MiniMax M3 428B — B200 vs B300 Performance per Dollar
Cost per million tokens of B200 (NVIDIA Blackwell) versus B300 (NVIDIA Blackwell) 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.
At 111 tok/s/user on MiniMax M3 428B, B200 costs $0.26 per million tokens; B300 costs $0.26. B300 is 2% more cost-efficient at this operating point.
B300 edges B200 at 207 tok/s/user on MiniMax M3 428B — $0.64 per million tokens versus $0.68, a 7% cost-per-token gap.
Push MiniMax M3 428B to 304 tok/s/user and B200 lands at $1.90 per million tokens against B300's $1.22 — B300 pulls ahead by 56%. (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:$0.264B300:$0.258 | B200:$0.681B300:$0.638 | B200:$1.901B300:$1.221 |
| Concurrency | B200:~58B300:~104 | B200:~8B300:~11 | B200:~1B300:~2 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.