MiniMax M2.5/M2.7 — H100 vs H200 Performance per Dollar
Cost per million tokens of H100 (NVIDIA Hopper) versus H200 (NVIDIA Hopper) 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.
On MiniMax M2.5/M2.7 at 60 tok/s/user, the per-million math comes out to $0.53 for H100 and $0.38 for H200; H200 delivers 39% more output per dollar.
At 79 tok/s/user on MiniMax M2.5/M2.7, H100 costs $0.92 per million tokens; H200 costs $0.57. H200 is 61% more cost-efficient at this operating point.
H200 edges H100 at 98 tok/s/user on MiniMax M2.5/M2.7 — $0.91 per million tokens versus $1.56, a 72% 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.)
Chip pricing (owning hyperscaler): H100 $1.17/chip/hr · H200 $1.22/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 | H100:$0.534H200:$0.384 | H100:$0.920H200:$0.570 | H100:$1.563H200:$0.906 |
| Concurrency | H100:~41H200:~30 | H100:~18H200:~15 | H100:~9H200:~8 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.