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Moonshot AI has released Kimi K2.7-Code, a specialized agentic model designed for long-horizon software engineering tasks rather than general chat interactions. The architecture utilizes 1T parameters with MoE activation and includes native support for vision inputs via MoonViT to handle complex codebases. Notably, the model demonstrates significant efficiency gains by reducing reasoning-token usage while outperforming previous iterations on key coding benchmarks.
Moonshot AI Releases Kimi K2.7-Code: a Coding Model Reporting +21.8% on Kimi Code Bench v2 Over K2.6
Moonshot AI has open-sourced Kimi K2.7-Code under a Modified MIT license. It is a coding-focused, agentic model built on Kimi K2.6, with a 256K context window and roughly 30% lower reasoning-token usage. Moonshot reports gains over K2.6 on six benchmarks, including +21.8% on Kimi Code Bench v2. The model is available via the Kimi API and Kimi Code. The post Moonshot AI Releases Kimi K2.7-Code: a Coding Model Reporting +21.8% on Kimi Code Bench v2 Over K2.6 appeared first on MarkTechPost.
This week, Moonshot AI released Kimi K2.7-Code. It is a coding-focused, agentic model. The model weights ship on Hugging Face under a Modified MIT license. You can also reach it through the Kimi API and Kimi Code. K2.7-Code targets long-horizon software engineering, not general chat. It plans, edits, runs tools, and debugs across many steps. Moonshot pairs the model with a subscription coding platform around it. K2.7-Code is a Mixture-of-Experts model. It holds 1T total…
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