Head-to-head AI inference benchmark comparison of B200 (NVIDIA Blackwell) and B300 (NVIDIA Blackwell) 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 60 tok/s/user on MiniMax M2.5/M2.7: B200 hits 13123 tok/s/chip, B300 hits 13222. Per-million costs land at $0.04 and $0.05 respectively. B200 is 30% cheaper per token; throughput per chip is essentially tied.
B200 / B300 on MiniMax M2.5/M2.7 at 101 tok/s/user: 6649 / 6697 tok/s/chip, $0.07 / $0.09 per million tokens. B200 is 30% cheaper per token; throughput per chip is essentially tied.
Toward the upper edge of the 19–183 tok/s/user interactivity band, at 143 tok/s/user on MiniMax M2.5/M2.7: B200 runs 3074 tok/s/chip at $0.16/M tokens, B300 runs 2915 at $0.22/M. B200 is 38% cheaper per token; B200 delivers 5% more tok/s/chip. (Numbers reflect the default 8k/1k · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | B200:13123.3B300:13221.8 | B200:6649.1B300:6697.5 | B200:3073.5B300:2915.1 |
| Cost ($/M tok) | B200:$0.037B300:$0.047 | B200:$0.072B300:$0.094 | B200:$0.156B300:$0.215 |
| tok/s/MW | B200:7674471B300:6958818 | B200:3888345B300:3524994 | B200:1797369B300:1534289 |
| Concurrency | B200:~787B300:~295 | B200:~9B300:~9 | B200:~11B300:~11 |
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