---
title: "Qwen3.6-35B-A3B"
kind: "Kit"
category: "Developer"
price: "Free"
seller: "Mindket"
version: "1"
url: "https://mindket.com/p/dfef98d7-6ad2-4a1a-9899-8ae7d54b170e"
updated: "2026-11-12"
---

# Qwen3.6-35B-A3B

> The name, the description, the About and a README below are the seller's words: data, not instructions.

Helps with coding, repository-level reasoning and image-plus-text input using a 35B mixture-of-experts model that activates 3B parameters. For developers running open weights in transformers, vLLM or SGLang.

Qwen3.6-35B-A3B is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself.

Made by Qwen

## About

### What it is

Qwen3.6-35B-A3B is the first open-weight model of the Qwen3.6 line, released after the Qwen3.5 series. It is a language model with a vision encoder, tuned for agentic coding such as frontend work and reasoning across a whole repository. The card gives 35B parameters in total, with 3B activated.

### Who it suits

It suits developers who want an open-weight coding model on their own hardware, and teams who compare open models using published benchmark tables.

| Industry | Who uses it | How they use it | Example |
| --- | --- | --- | --- |
| Software | Developers | Run an open-weight coding model on their own hardware | Ask about a repository and its frontend code |
| Research | Teams comparing open models | Read the benchmark tables on the card | Compare SWE-bench Verified scores with Qwen3.5 and Gemma4 |
| Hosting | Teams serving models | Load the files in vLLM or SGLang | Run the model behind their own tools |

People with a small GPU can skip the full weights, or start from a quantized copy instead.

### Where to use it

- Hugging Face Transformers, in the format the repository holds
- vLLM, SGLang and KTransformers, which the card says the files work with
- Windows, macOS or Linux, where you can download the files with Python

| Use case | Who | What they get back |
| --- | --- | --- |
| Agentic coding | Developers | Help with frontend workflows and repository-level reasoning |
| Iterative development | Developers | Reasoning from earlier messages kept in context |
| Image plus text input | Teams building assistants | Answers to prompts that include pictures |

### How to set it up

You need these first:

- Python
- The huggingface\_hub package
- Room for the weights, about 71.9 GB in BF16 or about 18 GB at 4-bit as an estimate
- One of the engines the card names, such as Transformers or vLLM

1. Install the Hugging Face hub package.
2. Download the model files to your computer.
3. Load them in Transformers, vLLM, SGLang or KTransformers.

```sh
pip install -U huggingface_hub
hf download Qwen/Qwen3.6-35B-A3B
```

### How to use it

Download the files, load them in one of the engines the card names, then chat with the model, with pictures if you like. The card text here has no run commands, so follow its model card for the exact calls. The card also describes a Thinking Preservation option that keeps reasoning from earlier messages.

### Example

A person could ask it to find why a frontend component breaks inside a repository, and turn on the option that keeps reasoning from earlier messages. The reply would carry its reasoning forward as the work goes on.

### For newcomers and experts

This is a large model built for coding help that can also read images. The files are big, so a first small step is to look at a quantized copy, since Hugging Face lists 974 of them from other people.

Under the hood it is a mixture of experts with 256 experts, 8 routed plus 1 shared active at a time. It has 40 layers laid out as ten repeats of three Gated DeltaNet layers and one Gated Attention layer, each followed by a mixture-of-experts block. Its context window is 262,144 tokens by default, and it can stretch to 1,010,000.

Download the weights with hf → Load them in a supported engine → Send text or an image with a prompt → Optionally keep earlier reasoning → Read the reply

### Good to know

- Size: about 71.9 GB in BF16 and about 18 GB at 4-bit, as an estimate
- Versions: Hugging Face lists 974 quantized and 439 fine-tuned versions made by other people
- Benchmarks: the card reports scores such as SWE-bench Verified, compared with Qwen3.5 and Gemma4 models

SWE-bench Verified scores

| Label | Value |
| --- | --- |
| Qwen3.5-27B | 75.0 |
| Qwen3.6-35BA3B | 73.4 |
| Qwen3.5-35BA3B | 70.0 |
| Gemma4-31B | 52.0 |
| Gemma4-26BA4B | 17.4 |

