Head-to-head AI inference benchmark comparison of B300 (NVIDIA Blackwell) and GB200 NVL72 (NVIDIA Blackwell) on MiniMax M3 428B. Latency, throughput, and cost across LLM workloads. 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 →
B300 posts 41655 tok/s/chip for $0.02 per million tokens at 147 tok/s/user on MiniMax M3 428B; GB200 NVL72 posts 22704 tok/s/chip for $0.02. B300 is 51% cheaper per token; B300 delivers 83% more tok/s/chip.
Throughput at 228 tok/s/user on MiniMax M3 428B: B300 hits 21272 tok/s/chip, GB200 NVL72 hits 15803. Per-million costs land at $0.03 and $0.03 respectively. B300 is 11% cheaper per token; B300 delivers 35% more tok/s/chip.
B300 / GB200 NVL72 on MiniMax M3 428B at 309 tok/s/user: 7413 / 9677 tok/s/chip, $0.08 / $0.05 per million tokens. GB200 NVL72 is 59% cheaper per token; GB200 NVL72 delivers 31% more tok/s/chip. (Numbers reflect the default agentic-traces · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | B300:41655.3GB200 NVL72:22703.6 | B300:21272.2GB200 NVL72:15802.6 | B300:7413.0GB200 NVL72:9676.9 |
| Cost ($/M tok) | B300:$0.015GB200 NVL72:$0.023 | B300:$0.030GB200 NVL72:$0.033 | B300:$0.085GB200 NVL72:$0.053 |
| tok/s/MW | B300:21923832GB200 NVL72:12140986 | B300:11195877GB200 NVL72:8450576 | B300:3901566GB200 NVL72:5174820 |
| Concurrency | B300:~25GB200 NVL72:~15 | B300:~15GB200 NVL72:~9 | B300:~3GB200 NVL72:~3 |
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