Generate music, create lifelike speech, clone voices, separate stems, repair sections, record, mix, master, and automate full audio workflows. Foundry runs locally on Windows with an NVIDIA GPU, so your scripts, voices, songs, and client audio stay on your machine.

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No cloud generation / No credit meters / Voice cloning included / Built for private production

50+

Music languages

Speech languages

50 x 5

Emotion control

200+

DSP presets

120+

Commands

Music

Songs, vocals, structure, style, and language control.

Create full tracks, extend ideas, transform references, patch weak sections, and generate multilingual songs locally.

Compare music generators

Speech

Expressive narration that does not sound like filler.

Generate speech in 10 languages, direct emotion line by line, clone voices from short samples, and build multi-speaker scenes.

Explore local AI voice

Studio

A real production workspace, not just a prompt field.

Record, arrange, stem-split, patch, crossfade, process with DSP, mix on a DAW-style timeline, and export final audio.

See what is inside

Local creative agent

Give the boring preparation work to the assistant.

Use the built-in agent for lyrics, music briefs, script preparation, speaker extraction, emotion planning, literature summaries, narration segmentation, batch workflows, and repeatable production tasks.

foundry analyze manuscript.pdf
foundry extract speakers and emotions
foundry generate narration
foundry compose intro music
foundry mix and export

Pick your workflow

YouTubers and faceless channels

Voiceovers, hooks, background music, repeatable production, no per-video credit anxiety.

Game developers

NPC dialogue, character voices, batch generation, ambience, score, and engine-ready export.

Audiobooks and podcasts

Long-form narration, multi-speaker scenes, private manuscripts, music beds, and final export.

Speech studios by language

English Deutsch Français Español Italiano 中文日本語 Русский Português 한국어

Learn fast

Getting started Emotion manager Patching Creative AI agent

Deep dives

Voice cloning ElevenLabs alternative Automation Privacy

Model

This model is hosted for Demodokos Foundry but it can be used for other purposes, enjoy a stable download location and custom quantizations not available elsewhere.

Qwen3.6 Highlights

This release delivers substantial upgrades, particularly in

Agentic Coding: the model now handles frontend workflows and repository-level reasoning with greater fluency and precision.
Thinking Preservation: we've introduced a new option to retain reasoning context from historical messages, streamlining iterative development and reducing overhead.

For more details, please refer to our blog post Qwen3.6-35B-A3B.

Model Overview

Type: Causal Language Model with Vision Encoder
Training Stage: Pre-training & Post-training
Language Model
- Number of Parameters: 35B in total and 3B activated
- Hidden Dimension: 2048
- Token Embedding: 248320 (Padded)
- Number of Layers: 40
- Hidden Layout: 10 × (3 × (Gated DeltaNet → MoE) → 1 × (Gated Attention → MoE))
- Gated DeltaNet:
  - Number of Linear Attention Heads: 32 for V and 16 for QK
  - Head Dimension: 128
- Gated Attention:
  - Number of Attention Heads: 16 for Q and 2 for KV
  - Head Dimension: 256
  - Rotary Position Embedding Dimension: 64
- Mixture Of Experts
  - Number of Experts: 256
  - Number of Activated Experts: 8 Routed + 1 Shared
  - Expert Intermediate Dimension: 512
- LM Output: 248320 (Padded)
- MTP: trained with multi-steps
Context Length: 262,144 natively and extensible up to 1,010,000 tokens.

Benchmark Results

Language

	Qwen3.5-27B	Gemma4-31B	Qwen3.5-35BA3B	Gemma4-26BA4B	Qwen3.6-35BA3B
Coding Agent
SWE-bench Verified	75.0	52.0	70.0	17.4	73.4
SWE-bench Multilingual	69.3	51.7	60.3	17.3	67.2
SWE-bench Pro	51.2	35.7	44.6	13.8	49.5
Terminal-Bench 2.0	41.6	42.9	40.5	34.2	51.5
Claw-Eval _Avg	64.3	48.5	65.4	58.8	68.7
Claw-Eval _Pass^3	46.2	25.0	51.0	28.0	50.0
SkillsBench _Avg5	27.2	23.6	4.4	12.3	28.7
QwenClawBench	52.2	41.7	47.7	38.7	52.6
NL2Repo	27.3	15.5	20.5	11.6	29.4
QwenWebBench	1068	1197	978	1178	1397
General Agent
TAU3-Bench	68.4	67.5	68.9	59.0	67.2
VITA-Bench	41.8	43.0	29.1	36.9	35.6
DeepPlanning	22.6	24.0	22.8	16.2	25.9
Tool Decathlon	31.5	21.2	28.7	12.0	26.9
MCPMark	36.3	18.1	27.0	14.2	37.0
MCP-Atlas	68.4	57.2	62.4	50.0	62.8
WideSearch	66.4	35.2	59.1	38.3	60.1
Knowledge
MMLU-Pro	86.1	85.2	85.3	82.6	85.2
MMLU-Redux	93.2	93.7	93.3	92.7	93.3
SuperGPQA	65.6	65.7	63.4	61.4	64.7
C-Eval	90.5	82.6	90.2	82.5	90.0
STEM & Reasoning
GPQA	85.5	84.3	84.2	82.3	86.0
HLE	24.3	19.5	22.4	8.7	21.4
LiveCodeBench v6	80.7	80.0	74.6	77.1	80.4
HMMT Feb 25	92.0	88.7	89.0	91.7	90.7
HMMT Nov 25	89.8	87.5	89.2	87.5	89.1
HMMT Feb 26	84.3	77.2	78.7	79.0	83.6
IMOAnswerBench	79.9	74.5	76.8	74.3	78.9
AIME26	92.6	89.2	91.0	88.3	92.7

