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MiniMax M2.5/M2.7 · Chip comparison

MiniMax M2.5/M2.7 — GB200 NVL72 vs H200

Head-to-head AI inference benchmark comparison of GB200 NVL72 (NVIDIA Blackwell) and H200 (NVIDIA Hopper) on MiniMax M2.5/M2.7. 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.

GB200 NVL72 posts 7412 tok/s/chip for $0.07 per million tokens at 56 tok/s/user on MiniMax M2.5/M2.7; H200 posts 2342 tok/s/chip for $0.14. GB200 NVL72 is 108% cheaper per token; GB200 NVL72 delivers 217% more tok/s/chip.

Throughput at 81 tok/s/user on MiniMax M2.5/M2.7: GB200 NVL72 hits 3276 tok/s/chip, H200 hits 1595. Per-million costs land at $0.16 and $0.21 respectively. GB200 NVL72 is 35% cheaper per token; GB200 NVL72 delivers 105% more tok/s/chip.

GB200 NVL72 / H200 on MiniMax M2.5/M2.7 at 106 tok/s/user: 2028 / 895 tok/s/chip, $0.25 / $0.38 per million tokens. GB200 NVL72 is 49% cheaper per token; GB200 NVL72 delivers 127% more tok/s/chip. (Numbers reflect the default 8k/1k · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)

View performance-per-dollar view →

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)
Throughput (tok/s/chip)
GB200 NVL72:7412.3H200:2341.7
GB200 NVL72:3276.0H200:1594.8
GB200 NVL72:2027.9H200:894.8
Cost ($/M tok)
GB200 NVL72:$0.070H200:$0.145
GB200 NVL72:$0.158H200:$0.213
GB200 NVL72:$0.255H200:$0.379
tok/s/MW
GB200 NVL72:3963793H200:1709253
GB200 NVL72:1751893H200:1164064
GB200 NVL72:1084456H200:653150
Concurrency
GB200 NVL72:~92H200:~19
GB200 NVL72:~30H200:~9
GB200 NVL72:~14H200:~4

Inference Performance

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

Total Tokens per $1 USD (Owning - Neocloud Giant) vs. Interactivity

MiniMax M2.5/2.7 230B • 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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