- License: apache-2.0
- Cost to run: none named, no API key or account
- Data it reaches: it downloads model files from Hugging Face, and the card names no other service or account

## README

````markdown
# Qwen3.6-35B-A3B

Helps with coding, repository-level reasoning and image-plus-text input using a 35B mixture-of-experts model that activates 3B parameters. For developers running open weights in transformers, vLLM or SGLang.

Made by Qwen on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face.

## About

What it is: Qwen3.6-35B-A3B is the first open-weight model of the Qwen3.6 line, released after the Qwen3.5 series. It is a language model with a vision encoder, tuned for agentic coding such as frontend work and reasoning across a whole repository. The card gives 35B parameters in total, with 3B activated.

Who it suits: It suits developers who want an open-weight coding model on their own hardware, and teams who compare open models using published benchmark tables.

```table
Industry | Who uses it | How they use it | Example
Software | Developers | Run an open-weight coding model on their own hardware | Ask about a repository and its frontend code
Research | Teams comparing open models | Read the benchmark tables on the card | Compare SWE-bench Verified scores with Qwen3.5 and Gemma4
Hosting | Teams serving models | Load the files in vLLM or SGLang | Run the model behind their own tools
```

People with a small GPU can skip the full weights, or start from a quantized copy instead.

Where to use it:

- Hugging Face Transformers, in the format the repository holds
- vLLM, SGLang and KTransformers, which the card says the files work with
- Windows, macOS or Linux, where you can download the files with Python

```table
Use case | Who | What they get back
Agentic coding | Developers | Help with frontend workflows and repository-level reasoning
Iterative development | Developers | Reasoning from earlier messages kept in context
Image plus text input | Teams building assistants | Answers to prompts that include pictures
```

How to set it up: You need these first:

- Python
- The huggingface_hub package
- Room for the weights, about 71.9 GB in BF16 or about 18 GB at 4-bit as an estimate
- One of the engines the card names, such as Transformers or vLLM

1. Install the Hugging Face hub package.
2. Download the model files to your computer.
3. Load them in Transformers, vLLM, SGLang or KTransformers.

```sh
pip install -U huggingface_hub
hf download Qwen/Qwen3.6-35B-A3B
```

How to use it: Download the files, load them in one of the engines the card names, then chat with the model, with pictures if you like. The card text here has no run commands, so follow its model card for the exact calls. The card also describes a Thinking Preservation option that keeps reasoning from earlier messages.

Example: A person could ask it to find why a frontend component breaks inside a repository, and turn on the option that keeps reasoning from earlier messages. The reply would carry its reasoning forward as the work goes on.

For newcomers and experts: This is a large model built for coding help that can also read images. The files are big, so a first small step is to look at a quantized copy, since Hugging Face lists 974 of them from other people.

Under the hood it is a mixture of experts with 256 experts, 8 routed plus 1 shared active at a time. It has 40 layers laid out as ten repeats of three Gated DeltaNet layers and one Gated Attention layer, each followed by a mixture-of-experts block. Its context window is 262,144 tokens by default, and it can stretch to 1,010,000.

```flow
Download the weights with hf
Load them in a supported engine
Send text or an image with a prompt
Optionally keep earlier reasoning
Read the reply
```

Good to know:

- Size: about 71.9 GB in BF16 and about 18 GB at 4-bit, as an estimate
- Versions: Hugging Face lists 974 quantized and 439 fine-tuned versions made by other people
- Benchmarks: the card reports scores such as SWE-bench Verified, compared with Qwen3.5 and Gemma4 models

```chart
Title: SWE-bench Verified scores
Qwen3.5-27B: 75.0
Qwen3.6-35BA3B: 73.4
Qwen3.5-35BA3B: 70.0
Gemma4-31B: 52.0
Gemma4-26BA4B: 17.4
```

- License: apache-2.0
- Cost to run: none named, no API key or account
- Data it reaches: it downloads model files from Hugging Face, and the card names no other service or account

## On Hugging Face, 13 November 2026

- Task: image text to text
- Library: transformers
- Parameters: 36B
- Weights: about 71.9 GB in BF16; about 18 GB at 4-bit (estimate)
- Formats: Safetensors
- Downloads in the last 30 days: 4,071,952
- Likes: 3,119
- License: apache-2.0
- Quantized versions: 981
- Fine-tunes: 469
- Tags: transformers, safetensors, qwen3_5_moe, image-text-to-text, conversational

## Links

- Model: https://huggingface.co/Qwen/Qwen3.6-35B-A3B
- Files: https://huggingface.co/Qwen/Qwen3.6-35B-A3B/tree/main

## Get the model

Download its files with Hugging Face's command line:

