Qwen3.6-35B-A3B-F32-GGUF

Qwen3.6-35B-A3B-F32-GGUF

Qwen3.6-35B-A3B from Alibaba's Qwen team is a sparse Mixture-of-Experts (MoE) multimodal language model with 35B total parameters but only ~3B active per token, featuring a hybrid architecture of 40 layers with Gated DeltaNet blocks, 2048 hidden dimension, 248K vocabulary for 201+ languages, multi-token prediction (MTP), and 262K native context length (extensible to 1M+ tokens via YaRN). Released as a coding/agentic powerhouse, it achieves SOTA agentic coding performance—73.4% on SWE-Bench Verified (rivaling models 10x its active size), frontier-level repository reasoning, frontend workflows, and multimodal understanding (text/images/video/OCR)—while delivering dense-3B inference costs with FP8 quants for single-GPU deployment via vLLM/Ollama. Apache 2.0-licensed with optimizations like prefix caching, chunked prefill, and tool-call parsing, the upgraded Qwen3.6 iteration over Qwen3.5-35B-A3B prioritizes production reliability for autonomous dev agents, structured JSON outputs, and long-context software engineering tasks.

Model Files

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):