Qwen 3.5 397B-A17B — B200 vs GB200 NVL72
Head-to-head AI inference benchmark comparison of B200 (NVIDIA Blackwell) and GB200 NVL72 (NVIDIA Blackwell) on Qwen 3.5 397B-A17B. 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.
AgentX replays real coding-agent sessions rather than fixed-length prompts, so context grows turn over turn and most of each request is served from cache instead of being recomputed. That turns the comparison into a systems question: KV transfer between nodes, prefix-aware routing, and cache capacity all move the curve alongside raw chip throughput. Fixed-sequence workloads stay the clean baseline for kernel and silicon performance, so the two scenarios answer different questions about the same hardware. Learn more about AgentX →
B200 posts 51921 tok/s/chip for $0.01 per million tokens at 145 tok/s/user on Qwen 3.5 397B-A17B; GB200 NVL72 posts 57952 tok/s/chip for $0.01. GB200 NVL72 is 4% cheaper per token; GB200 NVL72 delivers 12% more tok/s/chip.
Throughput at 227 tok/s/user on Qwen 3.5 397B-A17B: B200 hits 31924 tok/s/chip, GB200 NVL72 hits 27609. Per-million costs land at $0.02 and $0.02 respectively. B200 is 24% cheaper per token; B200 delivers 16% more tok/s/chip.
B200 / GB200 NVL72 on Qwen 3.5 397B-A17B at 309 tok/s/user: 13223 / 12476 tok/s/chip, $0.04 / $0.04 per million tokens. B200 is 14% cheaper per token; B200 delivers 6% more tok/s/chip. (Numbers reflect the default agentic-traces · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | B200:51921.4GB200 NVL72:57952.0 | B200:31924.3GB200 NVL72:27608.5 | B200:13222.7GB200 NVL72:12476.1 |
| Cost ($/M tok) | B200:$0.009GB200 NVL72:$0.009 | B200:$0.015GB200 NVL72:$0.019 | B200:$0.036GB200 NVL72:$0.041 |
| tok/s/MW | B200:30363395GB200 NVL72:30990380 | B200:18669171GB200 NVL72:14763916 | B200:7732575GB200 NVL72:6671725 |
| Concurrency | B200:~18GB200 NVL72:~20 | B200:~21GB200 NVL72:~19 | B200:~9GB200 NVL72:~4 |
Total Tokens per $1 USD (Owning - Neocloud Giant) vs. Interactivity
Qwen3.5 397B • FP4 • 8K / 1K • Source: SemiAnalysis InferenceX™
Qwen3.5 397B • 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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Interactivity is the rate at which a single user receives generated tokens while the model streams its answer — how quickly new words appear on screen. Higher values feel snappier; operators trade it against batch throughput.
How many total tokens (input + output) one US dollar of infrastructure spend buys, priced with the all-in hourly ownership cost of a Neocloud Giant operator. It is the reciprocal of cost per token, so higher means cheaper.
Formula: tok/$ = (total tok/s/chip × 3,600) ÷ all-in cost per chip-hour ($)