Muse Spark Open 816B — B200 vs H100 Performance per Dollar
Cost per million tokens of B200 (NVIDIA Blackwell) versus H100 (NVIDIA Hopper) 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 edges H100 at 105 tok/s/user on Muse Spark Open 816B — $0.02 per million tokens versus $0.25, a 1020% cost-per-token gap.
Push Muse Spark Open 816B to 125 tok/s/user and B200 lands at $0.03 per million tokens against H100's $0.25 — B200 pulls ahead by 868%.
B200: $0.03 per million tokens. H100: $0.25. Both at 146 tok/s/user on Muse Spark Open 816B, with B200 712% 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 · H100 $1.17/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:$0.022H100:$0.247 | B200:$0.026H100:$0.247 | B200:$0.030H100:$0.247 |
| Concurrency | B200:~38H100:~1 | B200:~30H100:~1 | B200:~22H100:~1 |
Inference Performance
Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.
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