Muse Spark Open 816B — B200 vs GB300 NVL72 Performance per Dollar
Cost per million tokens of B200 (NVIDIA Blackwell) versus GB300 NVL72 (NVIDIA Blackwell) on Muse Spark Open 816B. 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 →
B200 $0.04 and GB300 NVL72 $0.04 per million tokens at 160 tok/s/user on Muse Spark Open 816B: effectively the same cost.
Push Muse Spark Open 816B to 236 tok/s/user and B200 lands at $0.08 per million tokens against GB300 NVL72's $0.08 — B200 pulls ahead by 7%.
B200: $0.13 per million tokens. GB300 NVL72: $0.12. Both at 313 tok/s/user on Muse Spark Open 816B, with GB300 NVL72 5% cheaper. (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 · GB300 NVL72 $2.31/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:$0.038GB300 NVL72:$0.039 | B200:$0.078GB300 NVL72:$0.084 | B200:$0.126GB300 NVL72:$0.120 |
| Concurrency | B200:~17GB300 NVL72:~12 | B200:~9GB300 NVL72:~5 | B200:~5GB300 NVL72:~3 |
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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