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MiniMax M3 428B · Performance per Dollar

MiniMax M3 428B — GB200 NVL72 vs GB300 NVL72 Performance per Dollar

Cost per million tokens of GB200 NVL72 (NVIDIA Blackwell) versus GB300 NVL72 (NVIDIA Blackwell) on MiniMax M3 428B. Owning-hyperscaler TCO normalized by output 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.

Push MiniMax M3 428B to 52 tok/s/user and GB200 NVL72 lands at $0.32 per million tokens against GB300 NVL72's $0.31 — GB300 NVL72 pulls ahead by 6%.

GB200 NVL72: $0.78 per million tokens. GB300 NVL72: $0.97. Both at 86 tok/s/user on MiniMax M3 428B, with GB200 NVL72 23% cheaper.

Toward the upper edge of the 17–156 tok/s/user interactivity band — at 121 tok/s/user — GB200 NVL72 runs $2.42 per million tokens on MiniMax M3 428B while GB300 NVL72 runs $2.55. GB200 NVL72 is the cheaper choice by 5%. (Numbers reflect the default 1k/1k · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)

Chip pricing (owning hyperscaler): GB200 NVL72 $1.86/chip/hr · GB300 NVL72 $2.31/chip/hr. Source: SemiAnalysis Market July 2026 Pricing Surveys & AI Cloud TCO Model.

View full latency + throughput comparison →

MiniMax M3 428B: GB200 NVL72 versus GB300 NVL72 cost per million tokens at matched interactivity levels
GB200 NVL72 versus GB300 NVL72 cost per million tokens for this comparison's canonical default workload. Lower cost indicates better performance per dollar.
Interpolated from real benchmark data. Edit target interactivity values below to compare at different operating points.
Metric
Interactivity (tok/s/user)
Interactivity (tok/s/user)
Interactivity (tok/s/user)
Dollar per Million Tokens
GB200 NVL72:$0.323GB300 NVL72:$0.306
GB200 NVL72:$0.784GB300 NVL72:$0.967
GB200 NVL72:$2.416GB300 NVL72:$2.546
Concurrency
GB200 NVL72:~180GB300 NVL72:~452
GB200 NVL72:~76GB300 NVL72:~72
GB200 NVL72:~19GB300 NVL72:~21

Inference Performance

Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.

Cost per Million Total Tokens (Owning - Hyperscaler) vs. Interactivity

MiniMax M3 428B • FP4 • 8K / 1K • Source: SemiAnalysis InferenceX™

TCO $/chip/hr: VR200: 3.61H100: 1.17H200: 1.22B200: 1.73B300: 2.26GB200: 1.86GB300: 2.31MI300X: 0.95MI325X: 1.1MI355X: 1.5RTX6000PRO: 0.68Jalapeño (Teacup): 1.47

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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