Kimi K2.5/K2.6/K2.7-Code 1T — B300 vs Jalapeño Performance per Dollar
Cost per million tokens of B300 (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.
Near the low end of the 61–173 tok/s/user interactivity band — at 89 tok/s/user — B300 runs $0.31 per million tokens on Kimi K2.5/K2.6/K2.7-Code 1T while Jalapeño runs $0.03. Jalapeño is the cheaper choice by 914%.
On Kimi K2.5/K2.6/K2.7-Code 1T at 117 tok/s/user, the per-million math comes out to $0.52 for B300 and $0.04 for Jalapeño; Jalapeño delivers 1387% more total tokens per dollar.
At 145 tok/s/user on Kimi K2.5/K2.6/K2.7-Code 1T, B300 costs $1.07 per million tokens; Jalapeño costs $0.04. Jalapeño is 2398% more cost-efficient at this operating 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.)
Chip pricing (owning hyperscaler): B300 $2.26/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 | B300:$0.311Jalapeño:$0.031 | B300:$0.525Jalapeño:$0.035 | B300:$1.073Jalapeño:$0.043 |
| Concurrency | B300:~10Jalapeño:~1112 | B300:~5Jalapeño:~629 | B300:~2Jalapeño:~358 |