MiniMax M3 428B — B300 vs GB200 NVL72
Head-to-head AI inference benchmark comparison of B300 (NVIDIA Blackwell) and GB200 NVL72 (NVIDIA Blackwell) on MiniMax M3 428B. 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 →
B300 posts 63812 tok/s/chip for $0.01 per million tokens at 111 tok/s/user on MiniMax M3 428B; GB200 NVL72 posts 26272 tok/s/chip for $0.02. B300 is 100% cheaper per token; B300 delivers 143% more tok/s/chip.
Throughput at 155 tok/s/user on MiniMax M3 428B: B300 hits 37553 tok/s/chip, GB200 NVL72 hits 18724. Per-million costs land at $0.02 and $0.03 respectively. B300 is 65% cheaper per token; B300 delivers 101% more tok/s/chip.
B300 / GB200 NVL72 on MiniMax M3 428B at 199 tok/s/user: 22556 / 7680 tok/s/chip, $0.03 / $0.07 per million tokens. B300 is 142% cheaper per token; B300 delivers 194% more tok/s/chip. (Numbers reflect the default agentic-traces · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | B300:63812.0GB200 NVL72:26272.1 | B300:37552.6GB200 NVL72:18723.7 | B300:22555.8GB200 NVL72:7679.8 |
| Cost ($/M tok) | B300:$0.010GB200 NVL72:$0.020 | B300:$0.017GB200 NVL72:$0.028 | B300:$0.028GB200 NVL72:$0.067 |
| tok/s/MW | B300:33585288GB200 NVL72:14049269 | B300:19764548GB200 NVL72:10012691 | B300:11871474GB200 NVL72:4106844 |
| Concurrency | B300:~20GB200 NVL72:~18 | B300:~22GB200 NVL72:~13 | B300:~15GB200 NVL72:~5 |
Total Tokens per $1 USD (Owning - Neocloud Giant) vs. Interactivity
MiniMax M3 428B • FP4 • 8K / 1K • Source: SemiAnalysis InferenceX™
MiniMax M3 428B • 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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Interactivity is the rate at which a single user receives generated tokens while the model streams its answer — how quickly new words appear on screen. Higher values feel snappier; operators trade it against batch throughput.
How many total tokens (input + output) one US dollar of infrastructure spend buys, priced with the all-in hourly ownership cost of a Neocloud Giant operator. It is the reciprocal of cost per token, so higher means cheaper.
Formula: tok/$ = (total tok/s/chip × 3,600) ÷ all-in cost per chip-hour ($)