Head-to-head AI inference benchmark comparison of B200 (NVIDIA Blackwell) and B300 (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 posts 39261 tok/s/chip for $0.01 per million tokens at 143 tok/s/user on MiniMax M3 428B; B300 posts 43649 tok/s/chip for $0.01. B200 is 18% cheaper per token; B300 delivers 11% more tok/s/chip.
Throughput at 221 tok/s/user on MiniMax M3 428B: B200 hits 20474 tok/s/chip, B300 hits 22162. Per-million costs land at $0.02 and $0.03 respectively. B200 is 21% cheaper per token; B300 delivers 8% more tok/s/chip.
B200 / B300 on MiniMax M3 428B at 298 tok/s/user: 7505 / 8629 tok/s/chip, $0.06 / $0.07 per million tokens. B200 is 14% cheaper per token; B300 delivers 15% more tok/s/chip. (Numbers reflect the default agentic-traces · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | B200:39261.0B300:43648.9 | B200:20474.4B300:22162.3 | B200:7504.6B300:8629.1 |
| Cost ($/M tok) | B200:$0.012B300:$0.014 | B200:$0.023B300:$0.028 | B200:$0.064B300:$0.073 |
| tok/s/MW | B200:22959665B300:22973125 | B200:11973325B300:11664360 | B200:4388656B300:4541609 |
| Concurrency | B200:~23B300:~25 | B200:~14B300:~15 | B200:~3B300:~5 |
Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.
TCO $/chip/hr:
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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MiniMax M3 428B 8K / 1K Total Tokens per $1 TCO vs. Interactivity