GLM 5.3 744B — B200 vs B300 Performance per Dollar
Cost per million tokens of B200 (NVIDIA Blackwell) versus B300 (NVIDIA Blackwell) on GLM 5.3 744B. Large-hyperscaler-volume ownership TCO normalized by total 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.
AgentX replays real coding-agent sessions rather than fixed-length prompts, so context grows turn over turn and most of each request is served from cache instead of being recomputed. That turns the comparison into a systems question: KV transfer between nodes, prefix-aware routing, and cache capacity all move the curve alongside raw chip throughput. Fixed-sequence workloads stay the clean baseline for kernel and silicon performance, so the two scenarios answer different questions about the same hardware. Learn more about AgentX →
Push GLM 5.3 744B to 119 tok/s/user and B200 lands at $0.04 per million tokens against B300's $0.04 — B200 pulls ahead by 17%.
B200: $0.05 per million tokens. B300: $0.08. Both at 186 tok/s/user on GLM 5.3 744B, with B200 83% cheaper.
Toward the upper edge of the 52–319 tok/s/user interactivity band — at 253 tok/s/user — B200 runs $0.07 per million tokens on GLM 5.3 744B while B300 runs $0.11. B200 is the cheaper choice by 52%. (Numbers reflect the default agentic-traces · fp4 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:$0.036B300:$0.042 | B200:$0.045B300:$0.083 | B200:$0.070B300:$0.106 |
| Concurrency | B200:~12B300:~18 | B200:~6B300:~11 | B200:~3B300:~2 |
Inference Performance
Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.
No data available
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