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GLM 5/5.1 · Chip comparison

GLM 5/5.1 — B200 vs GB200 NVL72

Head-to-head AI inference benchmark comparison of B200 (NVIDIA Blackwell) and GB200 NVL72 (NVIDIA Blackwell) on GLM 5/5.1. 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.

B200 posts 2532 tok/s/chip for $0.19 per million tokens at 58 tok/s/user on GLM 5/5.1; GB200 NVL72 posts 9598 tok/s/chip for $0.05. GB200 NVL72 is 253% cheaper per token; GB200 NVL72 delivers 279% more tok/s/chip.

Throughput at 86 tok/s/user on GLM 5/5.1: B200 hits 1796 tok/s/chip, GB200 NVL72 hits 6589. Per-million costs land at $0.27 and $0.08 respectively. GB200 NVL72 is 241% cheaper per token; GB200 NVL72 delivers 267% more tok/s/chip.

B200 / GB200 NVL72 on GLM 5/5.1 at 114 tok/s/user: 1385 / 1997 tok/s/chip, $0.35 / $0.26 per million tokens. GB200 NVL72 is 34% cheaper per token; GB200 NVL72 delivers 44% 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)
B200:2531.9GB200 NVL72:9597.8
B200:1796.4GB200 NVL72:6589.1
B200:1385.1GB200 NVL72:1996.6
Cost ($/M tok)
B200:$0.190GB200 NVL72:$0.054
B200:$0.268GB200 NVL72:$0.078
B200:$0.347GB200 NVL72:$0.259
tok/s/MW
B200:1480645GB200 NVL72:5132497
B200:1050524GB200 NVL72:3523588
B200:809988GB200 NVL72:1067709
Concurrency
B200:~20GB200 NVL72:~953
B200:~10GB200 NVL72:~664
B200:~5GB200 NVL72:~108

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

GLM5/5.1 744B • 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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