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Qwen 3.5 397B-A17B · Chip comparison

Qwen 3.5 397B-A17B — B200 vs GB300 NVL72

Head-to-head AI inference benchmark comparison of B200 (NVIDIA Blackwell) and GB300 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 →

Throughput at 115 tok/s/user on Qwen 3.5 397B-A17B: B200 hits 63058 tok/s/chip, GB300 NVL72 hits 150385. Per-million costs land at $0.01 and $0.00 respectively. GB300 NVL72 is 79% cheaper per token; GB300 NVL72 delivers 138% more tok/s/chip.

B200 / GB300 NVL72 on Qwen 3.5 397B-A17B at 207 tok/s/user: 33060 / 113277 tok/s/chip, $0.01 / $0.01 per million tokens. GB300 NVL72 is 157% cheaper per token; GB300 NVL72 delivers 243% more tok/s/chip.

Toward the upper edge of the 24–390 tok/s/user interactivity band, at 299 tok/s/user on Qwen 3.5 397B-A17B: B200 runs 14238 tok/s/chip at $0.03/M tokens, GB300 NVL72 runs 71448 at $0.01/M. GB300 NVL72 is 276% cheaper per token; GB300 NVL72 delivers 402% 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.)

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)
B200:63057.8GB300 NVL72:150384.7
B200:33059.8GB300 NVL72:113277.0
B200:14237.7GB300 NVL72:71448.5
Cost ($/M tok)
B200:$0.008GB300 NVL72:$0.004
B200:$0.015GB300 NVL72:$0.006
B200:$0.034GB300 NVL72:$0.009
tok/s/MW
B200:36875891GB300 NVL72:70936182
B200:19333243GB300 NVL72:53432559
B200:8326124GB300 NVL72:33702111
Concurrency
B200:~24GB300 NVL72:~704
B200:~24GB300 NVL72:~624
B200:~10GB300 NVL72:~153

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

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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