Head-to-head AI inference benchmark comparison of GB200 NVL72 (NVIDIA Blackwell) and GB300 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 →
GB200 NVL72 posts 20905 tok/s/chip for $0.02 per million tokens at 159 tok/s/user on MiniMax M3 428B; GB300 NVL72 posts 32568 tok/s/chip for $0.02. GB300 NVL72 is 25% cheaper per token; GB300 NVL72 delivers 56% more tok/s/chip.
Throughput at 218 tok/s/user on MiniMax M3 428B: GB200 NVL72 hits 16598 tok/s/chip, GB300 NVL72 hits 21832. Per-million costs land at $0.03 and $0.03 respectively. GB300 NVL72 is 6% cheaper per token; GB300 NVL72 delivers 32% more tok/s/chip.
GB200 NVL72 / GB300 NVL72 on MiniMax M3 428B at 277 tok/s/user: 11991 / 13103 tok/s/chip, $0.04 / $0.05 per million tokens. GB200 NVL72 is 14% cheaper per token; GB300 NVL72 delivers 9% more tok/s/chip. (Numbers reflect the default agentic-traces · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | GB200 NVL72:20904.9GB300 NVL72:32567.6 | GB200 NVL72:16598.2GB300 NVL72:21832.5 | GB200 NVL72:11991.1GB300 NVL72:13103.4 |
| Cost ($/M tok) | GB200 NVL72:$0.025GB300 NVL72:$0.020 | GB200 NVL72:$0.031GB300 NVL72:$0.029 | GB200 NVL72:$0.043GB300 NVL72:$0.049 |
| tok/s/MW | GB200 NVL72:11179072GB300 NVL72:15362054 | GB200 NVL72:8876065GB300 NVL72:10298334 | GB200 NVL72:6412328GB300 NVL72:6180856 |
| Concurrency | GB200 NVL72:~14GB300 NVL72:~29 | GB200 NVL72:~9GB300 NVL72:~20 | GB200 NVL72:~5GB300 NVL72:~24 |
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.
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MiniMax M3 428B 8K / 1K Total Tokens per $1 TCO vs. Interactivity