GLM 5/5.1 — B200 vs B300 Performance per Dollar
Cost per million tokens of B200 (NVIDIA Blackwell) versus B300 (NVIDIA Blackwell) on GLM 5/5.1. 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: $0.29 per million tokens. B300: $0.49. Both at 37 tok/s/user on GLM 5/5.1, with B200 68% cheaper.
Around the middle of the 12–113 tok/s/user interactivity band — at 63 tok/s/user — B200 runs $0.43 per million tokens on GLM 5/5.1 while B300 runs $0.67. B200 is the cheaper choice by 56%.
On GLM 5/5.1 at 88 tok/s/user, the per-million math comes out to $0.62 for B200 and $0.92 for B300; B200 delivers 49% 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 · B300 $2.26/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:$0.295B300:$0.495 | B200:$0.428B300:$0.667 | B200:$0.617B300:$0.920 |
| Concurrency | B200:~216B300:~32 | B200:~17B300:~14 | B200:~9B300:~8 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.