Head-to-head AI inference benchmark comparison of B200 (NVIDIA Blackwell) and Vera Rubin NVL72 (NVIDIA Vera Rubin) on MiniMax M3 428B. Latency, throughput, and cost across LLM workloads. Use the chart controls below to switch sequences, precisions, and metrics β 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 β
B200 posts 25810 tok/s/chip for $0.02 per million tokens at 173 tok/s/user on MiniMax M3 428B; Vera Rubin NVL72 posts 104132 tok/s/chip for $0.01. Vera Rubin NVL72 is 93% cheaper per token; Vera Rubin NVL72 delivers 303% more tok/s/chip.
Throughput at 260 tok/s/user on MiniMax M3 428B: B200 hits 15223 tok/s/chip, Vera Rubin NVL72 hits 66780. Per-million costs land at $0.03 and $0.02 respectively. Vera Rubin NVL72 is 110% cheaper per token; Vera Rubin NVL72 delivers 339% more tok/s/chip.
B200 / Vera Rubin NVL72 on MiniMax M3 428B at 348 tok/s/user: 7209 / 42806 tok/s/chip, $0.07 / $0.02 per million tokens. Vera Rubin NVL72 is 185% cheaper per token; Vera Rubin NVL72 delivers 494% more tok/s/chip. (Numbers reflect the default agentic-traces Β· fp4 selection for this URL β table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | B200:25809.8Vera Rubin NVL72:104132.1 | B200:15222.9Vera Rubin NVL72:66780.0 | B200:7209.4Vera Rubin NVL72:42806.0 |
| Cost ($/M tok) | B200:$0.019Vera Rubin NVL72:$0.010 | B200:$0.032Vera Rubin NVL72:$0.015 | B200:$0.067Vera Rubin NVL72:$0.023 |
| tok/s/MW | B200:15093470Vera Rubin NVL72:31555182 | B200:8902309Vera Rubin NVL72:20236353 | B200:4216025Vera Rubin NVL72:12971515 |
| Concurrency | B200:~17Vera Rubin NVL72:~34 | B200:~9Vera Rubin NVL72:~20 | B200:~2Vera Rubin NVL72:~33 |
Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.
TCO $/chip/hr:
Source: SemiAnalysis Market July 2026 Pricing Surveys & AI Cloud TCO Model
Note: Disaggregated inference configurations (e.g., MoRI SGLang, Dynamo TRTLLM) calculate cost per million tokens per decode chip or per prefill chip, rather than per total chip count. This makes direct token cost comparison with aggregated configs not an apples-to-apples comparison.
Note: Disaggregated inference configurations (e.g., MoRI SGLang, Dynamo TRTLLM) calculate input throughput per decode chip or per prefill chip, rather than per total chip count. This makes direct input throughput comparison with aggregated configs not an apples-to-apples comparison.
Note: Disaggregated inference configurations (e.g., MoRI SGLang, Dynamo TRTLLM) calculate output throughput per decode chip or per prefill chip, rather than per total chip count. This makes direct output throughput comparison with aggregated configs not an apples-to-apples comparison.
Note: Disaggregated inference configurations (e.g., MoRI SGLang, Dynamo TRTLLM) calculate power per decode chip or per prefill chip, rather than per total chip count. This makes direct power comparison with aggregated configs not an apples-to-apples comparison.
Note: Disaggregated inference configurations (e.g., MoRI SGLang, Dynamo TRTLLM) calculate Joules per decode chip or per prefill chip, rather than per total chip count. This makes direct Joules per token comparison with aggregated configs not an apples-to-apples comparison.
Matching measurements exist, but their chip series are hidden.
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MiniMax M3 428B 8K / 1K Total Tokens per $1 TCO vs. Interactivity