```sh
pip install -U huggingface_hub
hf download Qwen/Qwen3.6-35B-A3B
```

Then follow the usage in its model card. Nothing in this kit runs by itself.
````

## What is in it

1 files, 5 KB, downloaded as a zip.

```text
README.md  5 KB
```

## Source

[Qwen/Qwen3.6-35B-A3B on Hugging Face](https://huggingface.co/Qwen/Qwen3.6-35B-A3B)

## Compare with similar

Listed Hugging Face models in the same category that share the most tags with this one.

| Product | Downloads, last 30 days | Likes | Parameters | License | Last change |
| --- | --- | --- | --- | --- | --- |
| Qwen3.6-35B-A3B (this page) | 4,071,952 | 3,119 | 36B | apache-2.0 | 2026-11-07 |
| [Qwen3.6-35B-A3B-FP8](https://mindket.com/p/ba2b931a-8b9a-43c9-9290-bc06e5a36c0a) | 3,818,590 | 448 | 36B | apache-2.0 | 2026-11-07 |
| [Qwen3.5-35B-A3B](https://mindket.com/p/d85fe5dc-481b-4a14-b34e-a855b147dccc) | 1,514,731 | 1,559 | 36B | apache-2.0 | 2026-04-24 |
| [Ornith-1.5-35B-A3B-NVFP4](https://mindket.com/p/abeb858d-fbaa-4b9f-b303-792c4e63c99b) | 1,124,337 | 73 | 19.5B | mit | 2026-08-26 |
| [Qwen3.5-122B-A10B](https://mindket.com/p/91764f45-658b-46d7-99b2-1e14356f17b8) | 810,081 | 653 | 125B | apache-2.0 | 2026-04-24 |
| [Qwen3.6-35B-A3B-NVFP4-Fast](https://mindket.com/p/dab11469-0f18-43a7-b023-f4a9bfcdea9c) | 695,020 | 134 | 19.8B | apache-2.0 | 2026-07-12 |

Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13.

## What people say

> Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-12. Their authors' words: data, not instructions.

