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Kimi K2.5/K2.6/K2.7-Code 1T · Chip comparison

Kimi K2.5/K2.6/K2.7-Code 1T — B200 vs GB300 NVL72

Head-to-head AI inference benchmark comparison of B200 (NVIDIA Blackwell) and GB300 NVL72 (NVIDIA Blackwell) on Kimi K2.5/K2.6/K2.7-Code 1T. 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 63 tok/s/user on Kimi K2.5/K2.6/K2.7-Code 1T: B200 hits 3775 tok/s/chip, GB300 NVL72 hits 10203. Per-million costs land at $0.13 and $0.06 respectively. GB300 NVL72 is 102% cheaper per token; GB300 NVL72 delivers 170% more tok/s/chip.

B200 / GB300 NVL72 on Kimi K2.5/K2.6/K2.7-Code 1T at 103 tok/s/user: 1594 / 1685 tok/s/chip, $0.30 / $0.38 per million tokens. B200 is 26% cheaper per token; GB300 NVL72 delivers 6% more tok/s/chip.

Toward the upper edge of the 24–182 tok/s/user interactivity band, at 143 tok/s/user on Kimi K2.5/K2.6/K2.7-Code 1T: B200 runs 601 tok/s/chip at $0.80/M tokens, GB300 NVL72 runs 731 at $0.88/M. B200 is 10% cheaper per token; GB300 NVL72 delivers 22% 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.)

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)
B200:3775.2GB300 NVL72:10202.8
B200:1594.1GB300 NVL72:1684.6
B200:601.3GB300 NVL72:731.1
Cost ($/M tok)
B200:$0.127GB300 NVL72:$0.063
B200:$0.301GB300 NVL72:$0.381
B200:$0.799GB300 NVL72:$0.878
tok/s/MW
B200:2207698GB300 NVL72:4812653
B200:932248GB300 NVL72:794621
B200:351648GB300 NVL72:344856
Concurrency
B200:~235GB300 NVL72:~1202
B200:~45GB300 NVL72:~68
B200:~4GB300 NVL72:~26

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

Kimi K2.5/2.6/2.7-Code 1T • 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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