Model
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Qwen3.6-35B-A3B

OPEN
Alibaba · Qwen3.6 family · released Apr 16, 2026

First open-weight model of the Qwen3.6 generation; sparse MoE (35B total/3B active) that substantially beats its direct predecessor Qwen3.5-35B-A3B and rivals much larger dense models on agentic coding, per the launch blog. This is the single real model behind the leads' 'Qwen3.6 (~35B, open-weights)' and 'Qwen3.6-35B-A3B' entries, which were duplicates of each other (see excluded).

ReasoningCodingVisionFunction callingTool useAgentic
1971.2
Elo · rank #60
Parameters
35B
Active params
3B (MoE)
Context
256K tokens
Architecture
Hybrid-attention MoE (Qwen3-Next lineage): 40 layers, 10x(3x(Gated DeltaNet -> MoE) -> 1x(Gated Attention -> MoE)); 256 experts, 8 routed + 1 shared activated per token; hidden dim 2048; natively multimodal (Causal LM with built-in vision encoder)
License
Apache 2.0
Languages
API price (in/out)
No hosted API
Modalities
text · vision
Benchmark results
Bar shows position within the tracked field; marker = field best
AI2DVision92.7%#16
best: Molmo 72B · 96.3%
AIMEMath92.7%#34
best: GPT-5.2 · 100.0%
C-EvalKnowledge90.0%#13
best: Qwen3.6-Plus · 93.3%
CharXivVision78.0%#15
best: Muse Spark 1.1 (xhigh) · 88.4%
GPQA DiamondReasoning86.0%#53
best: GPT-5.6 · 94.6%
Humanity's Last ExamReasoning21.4%#61
best: Claude Sonnet 5 · 57.4%
HumanEvalCoding93.3%#10
best: Claude Opus 4.5 · 99.4%
LiveCodeBenchCoding80.4%#47
best: DeepSeek-V4-Pro (Think Max) · 93.5%
MathVistaVision86.4%#7
best: Seed 2.1 Pro · 90.7%
MMBench (English)Vision92.8%#4
best: Qwen3.5-397B-A17B · 93.7%
MMLU-ProKnowledge85.2%#25
best: Claude Fable 5 · 91.5%
MMLU-ReduxKnowledge93.3%#13
best: Qwen3.7-Max · 95.0%
MMMU-ProVision75.3%#24
best: Claude Opus 4.7 · 85.5%
MMMUVision81.7%#19
best: Claude Fable 5 · 89.3%
RefCOCOVision92.0%#9
best: Qwen3.5-Omni-Plus · 95.0%
SuperGPQAReasoning64.7%#11
best: Qwen3.7-Max · 73.6%
SWE-bench ProCoding49.5%#38
best: Claude Fable 5 · 80.0%
SWE-bench VerifiedCoding73.4%#48
best: Claude Fable 5 · 95.0%
Terminal-Bench 2.0Coding51.5%#45
best: GPT-5.6 · 88.8%
Video-MMEVision86.6%#7
best: Seed 2.1 Pro · 89.2%
Run it locally
VRAM @ Q4
VRAM @ FP16
Fits on (Q4)
Multi-node cluster required
Throughput data unavailable.
Quantizations
API price weights · each benchmark row carries its own source badge (see methodology)