MiniMax M3 428B — B200 vs H200 Performance per Dollar
Cost per million tokens of B200 (NVIDIA Blackwell) versus H200 (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.
On MiniMax M3 428B at 82 tok/s/user, the per-million math comes out to $0.32 for B200 and $0.58 for H200; B200 delivers 80% more output per dollar.
At 152 tok/s/user on MiniMax M3 428B, B200 costs $0.52 per million tokens; H200 costs $1.16. B200 is 125% more cost-efficient at this operating point.
B200 edges H200 at 223 tok/s/user on MiniMax M3 428B — $1.22 per million tokens versus $2.43, a 99% cost-per-token gap. (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.)
GPU pricing (owning hyperscaler): B200 $1.95/GPU/hr · H200 $1.41/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:$0.324H200:$0.584 | B200:$0.515H200:$1.161 | B200:$1.219H200:$2.429 |
| Concurrency | B200:~62H200:~18 | B200:~16H200:~4 | B200:~4H200:~3 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.