Head-to-head AI inference benchmark comparison of B300 (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.
Throughput at 43 tok/s/user on MiniMax M2.5/M2.7: B300 hits 10382 tok/s/chip, H200 hits 2816. Per-million costs land at $0.06 and $0.12 respectively. B300 is 99% cheaper per token; B300 delivers 269% more tok/s/chip.
B300 / H200 on MiniMax M2.5/M2.7 at 72 tok/s/user: 4787 / 1855 tok/s/chip, $0.13 / $0.18 per million tokens. B300 is 39% cheaper per token; B300 delivers 158% more tok/s/chip.
Toward the upper edge of the 14–131 tok/s/user interactivity band, at 102 tok/s/user on MiniMax M2.5/M2.7: B300 runs 2425 tok/s/chip at $0.26/M tokens, H200 runs 1011 at $0.34/M. B300 is 29% cheaper per token; B300 delivers 140% more tok/s/chip. (Numbers reflect the default 8k/1k · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | B300:10382.5H200:2816.4 | B300:4786.6H200:1855.1 | B300:2425.0H200:1011.3 |
| Cost ($/M tok) | B300:$0.060H200:$0.120 | B300:$0.131H200:$0.183 | B300:$0.259H200:$0.335 |
| tok/s/MW | B300:5464464H200:2055797 | B300:2519239H200:1354090 | B300:1276295H200:738156 |
| Concurrency | B300:~215H200:~30 | B300:~8H200:~12 | B300:~16H200:~5 |
Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.
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.
Matching measurements exist, but their chip series are hidden.
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MiniMax M2.5/2.7 230B 8K / 1K Total Tokens per $1 TCO vs. Interactivity