MiniMax M3 428B — B200 vs GB300 NVL72
Head-to-head AI inference benchmark comparison of B200 (NVIDIA Blackwell) and GB300 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 →
B200 / GB300 NVL72 on MiniMax M3 428B at 159 tok/s/user: 34489 / 32568 tok/s/chip, $0.01 / $0.02 per million tokens. B200 is 41% cheaper per token; B200 delivers 6% more tok/s/chip.
Around the middle of the 100–336 tok/s/user interactivity band, at 218 tok/s/user on MiniMax M3 428B: B200 runs 21056 tok/s/chip at $0.02/M tokens, GB300 NVL72 runs 21832 at $0.03/M. B200 is 29% cheaper per token; GB300 NVL72 delivers 4% more tok/s/chip.
Setting 277 tok/s/user as the target on MiniMax M3 428B, B200 produces 12813 tok/s/chip ($0.04 per million tokens) and GB300 NVL72 produces 13103 ($0.05). B200 is 31% cheaper per token; GB300 NVL72 delivers 2% more tok/s/chip. (Numbers reflect the default agentic-traces · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | B200:34489.2GB300 NVL72:32567.6 | B200:21055.6GB300 NVL72:21832.5 | B200:12813.1GB300 NVL72:13103.4 |
| Cost ($/M tok) | B200:$0.014GB300 NVL72:$0.020 | B200:$0.023GB300 NVL72:$0.029 | B200:$0.038GB300 NVL72:$0.049 |
| tok/s/MW | B200:20169138GB300 NVL72:15362054 | B200:12313219GB300 NVL72:10298334 | B200:7493050GB300 NVL72:6180856 |
| Concurrency | B200:~21GB300 NVL72:~29 | B200:~14GB300 NVL72:~20 | B200:~7GB300 NVL72:~24 |
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 ($)