Head-to-head AI inference benchmark comparison of B200 (NVIDIA Blackwell) and MI300X (AMD CDNA 3) 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 →
Near the low end of the 4–10 tok/s/user interactivity band, at 5 tok/s/user on Qwen 3.5 397B-A17B: B200 runs 37074 tok/s/chip at $0.01/M tokens, MI300X runs 3331 at $0.08/M. B200 is 511% cheaper per token; B200 delivers 1013% more tok/s/chip.
Setting 7 tok/s/user as the target on Qwen 3.5 397B-A17B, B200 produces 37074 tok/s/chip ($0.01 per million tokens) and MI300X produces 3331 ($0.08). B200 is 511% cheaper per token; B200 delivers 1013% more tok/s/chip.
At 9 tok/s/user interactivity on Qwen 3.5 397B-A17B, B200 delivers 37074 tok/s/chip at $0.01 per million tokens; MI300X delivers 3270 tok/s/chip at $0.08. B200 is 523% cheaper per token; B200 delivers 1034% more tok/s/chip at this point. (Numbers reflect the default agentic-traces · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | B200:37074.2MI300X:3330.7 | B200:37074.2MI300X:3330.7 | B200:37074.2MI300X:3270.0 |
| Cost ($/M tok) | B200:$0.013MI300X:$0.079 | B200:$0.013MI300X:$0.079 | B200:$0.013MI300X:$0.081 |
| tok/s/MW | B200:21680845MI300X:2396173 | B200:21680845MI300X:2396173 | B200:21680845MI300X:2352546 |
| Concurrency | B200:~32MI300X:~24 | B200:~32MI300X:~24 | B200:~32MI300X:~23 |
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