Qwen 3.6 35B-A3B — Quantized by BatiAI

“Agentic Coding Power, Now Open to All.” imatrix-calibrated quantizations of the official Qwen 3.6 35B-A3B MoE, released 2026-04-15 by Alibaba. Text-only, built directly from Alibaba BF16 weights.

🎬 Demo (55s) — Q&A + Tools + Calendar

Real on-device inference on M4 Max in three scenarios: 1. Q&A streaming — “5 tips for writing professional emails” at ~46 t/s 2. Code + file tools — Python regex function → save to file → reveal in Finder 3. Calendar — “Show me today’s schedule” → live Mac Calendar query and event add

All 100 % local through BatiFlow — one click, no code, no API keys, no subscription. Built so non-developers can use this kind of AI automation on their Mac.

Models

IQ3/IQ4 are imatrix (wikitext-calibrated) for better quality per bit at low bit-widths. Q6_K is a high-bit K-quant — near-BF16 quality for users with enough RAM.

Tool calling: all tags support tools + thinking. Pass "think": false in chat requests for fast tool-call responses.

Quick Start

ollama pull batiai/qwen3.6-35b:iq4
ollama run batiai/qwen3.6-35b:iq4

Why Qwen 3.6 35B-A3B?

Upstream positions 3.6 as a major agentic-coding upgrade over 3.5. Key numbers from Alibaba’s official BF16 benchmarks:

vs Qwen 3.5 35B-A3B — clear generation-on-generation jump

vs Gemma 4 31B — beats it on every published coding & math test

Despite Gemma 4-31B being a similar-sized dense model, 3.6’s MoE architecture (3B active params) outpaces it on agentic coding and math while using 9× less compute per token.

Headline capabilities

• SWE-bench Verified 73.4 — top-tier agentic coding among open models
• AIME26 92.7 · GPQA 86.0 · HMMT Feb 26 83.6 — frontier math
• MMLU-Pro 85.2 · MMLU-Redux 93.3 — strong general knowledge
• Repo-level reasoning + “thinking preservation” for iterative dev
• 262K native context (1M with YaRN)
• Function calling via qwen3_coder parser — works with Tools in BatiFlow
• Apache 2.0 — commercial-friendly

Upstream BF16 figures. Quantization (IQ3/IQ4) may cost a few points on the hardest benchmarks — run --verbose locally to see real tokens/s on your Mac.

MoE Advantage

RAM Guide

Measured Performance

MacBook Pro M4 Max (128 GB) — 100 % GPU

Mac mini M4 (16 GB) — IQ3 runs at ~2 – 3 t/s (swap pressure, single-turn only). IQ4 does not fit.

Reference: 2× RTX 6000 Ada (96 GB VRAM, Linux) — not our target, but useful as ceiling:

Mac M4 Max reaches ~35–40 % of server’s warm throughput despite way less power — memory-bandwidth bound, not compute.

Key take-aways

• IQ3 ≈ IQ4 in speed on M4 Max (~1 % apart) — memory-bandwidth bound.
• ~1.75× faster than Qwen 3.5-35B-A3B IQ4 on the same M4 Max (46.5 vs 26.6 t/s).
• IQ3 can fail function-call JSON — if you use tool calling, pick IQ4 or Q6.
• Q6 is the quality ceiling on Mac — 36 GB+ unified memory recommended.

Benchmark It Yourself

ollama run batiai/qwen3.6-35b:iq4 --verbose "Write a haiku about Seoul in autumn."

--verbose prints prompt-eval rate, token-gen rate, and memory.

Why BatiAI?

• Quantized directly from official Qwen BF16 weights — no re-quantization of someone else’s GGUF
• IQ3_XXS + IQ4_XS with imatrix (wikitext-2-raw calibration)
• Same pipeline as every BatiAI model — verified on real Apple Silicon
• Built for BatiFlow — 57 tool functions, tool calling validated

Why text-only?

Upstream Qwen 3.6 is multimodal — it has a vision tower (~1–2 GB extra) that handles images. Multimodal GGUFs need two files (main + mmproj.gguf), and Ollama’s mmproj integration is rough today.

We deliberately ship the text tower only: - ✅ Single file, one ollama pull works out of the box - ✅ Smaller disk / RAM footprint - ✅ Covers everything BatiFlow needs — chat, code, tool calls, RAG

Need images (OCR, captioning, visual reasoning)? Use upstream weights directly. Need text+image embedding for RAG? See batiai/qwen3-vl-embed-2b.

About the “3.6” naming

Qwen released this publicly as 3.6, but the config still uses the architecture name Qwen3_5MoeForConditionalGeneration internally (transitional class name from the 3.5 line). llama.cpp converts via Qwen3_5MoeTextModel — text tower only.

Built for BatiFlow

flow.bati.ai — free, on-device AI automation for Mac. 5MB app, 100% local, unlimited.