MiniMax M2.5/M2.7 — B200 vs B300 Performance per Dollar
Cost per million tokens of B200 (NVIDIA Blackwell) versus B300 (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.
B200 edges B300 at 68 tok/s/user on MiniMax M2.5/M2.7 — $0.09 per million tokens versus $0.10, a 18% cost-per-token gap.
Push MiniMax M2.5/M2.7 to 111 tok/s/user and B200 lands at $0.26 per million tokens against B300's $0.32 — B200 pulls ahead by 20%.
B200: $0.64 per million tokens. B300: $0.74. Both at 154 tok/s/user on MiniMax M2.5/M2.7, with B200 14% cheaper. (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.)
GPU pricing (owning hyperscaler): B200 $1.95/GPU/hr · B300 $2.34/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:$0.087B300:$0.103 | B200:$0.264B300:$0.317 | B200:$0.644B300:$0.736 |
| Concurrency | B200:~128B300:~128 | B200:~16B300:~17 | B200:~13B300:~6 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.