Kimi K2.5/K2.6 1T — B300 vs H200
Head-to-head AI inference benchmark comparison of B300 (NVIDIA Blackwell) and H200 (NVIDIA Hopper) on Kimi K2.5/K2.6 1T. 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.
B300 / H200 on Kimi K2.5/K2.6 1T at 51 tok/s/user: 464 / 314 tok/s/GPU, $1.41 / $1.23 per million tokens. H200 is 14% cheaper per token; B300 delivers 48% more tok/s/GPU.
Around the middle of the 34–106 tok/s/user interactivity band, at 70 tok/s/user on Kimi K2.5/K2.6 1T: B300 runs 325 tok/s/GPU at $1.99/M tokens, H200 runs 218 at $1.77/M. H200 is 12% cheaper per token; B300 delivers 49% more tok/s/GPU.
Setting 88 tok/s/user as the target on Kimi K2.5/K2.6 1T, B300 produces 233 tok/s/GPU ($2.81 per million tokens) and H200 produces 158 ($2.50). H200 is 12% cheaper per token; B300 delivers 48% more tok/s/GPU. (Numbers reflect the default 1k/1k · int4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/gpu) | B300:464.1H200:314.2 | B300:324.6H200:218.5 | B300:232.6H200:157.6 |
| Cost ($/M tok) | B300:$1.412H200:$1.234 | B300:$1.995H200:$1.775 | B300:$2.809H200:$2.500 |
| tok/s/MW | B300:213867H200:181608 | B300:149585H200:126289 | B300:107202H200:91070 |
| Concurrency | B300:~32H200:~25 | B300:~13H200:~13 | B300:~5H200:~7 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.