Composer 1

Composer 2.5

Cursor

Composer 2.5 is Cursor’s newer Kimi K2.5-derived coding model, significantly improving sustained agent work, tool selection, instruction following, and effort calibration. Training scales synthetic reinforcement-learning tasks and adds localized textual feedback that teaches desired behavior through on-policy distillation. The model retains a documented 200K context window, with standard and fast serving options representing the same intelligence tier. It is available within Cursor’s desktop, web, CLI, and mobile surfaces; the provider also describes building agents with its SDK. The larger model being trained with SpaceXAI is a future project, not a released Composer 3 identity. Composer 2.5 weights remain proprietary.

Composer 2

Cursor

Composer 2 is Cursor’s Kimi K2.5-derived agentic coding model, released after continued pretraining on code-focused data and large-scale reinforcement learning in realistic Cursor sessions. The training combines stronger base knowledge with the same tools and agent harness used for deployment. It supports multi-step repository work and long-running coding sessions, with a documented 200K single-call context and a faster serving option within the same model identity. The launch on March 19 predates the public technical report. Its ancestry is explicitly documented; this does not make the proprietary post-trained weights publicly downloadable. Performance and cost claims depend on task sets, harnesses, and serving configuration.

Composer 1.5

Cursor

Composer 1.5 is Cursor’s significant coding-model update, built by expanding reinforcement learning approximately twentyfold on the same pretrained base used for Composer 1. It adds adaptive thinking and trained self-summarization, balancing fast responses to simple requests with longer reasoning on difficult repository tasks. When a session approaches its context limit, the model writes a summary and continues from that state; this extends task duration rather than the raw context capacity of one inference call. The proprietary mixture-of-experts model is delivered through Cursor. The publisher does not identify the pretrained checkpoint or parameter count, so this record does not invent an ancestry relationship to Composer 1’s post-trained weights.