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 48 tok/s/user, the per-million math comes out to $0.06 for B200 and $0.13 for MI355X; B200 delivers 116% more output per dollar.
At 73 tok/s/user on Kimi K2.5/K2.6/K2.7-Code 1T, B200 costs $0.18 per million tokens; MI355X costs $0.18. B200 is 2% more cost-efficient at this operating point.
MI355X edges B200 at 98 tok/s/user on Kimi K2.5/K2.6/K2.7-Code 1T — $0.25 per million tokens versus $0.27, a 9% cost-per-token gap. (Numbers reflect the default 8k/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:$0.059MI355X:$0.126 | B200:$0.175MI355X:$0.178 | B200:$0.271MI355X:$0.250 |
| Concurrency | B200:~319MI355X:~31 | B200:~156MI355X:~15 | B200:~89MI355X:~8 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.