Qwen3.6 35B A3B is a native vision-language MoE model with hybrid attention. Compared to Qwen3.5 35B A3B, Alibaba reports stronger agentic coding, mathematical and code reasoning, and better spatial understanding (including object localization and detection).
Added Apr 17, 2026
Model weightsContext Window
262.1K
Max Output
16.4K
Avg output tokens (7d)
1.8K tokens
Input Price (Auto)
$0.12/1M
Output Price (Auto)
$0.84/1M
Cache Read (Auto)
$0.059/1M
Capabilities
Benchmarks
Benchmarks
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
24.6
Coding Index
28.1
Reasoning
GPQA Diamond
Graduate-level scientific reasoning
81.7%
Better than 73% of models compared
HLE
Humanity's Last Exam
13.9%
Better than 67% of models compared
IFBench
Instruction-following benchmark
36.2%
Better than 28% of models compared
T²-Bench Telecom
Conversational AI agents in dual-control scenarios
85.1%
Better than 77% of models compared
AA-LCR
Long context reasoning evaluation
60.0%
Better than 62% of models compared
GDPval-AA
Economically valuable tasks
26.0%
CritPt
Research-level physics reasoning
0.0%
Coding
SciCode
Python programming for scientific computing
1.3%
Better than 1% of models compared
Terminal-Bench Hard
Agentic coding and terminal use
25.8%
Better than 67% of models compared
Knowledge
AA-Omniscience Accuracy
Proportion of correctly answered questions
16.7%
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
92.1%
Last updated Aug 16, 2026
Artificial AnalysisProviders
Choose explicit providers for this model. Auto routing remains available as the default option.
Loading provider options…
Related text models
Compare Qwen3.6 35B A3B with similar models from the same provider or model family.
Qwen3.6 35B A3B Thinking
Qwen/Qwen3.6-35B-A3B:thinkingQwen3.6 35B A3B is a native vision-language MoE model with hybrid attention. Compared to Qwen3.5 35B A3B, Alibaba reports stronger agentic coding, mathematical and code reasoning, and better spatial understanding (including object localization and detection).
Qwen3 Next 80B A3B (Instruct)
Qwen/Qwen3-Next-80B-A3B-InstructBased on the new Qwen3‑Next architecture (hybrid attention, highly sparse MoE, training‑stability optimizations, and multi‑token prediction), the Qwen3‑Next‑80B‑A3B‑Instruct model delivers extreme efficiency with only 3B active parameters per pass. It performs comparably to Qwen3‑235B‑A22B‑Instruct‑2507 and shows clear advantages on ultra‑long context tasks (up to 256K tokens).
Qwen3 30B A3B
qwen/qwen3-30b-a3bQwen 3 30b A3B is a 30b model with 3 billion active parameters per pass. Supports switching between thinking and non thinking: trigger thinking with /think and /no_think anywhere in a prompt or system message to toggle chain-of-thought reasoning.
Qwen3 Next 80B A3B (Thinking)
qwen/qwen3-next-80b-a3b-thinkingQwen3 Next 80B A3B (Thinking)
Qwen 3 Coder 480B
qwen/qwen3-coderQwen 3 Coder 480B, a 480 billion total parameter model with 35B active, and 160 total experts with 8 active. Performs similar to Claude 4 Sonnet in coding benchmarks, but does so at a much lower price.
Qwen3.5 9B
qwen/qwen3.5-9bQwen3.5 9B is a multimodal foundation model from the Qwen 3.5 family, built for efficient reasoning, coding, and visual understanding in a compact 9B architecture.