Qwen 3.5 397B-A17B — B200 vs MI300X Performance per Dollar
Cost per million tokens of B200 (NVIDIA Blackwell) versus MI300X (AMD CDNA 3) on Qwen 3.5 397B-A17B. 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.
B200: $0.04 per million tokens. MI300X: $0.25. Both at 34 tok/s/user on Qwen 3.5 397B-A17B, with B200 488% cheaper.
Around the middle of the 23–66 tok/s/user interactivity band — at 45 tok/s/user — B200 runs $0.05 per million tokens on Qwen 3.5 397B-A17B while MI300X runs $0.37. B200 is the cheaper choice by 687%.
On Qwen 3.5 397B-A17B at 56 tok/s/user, the per-million math comes out to $0.05 for B200 and $0.59 for MI300X; B200 delivers 1036% more output per dollar. (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): B200 $1.73/chip/hr · MI300X $0.95/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 | B200:$0.043MI300X:$0.253 | B200:$0.047MI300X:$0.372 | B200:$0.052MI300X:$0.586 |
| Concurrency | B200:~155MI300X:~29 | B200:~105MI300X:~15 | B200:~79MI300X:~8 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.