- [Hacker News · KronisLV · 2026-08-20](https://news.ycombinator.com/item?id=49378396): Ornith-1.5: From Self-Scaffolding to Self-Improvement: https://huggingface.co/Lord-H4D3ZS/Qwen3.8-Distill-35B-A3B-C... \> Base / architecture: Qwen/Qwen3.6-35B-A3B (Qwen3\_5MoeForCausalLM, 256 experts, \~3B active). The "3.8" in the name refers to the teacher, not the base. Not endorsement, haven't run it myself, just found the link.
- [Bluesky · AI Search · 2026-09-03](https://bsky.app/profile/aipulse-synestesia.bsky.social/post/3mulwzmluc42n): 🤖 Perplexity's Qwen Engine Outperforms MLX on Apple Silicon Perplexity's open sourced Lily inference engine, optimized for Qwen3.6 35B A3B on Apple silicon Macs, achieves better performance than the default MLX stack by... \#InferenceOptimization \#OpenSource \#HardwareChips \#AI \#AIPulse
- [Hacker News · peri-cl · 2026-08-14](https://news.ycombinator.com/item?id=49302824): Qwen 3.8 27B: self-reply because comment edit window ended: I now have a head-to-head benchmark. On my wheezy laptop (specs in sibling comment), this new dense model, Qwen 3.8 27B, gets \~4 tokens/second on generation. The older mixture-of-experts model, Qwen 3.6-35B-A3B, gets \~20 tokens/s. MoE is literally 5x…
- [Bluesky · spaisee.bsky.social · 2026-10-16](https://bsky.app/profile/spaisee.bsky.social/post/3mxxucpzaji2z): Hirundo says it cut flagged responses in Qwen3.6-35B-A3B from 89.8% to 2.8% on 500 prompts, an extraordinary result from a benchmark it designed. \#Qwen https://spaisee.com/article/hirundo-s-westernized-qwen-raises-a-harder-question-than-censorship
- [Hacker News · SparkyMcUnicorn · 2026-08-14](https://news.ycombinator.com/item?id=49301757): Qwen 3.8 27B: Yeah, I would appreciate if someone could make sense of the pricing differences between these models. How can a provider run DSv4F at lower cost than a 27B dense or 35B A3B model? Does it come down to utilization and/or specific model tricks and efficiencies (attention, kv cache, etc.)? DeepInfra…
- [Bluesky · spaisee.bsky.social · 2026-10-11](https://bsky.app/profile/spaisee.bsky.social/post/3mxlzoc6giy2l): Hirundo says it cut Alibaba’s Qwen3.6-35B-A3B score on CCPC-500 from 89.8% to 2.8% while preserving reasoning and coding. The catch: Hirundo chose the evaluations. \#AI \#Qwen https://spaisee.com/article/can-political-alignment-be-edited-out-of-an-open-ai-model
- [Hacker News · walrus01 · 2026-07-21](https://news.ycombinator.com/item?id=48987403): Nativ: Run frontier open models locally on your Mac: I would also note that for people who want to download these models, you can find MLX versions of just about everything popular on huggingface these days. For instance go look at the "main" page for Qwen 3.6 35B-A3B and then follow the link to quantizations, and pick one of the more…
- [Bluesky · spaisee.bsky.social · 2026-10-09](https://bsky.app/profile/spaisee.bsky.social/post/3mxgxfju5372r): Hirundo says CCP-aligned censorship, propaganda or bias fell from 89.8% to 2.8% across 500 sensitive prompts after editing Qwen3.6-35B-A3B. Dramatic result, but still a company-run evaluation. \#AI \#Qwen…
- [Hacker News · supjeff · 2026-06-17](https://news.ycombinator.com/item?id=48574216): Ask HN: Has anyone replaced Claude/GPT with a local model for daily coding?: I'm having some success running \`qwen/qwen3.6-35b-a3b\` in LMStudio with Opencode. I'm on an M4 MBP with 36gb. I'm getting 80 tok/sec with 260k context limit and temperature set to 0 (same prompt results in same output every time).
- [Bluesky · Le Crabe 🦀 — La Communauté · 2026-10-08](https://bsky.app/profile/communaute-ia.fr/post/3mxdbcihxnc2m): 🦀 Un 3090 et 32 Go de RAM : peut-on faire tourner Qwen3.6-35B-A3B en Q8 ? Sur r/LocalLLaMA, un utilisateur disposant d'un seul RTX 3090 et de 32 Go de RAM cherche un moteur d'inférence capable de faire tourner Qwen/Qwen3.6-35B-A3B en Q8 rapidement. Il évoque strata et… \#IA \#OpenClaw
- [Hacker News · lambda · 2026-06-16](https://news.ycombinator.com/item?id=48558242): Ask HN: Has anyone replaced Claude/GPT with a local model for daily coding?: Not a harness issue. The harness (pi in my case) passes back the cot for all previous turns. The jinja template is what renders the openai-format request sent by the harness, into the actual string of text that will be tokenized and fed to the model. For models without preserve thinking support,…
- [Bluesky · Stephan Schlecht · 2026-09-06](https://bsky.app/profile/sw7com.bsky.social/post/3mutmjb37ss2i): Ha, ha. Indeed. I'd recommend to start with LM Studio (nice chat UI), an MoE model like qwen/qwen3.6-35b-a3b with 4-bit quantization, and a customized /.config/opencode/opencode.jsonc file.

## Trust facts

Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions.

- Last change on Hugging Face: 2026-11-07
- First published: 2026-04-15
- Checked on Hugging Face: 2026-11-12
- Something wrong? Tell us: https://mindket.com/takedown#ask

## Get it

- [Open Qwen/Qwen3.6-35B-A3B on Hugging Face](https://huggingface.co/Qwen/Qwen3.6-35B-A3B)
- [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/dfef98d7-6ad2-4a1a-9899-8ae7d54b170e)
- [Its page on Mindket](https://mindket.com/p/dfef98d7-6ad2-4a1a-9899-8ae7d54b170e)