Qwen3.8-3.6-27B-blend
Unofficial JetBrains derivative. This model was prepared by JetBrains using Qwen models developed by Alibaba Cloud. This release is not affiliated with, sponsored by, or endorsed by Alibaba Cloud or Alibaba Group.
Tasks completed versus output tokens on JetBrains’ 100-task internal coding benchmark. Reasoning settings differ between configurations; see the article for methodology and context.
Read Making Local AI Smarter and Faster for the Junie Local release story, coding evaluations, token-efficiency results, and MTP runtime experiments.
Available formats: BF16 · GGUF · MLX 4-bit · MTP MLX 4-bit.
This model is a 50/50 combination of Qwen3.6-27B and Qwen3.8-27B, created by linearly interpolating their checkpoint parameters. The configuration, tokenizer, processor, and chat template are retained from Qwen3.8-27B. Exact source revisions and merge settings are recorded in merge-manifest.json.
The interpolation uses float32 accumulation.
0.5 * Qwen/Qwen3.6-27B + 0.5 * Qwen/Qwen3.8-27B
Qwen/Qwen3.6-27Brevision6a9e13bd6fc8f0983b9b99948120bc37f49c13e9Qwen/Qwen3.8-27Brevision1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0
License and attribution
Distributed under the Apache License, Version 2.0. The original Qwen LICENSE, including Copyright 2026 Alibaba Cloud, is retained unchanged.
Copyright © 2026 JetBrains s.r.o. This notice applies only to original material contributed by JetBrains. Upstream Qwen material remains subject to its original copyright and attribution notices.
See NOTICE for attribution and CHANGES.md for a description of the modifications and the affected files.
Note on acceptable use
This model is distributed under the Apache License, Version 2.0. This notice does not modify the License. Users are reminded that using this model, or its outputs, to infringe the copyright or other intellectual property rights of third parties may violate applicable law, independent of the terms of this License.
Training and upstream information
No additional training or fine-tuning was performed, and no additional training data was used to create this derivative; its preparation consisted of the checkpoint merge and, where applicable, the extraction, format conversion, and quantization described above and in CHANGES.md.
For upstream information, see the official Qwen3.6-27B model card and Qwen3.8-27B model card. Qwen also publishes a Training Data Summary, which describes training data for models powering qwen.ai generally; it does not identify the exact training datasets for these two checkpoints.