DeepSeek R1 — H100 vs H200 Performance per Dollar
Cost per million tokens of H100 (NVIDIA Hopper) versus H200 (NVIDIA Hopper) on DeepSeek R1. 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 45 tok/s/user on DeepSeek R1, H100 costs $0.50 per million tokens; H200 costs $0.19. H200 is 162% more cost-efficient at this operating point.
H200 edges H100 at 71 tok/s/user on DeepSeek R1 — $0.35 per million tokens versus $1.23, a 249% cost-per-token gap.
Push DeepSeek R1 to 97 tok/s/user and H100 lands at $2.21 per million tokens against H200's $0.44 — H200 pulls ahead by 397%. (Numbers reflect the default 8k/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:$0.501H200:$0.191 | H100:$1.230H200:$0.352 | H100:$2.208H200:$0.445 |
| Concurrency | H100:~132H200:~115 | H100:~41H200:~127 | H100:~17H200:~7 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.