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Model details

Qwen3.6 35B-A3B

Built on the Qwen lineage as a sparse mixture-of-experts design, this 35-billion-total-parameter model activates only about 3 billion parameters per token, pairing a large knowledge capacity with the inference efficiency typical of MoE routing. The release framing positions it as a successor to the prior 35B-A3B generation, with a stated emphasis on agentic coding workflows where it is reported to compete with substantially larger dense models. It continues to support both thinking and non-thinking operating modes, and is described as versatile across multimodal perception, reasoning, and structured generation tasks.

In practical use, the model fits developers and teams who want a single open-weight checkpoint for long-context work such as document analysis, code generation, and tool-driven agents, rather than juggling several specialized models. Its sparse activation profile makes it well suited for sustained agentic loops and structured-output pipelines where consistent latency and predictable tool calling matter. For organizations already standardizing on an OpenAI-compatible client surface, the model can be wired in with minimal integration effort, keeping the focus on task design rather than plumbing.

Zenifraalibaba/qwen3.6-35b-a3bqwen

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Provider
Zenifra
Model key
alibaba/qwen3.6-35b-a3b
Release date
Apr 17, 2026
Last updated
Apr 17, 2026
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.19
Output token cost
$0.48

Limits

Output tokens
65,536 tokens
Context window
262,144 tokens

Transparent token rates

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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Latest news about Qwen3.6 35B-A3B

Alibaba

Coverage

On April 2, 2026, Alibaba's Qwen team released Qwen3.6-35B-A3B as an open-weight, Apache 2.0 licensed variant of the Qwen3.6 generation on Hugging Face, paired with the closed Qwen3.6-Plus API model. The 35-billion-parameter Mixture-of-Experts model activates only 3B parameters per token and was framed with the tagline Qwen3.6-35B-A3B uses a sparse MoE with 256 experts (8 routed plus 1 shared activating per token), a 40-layer stack of three Gated DeltaNet linear-attention layers followed by one Gated Attention layer, a hidden dimension of 2048, an expert intermediate dimension of 512, and Multi-Token Prediction training for faster sp

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