MiniMax M3 428B — B200 vs H100 Performance per Dollar
Cost per million tokens of B200 (NVIDIA Blackwell) versus H100 (NVIDIA Hopper) on MiniMax M3 428B. 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.
Near the low end of the 15–260 tok/s/user interactivity band — at 76 tok/s/user — B200 runs $0.23 per million tokens on MiniMax M3 428B while H100 runs $0.61. B200 is the cheaper choice by 159%.
On MiniMax M3 428B at 138 tok/s/user, the per-million math comes out to $0.40 for B200 and $1.37 for H100; B200 delivers 241% more output per dollar.
At 199 tok/s/user on MiniMax M3 428B, B200 costs $0.87 per million tokens; H100 costs $2.08. B200 is 140% more cost-efficient at this operating point. (Numbers reflect the default 1k/1k · fp8 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 · H100 $1.17/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:$0.234H100:$0.605 | B200:$0.400H100:$1.366 | B200:$0.866H100:$2.076 |
| Concurrency | B200:~119H100:~31 | B200:~18H100:~8 | B200:~5H100:~4 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.