MiniMax M3.1 Flash 428B — B200 vs H100 Performance per Dollar
Cost per million tokens of B200 (NVIDIA Blackwell) versus H100 (NVIDIA Hopper) on MiniMax M3.1 Flash 428B. 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 86 tok/s/user on MiniMax M3.1 Flash 428B — $0.01 per million tokens versus $0.06, a 598% cost-per-token gap.
Push MiniMax M3.1 Flash 428B to 153 tok/s/user and B200 lands at $0.01 per million tokens against H100's $0.07 — B200 pulls ahead by 794%.
B200: $0.01 per million tokens. H100: $0.09. Both at 221 tok/s/user on MiniMax M3.1 Flash 428B, with B200 606% 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:$0.008H100:$0.057 | B200:$0.008H100:$0.073 | B200:$0.013H100:$0.092 |
| Concurrency | B200:~32H100:~9 | B200:~32H100:~7 | B200:~20H100:~3 |
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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