Junie Local: Smarter, Faster AI Coding

Junie Local has introduced Qwen3.8-3.6-27B-blend, a 27-billion-parameter model created by equally merging related Qwen models to improve local coding-agent performance without the long reasoning times associated with Qwen3.8. In an internal 100-task coding benchmark, the blend completed 37 tasks, compared with 34 for Qwen3.6 and 39 for Qwen3.8, while producing 71% fewer output tokens than Qwen3.8; on shared successful tasks, it used roughly 279,000 tokens versus 935,000. Public benchmark testing also showed strong LiveCodeBench results, though visual tasks revealed that the blend can still spend additional tokens reasoning through difficult problems. The update includes runtime optimization research using multi-token prediction, where proposing two tokens per round provided a 60% decoding speedup on an M5 MacBook Pro, while a lower-memory four-bit prediction head performed similarly to an eight-bit version. Developers also identified and corrected evaluation instability caused by repeatedly using the same random seed, and the release is available for Apple M5 users, with experimental Windows support for newer NVIDIA RTX GPUs with at least 24 GB of VRAM.

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