Head-to-head AI inference benchmark comparison of B200 (NVIDIA Blackwell) and H200 (NVIDIA Hopper) 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 36441 tok/s/chip for $0.01 per million tokens at 97 tok/s/user on Qwen 3.5 397B-A17B; H200 posts 8236 tok/s/chip for $0.04. B200 is 212% cheaper per token; B200 delivers 342% more tok/s/chip.
Throughput at 142 tok/s/user on Qwen 3.5 397B-A17B: B200 hits 30671 tok/s/chip, H200 hits 5515. Per-million costs land at $0.02 and $0.06 respectively. B200 is 292% cheaper per token; B200 delivers 456% more tok/s/chip.
B200 / H200 on Qwen 3.5 397B-A17B at 187 tok/s/user: 21087 / 3033 tok/s/chip, $0.02 / $0.11 per million tokens. B200 is 390% cheaper per token; B200 delivers 595% more tok/s/chip. (Numbers reflect the default agentic-traces · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | B200:36440.7H200:8236.1 | B200:30671.0H200:5515.3 | B200:21086.6H200:3032.7 |
| Cost ($/M tok) | B200:$0.013H200:$0.041 | B200:$0.016H200:$0.061 | B200:$0.023H200:$0.112 |
| tok/s/MW | B200:21310328H200:6011752 | B200:17936255H200:4025740 | B200:12331343H200:2213642 |
| Concurrency | B200:~28H200:~13 | B200:~21H200:~9 | B200:~24H200:~4 |
Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.
TCO $/chip/hr:
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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Qwen3.5 397B 8K / 1K Total Tokens per $1 TCO vs. Interactivity