B300:~8GB200 NVL72:~256 | B300:~3GB200 NVL72:~28 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.