Kimi K2.5/K2.6/K2.7-Code 1T — B200 vs MI355X
Head-to-head AI inference benchmark comparison of B200 (NVIDIA Blackwell) and MI355X (AMD CDNA 4) on Kimi K2.5/K2.6/K2.7-Code 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.
Near the low end of the 11–123 tok/s/user interactivity band, at 38 tok/s/user on Kimi K2.5/K2.6/K2.7-Code 1T: B200 runs 12043 tok/s/chip at $0.04/M tokens, MI355X runs 4004 at $0.10/M. B200 is 161% cheaper per token; B200 delivers 201% more tok/s/chip.
Setting 67 tok/s/user as the target on Kimi K2.5/K2.6/K2.7-Code 1T, B200 produces 3662 tok/s/chip ($0.13 per million tokens) and MI355X produces 2493 ($0.17). B200 is 27% cheaper per token; B200 delivers 47% more tok/s/chip.
At 95 tok/s/user interactivity on Kimi K2.5/K2.6/K2.7-Code 1T, B200 delivers 1643 tok/s/chip at $0.29 per million tokens; MI355X delivers 1739 tok/s/chip at $0.24. MI355X is 22% cheaper per token; MI355X delivers 6% more tok/s/chip at this point. (Numbers reflect the default 8k/1k · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | B200:12042.6MI355X:4003.6 | B200:3662.1MI355X:2492.7 | B200:1643.4MI355X:1738.7 |
| Cost ($/M tok) | B200:$0.040MI355X:$0.104 | B200:$0.131MI355X:$0.167 | B200:$0.292MI355X:$0.240 |
| tok/s/MW | B200:7042449MI355X:1915595 | B200:2141577MI355X:1192676 | B200:961048MI355X:831906 |
| Concurrency | B200:~736MI355X:~102 | B200:~178MI355X:~17 | B200:~9MI355X:~9 |
Inference Performance
Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.
Total Tokens per $1 USD (Owning - Hyperscaler) vs. Interactivity
Kimi K2.5/2.6/2.7-Code 1T • FP4 • 8K / 1K • Source: SemiAnalysis InferenceX™
TCO $/chip/hr: H100: 1.17H200: 1.22B200: 1.73B300: 2.26GB200: 1.86GB300: 2.31MI300X: 0.95MI325X: 1.1MI355X: 1.5RTX6000PRO: 0.68
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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