Head-to-head AI inference benchmark comparison of B300 (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 →
Throughput at 99 tok/s/user on Qwen 3.5 397B-A17B: B300 hits 36696 tok/s/chip, H200 hits 8013. Per-million costs land at $0.02 and $0.04 respectively. B300 is 147% cheaper per token; B300 delivers 358% more tok/s/chip.
B300 / H200 on Qwen 3.5 397B-A17B at 146 tok/s/user: 30929 / 5351 tok/s/chip, $0.02 / $0.06 per million tokens. B300 is 212% cheaper per token; B300 delivers 478% more tok/s/chip.
Toward the upper edge of the 52–239 tok/s/user interactivity band, at 193 tok/s/user on Qwen 3.5 397B-A17B: B300 runs 19033 tok/s/chip at $0.03/M tokens, H200 runs 2584 at $0.13/M. B300 is 298% cheaper per token; B300 delivers 636% more tok/s/chip. (Numbers reflect the default agentic-traces · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | B300:36695.7H200:8012.9 | B300:30929.4H200:5350.5 | B300:19033.2H200:2584.4 |
| Cost ($/M tok) | B300:$0.017H200:$0.042 | B300:$0.020H200:$0.063 | B300:$0.033H200:$0.131 |
| tok/s/MW | B300:19313530H200:5848806 | B300:16278616H200:3905478 | B300:10017456H200:1886394 |
| Concurrency | B300:~29H200:~13 | B300:~22H200:~9 | B300:~13H200:~4 |
Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.
TCO $/chip/hr: VR200: 3.61H100: 1.17H200: 1.22B200: 1.73B300: 2.26GB200: 1.86GB300: 2.31MI300X: 0.95MI325X: 1.1MI355X: 1.5RTX6000PRO: 0.68Jalapeño (Teacup): 1.47
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