body { background: #06080c; color: #e5e9f0; margin: 0; } .rw-nojs-bar { max-width: 880px; margin: 0 auto; padding: 18px 16px 14px; } .rw-nojs-bar .rw-nojs-brand { font: 700 20px/1 Inter, system-ui, sans-serif; color: #e5e9f0; text-decoration: none; } .rw-nojs-nav { max-width: 880px; margin: 0 auto; padding: 0 16px 14px; border-bottom: 1px solid #1c2230; font: 500 14px/1.4 Inter, system-ui, sans-serif; } .rw-nojs-nav a { color: #6f9bff; margin: 0 14px 6px 0; text-decoration: none; display: inline-block; } .rw-nojs-nav a:hover { text-decoration: underline; } .rw-nojs-note { max-width: 880px; margin: 12px auto 0; padding: 0 16px; font: 400 13px/1.5 Inter, system-ui, sans-serif; color: #8a93a6; } #root [data-rw-crawler] { max-width: 880px; margin: 0 auto; padding: 8px 16px 48px; font: 400 16px/1.65 Inter, system-ui, sans-serif; color: #e5e9f0; } #root [data-rw-crawler] a { color: #6f9bff; } #root [data-rw-crawler] h1 { font-size: 28px; line-height: 1.2; } #root [data-rw-crawler] h2 { font-size: 20px; margin-top: 28px; } #root [data-rw-crawler] img { max-width: 100%; height: auto; } #root [data-rw-crawler] ul { padding-left: 0; list-style: none; } #root [data-rw-crawler] li { margin: 0 0 18px; } #root [data-rw-crawler] .rw-pagination { margin: 28px 0 0; display: flex; flex-wrap: wrap; gap: 12px; align-items: baseline; } #root [data-rw-crawler] .rw-pagination strong { color: #e5e9f0; } .rw-nojs-footer { max-width: 880px; margin: 40px auto 0; padding: 22px 16px 44px; border-top: 1px solid #1c2230; font: 400 13px/1.6 Inter, system-ui, sans-serif; color: #8a93a6; } .rw-nojs-footer .rw-nojs-fcols { display: flex; flex-wrap: wrap; gap: 28px 40px; margin-bottom: 20px; } .rw-nojs-footer h2 { font-size: 11px; letter-spacing: 0.05em; text-transform: uppercase; color: #b7c0d3; margin: 0 0 8px; } .rw-nojs-footer a { color: #6f9bff; text-decoration: none; display: block; margin: 0 0 5px; } .rw-nojs-footer a:hover { text-decoration: underline; } .rw-nojs-footer .rw-nojs-legal { font: 400 12px/1.6 Inter, system-ui, sans-serif; color: #6b7384; margin: 0; } .rw-nojs-footer .rw-nojs-legal a { display: inline; } RuntimeWire Investigations Stories AI Startups Venture Products Funding Exits Models Head-to-Head About You're browsing RuntimeWire with JavaScript disabled. Articles and navigation work fully. Interactive features — search, comments, and newsletter signup — require JavaScript.

moonshotai

MoonshotAI: Kimi K2.7 Code

moonshotai/kimi-k2.7-code

MoonshotAI: Kimi K2.7 Code is a coding-focused model in Moonshot AI's Kimi K2 family, built to complete end-to-end programming tasks reliably over long contexts.

Context window: 262,144 tokens
Input: text, image
Output: text
Pricing: $0.6712/M input tokens, $3.35/M output tokens

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