* SWE-Bench Series: Internal agent scaffold (bash + file-edit tools); temp=1.0, top_p=0.95, 200K context window. We correct some problematic tasks in the public set of SWE-bench Pro and evaluate all baselines on the refined benchmark.
* Terminal-Bench 2.0: Harbor/Terminus-2 harness; 3h timeout, 32 CPU/48 GB RAM; temp=1.0, top_p=0.95, top_k=20, max_tokens=80K, 256K ctx; avg of 5 runs.
* SkillsBench: Evaluated via OpenCode on 78 tasks (self-contained subset, excluding API-dependent tasks); avg of 5 runs.
* NL2Repo: Others are evaluated via Claude Code (temp=1.0, top_p=0.95, max_turns=900).
* QwenClawBench: An internal real-user-distribution Claw agent benchmark (open-sourcing soon); temp=0.6, 256K ctx.
* QwenWebBench: An internal front-end code generation benchmark; bilingual (EN/CN), 7 categories (Web Design, Web Apps, Games, SVG, Data Visualization, Animation, and 3D); auto-render + multimodal judge (code/visual correctness); BT/Elo rating system.
* TAU3-Bench: We use the official user model (gpt-5.2, low reasoning effort) + default BM25 retrieval.
* VITA-Bench: Avg subdomain scores; using claude-4-sonnet as judger, as the official judger (claude-3.7-sonnet) is no longer available.
* MCPMark: GitHub MCP v0.30.3; Playwright responses truncated at 32K tokens.
* MCP-Atlas: Public set score; gemini-2.5-pro judger.
* AIME 26: We use the full AIME 2026 (I & II), where the scores may differ from Qwen 3.5 notes.

Vision Language

	Qwen3.5-27B	Claude-Sonnet-4.5	Gemma4-31B	Gemma4-26BA4B	Qwen3.5-35B-A3B	Qwen3.6-35B-A3B
STEM and Puzzle
MMMU	82.3	79.6	80.4	78.4	81.4	81.7
MMMU-Pro	75.0	68.4	76.9*	73.8*	75.1	75.3
Mathvista(mini)	87.8	79.8	79.3	79.4	86.2	86.4
ZEROBench_sub	36.2	26.3	26.0	26.3	34.1	34.4
General VQA
RealWorldQA	83.7	70.3	72.3	72.2	84.1	85.3
MMBench_EN-DEV-v1.1	92.6	88.3	90.9	89.0	91.5	92.8
SimpleVQA	56.0	57.6	52.9	52.2	58.3	58.9
HallusionBench	70.0	59.9	67.4	66.1	67.9	69.8
Text Recognition and Document Understanding
OmniDocBench1.5	88.9	85.8	80.1	74.4	89.3	89.9
CharXiv(RQ)	79.5	67.2	67.9	69.0	77.5	78.0
CC-OCR	81.0	68.1	75.7	74.5	80.7	81.9
AI2D_TEST	92.9	87.0	89.0	88.3	92.6	92.7
Spatial Intelligence
RefCOCO(avg)	90.9	--	--	--	89.2	92.0
ODInW13	41.1	--	--	--	42.6	50.8
EmbSpatialBench	84.5	71.8	--	--	83.1	84.3
RefSpatialBench	67.7	--	--	--	63.5	64.3
Video Understanding
VideoMME_{(w sub.)}	87.0	81.1	--	--	86.6	86.6
VideoMME_{(w/o sub.)}	82.8	75.3	--	--	82.5	82.5
VideoMMMU	82.3	77.6	81.6	76.0	80.4	83.7
MLVU	85.9	72.8	--	--	85.6	86.2
MVBench	74.6	--	--	--	74.8	74.6
LVBench	73.6	--	--	--	71.4	71.4

* Empty cells (--) indicate scores not available or not applicable.

Citation

If you find our work helpful, feel free to give us a cite.

bibtex

@misc{qwen36_35b_a3b,
    title = {{Qwen3.6-35B-A3B}: Agentic Coding Power, Now Open to All},
    url = {https://qwen.ai/blog?id=qwen3.6-35b-a3b},
    author = {{Qwen Team}},
    month = {April},
    year = {2026}
}

cmp-nct/Qwen3.6-35B-A3B-GGUF

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