Kimi K2.5/K2.6/K2.7-Code 1T — B200 vs Jalapeño Performance per Dollar
Cost per million tokens of B200 (NVIDIA Blackwell) versus Jalapeño (OpenAI Jalapeño) on Kimi K2.5/K2.6/K2.7-Code 1T. Large-hyperscaler-volume ownership TCO normalized by total tokens — performance per dollar across LLM workloads. Pick the more cost-efficient SKU at every target interactivity level. Use the chart controls below to switch sequences, precisions, and metrics — same interactions as the main inference chart.
Jalapeño edges B200 at 91 tok/s/user on Kimi K2.5/K2.6/K2.7-Code 1T — $0.03 per million tokens versus $0.28, a 791% cost-per-token gap.
Push Kimi K2.5/K2.6/K2.7-Code 1T to 122 tok/s/user and B200 lands at $0.48 per million tokens against Jalapeño's $0.04 — Jalapeño pulls ahead by 1216%.
B200: $0.95 per million tokens. Jalapeño: $0.05. Both at 153 tok/s/user on Kimi K2.5/K2.6/K2.7-Code 1T, with Jalapeño 1961% cheaper. (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.)
Chip pricing (owning hyperscaler): B200 $1.73/chip/hr · Jalapeño $1.27/chip/hr. Source: SemiAnalysis Market July 2026 Pricing Surveys & AI Cloud TCO Model.
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Dollar per Million Tokens | B200:$0.275Jalapeño:$0.031 | B200:$0.479Jalapeño:$0.036 | B200:$0.953Jalapeño:$0.046 |
| Concurrency | B200:~62Jalapeño:~1062 | B200:~7Jalapeño:~573 | B200:~18Jalapeño:~304 |