Kimi K2.5/K2.6/K2.7-Code 1T — B200 vs MI355X Performance per Dollar
Cost per million tokens of B200 (NVIDIA Blackwell) versus MI355X (AMD CDNA 4) on Kimi K2.5/K2.6/K2.7-Code 1T. 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 Kimi K2.5/K2.6/K2.7-Code 1T at 58 tok/s/user, the per-million math comes out to $0.55 for B200 and $0.44 for MI355X; MI355X delivers 24% more output per dollar.
At 82 tok/s/user on Kimi K2.5/K2.6/K2.7-Code 1T, B200 costs $0.90 per million tokens; MI355X costs $0.64. MI355X is 40% more cost-efficient at this operating point.
MI355X edges B200 at 105 tok/s/user on Kimi K2.5/K2.6/K2.7-Code 1T — $0.88 per million tokens versus $1.39, a 58% cost-per-token gap. (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 · MI355X $1.48/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:$0.548MI355X:$0.443 | B200:$0.901MI355X:$0.645 | B200:$1.387MI355X:$0.878 |
| Concurrency | B200:~37MI355X:~33 | B200:~15MI355X:~16 | B200:~8MI355X:~9 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.