MiniMax M3 428B — GB200 NVL72 vs GB300 NVL72
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 21416 tok/s/chip for $0.02 per million tokens at 135 tok/s/user on MiniMax M3 428B; GB300 NVL72 posts 38572 tok/s/chip for $0.02. GB300 NVL72 is 45% cheaper per token; GB300 NVL72 delivers 80% more tok/s/chip.
Throughput at 171 tok/s/user on MiniMax M3 428B: GB200 NVL72 hits 12029 tok/s/chip, GB300 NVL72 hits 29597. Per-million costs land at $0.04 and $0.02 respectively. GB300 NVL72 is 98% cheaper per token; GB300 NVL72 delivers 146% more tok/s/chip.
GB200 NVL72 / GB300 NVL72 on MiniMax M3 428B at 206 tok/s/user: 6887 / 23674 tok/s/chip, $0.08 / $0.03 per million tokens. GB300 NVL72 is 177% cheaper per token; GB300 NVL72 delivers 244% more tok/s/chip. (Numbers reflect the default agentic-traces · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | GB200 NVL72:21416.4GB300 NVL72:38571.8 | GB200 NVL72:12028.5GB300 NVL72:29597.2 | GB200 NVL72:6886.8GB300 NVL72:23673.8 |
| Cost ($/M tok) | GB200 NVL72:$0.024GB300 NVL72:$0.017 | GB200 NVL72:$0.043GB300 NVL72:$0.022 | GB200 NVL72:$0.075GB300 NVL72:$0.027 |
| tok/s/MW | GB200 NVL72:11452639GB300 NVL72:18194243 | GB200 NVL72:6432374GB300 NVL72:13960952 | GB200 NVL72:3682761GB300 NVL72:11166907 |
| Concurrency | GB200 NVL72:~15GB300 NVL72:~48 | GB200 NVL72:~8GB300 NVL72:~25 | GB200 NVL72:~4GB300 NVL72:~20 |
Total Tokens per $1 USD (Owning - Neocloud Giant) vs. Interactivity
MiniMax M3 428B • FP4 • 8K / 1K • Source: SemiAnalysis InferenceX™
MiniMax M3 428B • FP4 • 8K / 1K • Source: SemiAnalysis InferenceX™TCO $/chip/hr: VR200: 3.61H100: 1.55H200: 1.59B200: 2.07B300: 2.52GB200: 2.26GB300: 2.79MI300X: 1.16MI325X: 1.32MI355X: 2.09RTX6000PRO: 0.75Jalapeño (Teacup): 1.56
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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Interactivity is the rate at which a single user receives generated tokens while the model streams its answer — how quickly new words appear on screen. Higher values feel snappier; operators trade it against batch throughput.
How many total tokens (input + output) one US dollar of infrastructure spend buys, priced with the all-in hourly ownership cost of a Neocloud Giant operator. It is the reciprocal of cost per token, so higher means cheaper.
Formula: tok/$ = (total tok/s/chip × 3,600) ÷ all-in cost per chip-hour ($)