Model
Model explorer

Qwen3.6-27B

OPEN
Alibaba · Qwen3.6 family · released Apr 22, 2026

Not in the original flagged leads, but discovered via direct primary-source research (qwen.ai/blog and github.com/QwenLM/Qwen3.6) while verifying the flagged models -- and arguably the single most important model actually missing, since it is explicitly billed by Alibaba as surpassing the prior open-weight flagship Qwen3.5-397B-A17B (15x its parameter count) on every major agentic-coding benchmark despite being fully dense at 27B params.

ReasoningCodingVisionFunction callingTool useAgentic
2041.8
Elo · rank #56
Parameters
27B
Active params
Undisclosed
Context
256K tokens
Architecture
Hybrid-attention dense model (Qwen3-Next lineage, no MoE routing): 64 layers, 16x(3x(Gated DeltaNet -> FFN) -> 1x(Gated Attention -> FFN)); hidden dim 5120, FFN intermediate dim 17408; natively multimodal (Causal LM with built-in vision encoder)
License
Apache 2.0
Languages
API price (in/out)
$0.6 / $3.6
Modalities
text · vision
Benchmark results
Bar shows position within the tracked field; marker = field best
AIMEMath94.1%#22
best: GPT-5.2 · 100.0%
C-EvalKnowledge91.4%#11
best: Qwen3.6-Plus · 93.3%
CharXivVision78.4%#13
best: Muse Spark 1.1 (xhigh) · 88.4%
GPQA DiamondReasoning87.8%#39
best: GPT-5.6 · 94.6%
Humanity's Last ExamReasoning24.0%#59
best: Claude Sonnet 5 · 57.4%
LiveCodeBenchCoding83.9%#29
best: DeepSeek-V4-Pro (Think Max) · 93.5%
MathVistaVision87.4%#5
best: Seed 2.1 Pro · 90.7%
MMBench (English)Vision92.3%#5
best: Qwen3.5-397B-A17B · 93.7%
MMLU-ProKnowledge86.2%#22
best: Claude Fable 5 · 91.5%
MMLU-ReduxKnowledge93.5%#11
best: Qwen3.7-Max · 95.0%
MMLUKnowledge84.5%#63
best: OpenAI o3 · 92.9%
MMMU-ProVision75.8%#23
best: Claude Opus 4.7 · 85.5%
MMMUVision82.9%#16
best: Claude Fable 5 · 89.3%
OCRBenchVision89#17
best: InternVL3-78B · 906
RefCOCOVision92.5%#8
best: Qwen3.5-Omni-Plus · 95.0%
SuperGPQAReasoning66.0%#9
best: Qwen3.7-Max · 73.6%
SWE-bench ProCoding53.5%#30
best: Claude Fable 5 · 80.0%
SWE-bench VerifiedCoding77.2%#32
best: Claude Fable 5 · 95.0%
Terminal-Bench 2.0Coding59.3%#34
best: GPT-5.6 · 88.8%
Video-MMEVision87.7%#5
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 $0.6/$3.6 · each benchmark row carries its own source badge (see methodology)