GLM 5.3 744B — B200 vs MI325X Performance per Dollar
Cost per million tokens of B200 (NVIDIA Blackwell) versus MI325X (AMD CDNA 3) on GLM 5.3 744B. Large-hyperscaler-volume ownership TCO normalized by total tokens — performance per dollar across LLM workloads. Pick the more cost-efficient SKU at every target interactivity level. 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 edges MI325X at 67 tok/s/user on GLM 5.3 744B — $0.06 per million tokens versus $0.32, a 445% cost-per-token gap.
Push GLM 5.3 744B to 68 tok/s/user and B200 lands at $0.06 per million tokens against MI325X's $0.32 — B200 pulls ahead by 445%.
B200: $0.06 per million tokens. MI325X: $0.32. Both at 69 tok/s/user on GLM 5.3 744B, with B200 445% cheaper. (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.)
Chip pricing (owning hyperscaler): B200 $1.73/chip/hr · MI325X $1.10/chip/hr. Source: SemiAnalysis Market July 2026 Pricing Surveys & AI Cloud TCO Model.

| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Dollar per Million Tokens | B200:$0.059MI325X:$0.320 | B200:$0.059MI325X:$0.321 | B200:$0.059MI325X:$0.322 |
| Concurrency | B200:~16MI325X:~1 | B200:~16MI325X:~1 | B200:~16MI325X:~1 |
Inference Performance
Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.
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