gpt-oss 120B — H100 vs H200 Performance per Dollar
Cost per million tokens of H100 (NVIDIA Hopper) versus H200 (NVIDIA Hopper) on gpt-oss 120B. 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.
H100: $0.13 per million tokens. H200: $0.12. Both at 117 tok/s/user on gpt-oss 120B, with H200 4% cheaper.
Around the middle of the 67–266 tok/s/user interactivity band — at 166 tok/s/user — H100 runs $0.24 per million tokens on gpt-oss 120B while H200 runs $0.22. H200 is the cheaper choice by 9%.
On gpt-oss 120B at 216 tok/s/user, the per-million math comes out to $0.44 for H100 and $0.42 for H200; H200 delivers 4% more output per dollar. (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.)
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:$0.125H200:$0.120 | H100:$0.236H200:$0.216 | H100:$0.438H200:$0.419 |
| Concurrency | H100:~64H200:~64 | H100:~17H200:~43 | H100:~8H200:~8 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.