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Model details

Qwen3.5 397B A17B

Qwen3.5-397B-A17B is a large-scale mixture-of-experts model in Alibaba's Qwen3.5 family, designed for the agentic AI era as a vision-language foundation that combines multimodal understanding with long-context inference. Production deployments can take advantage of MTP speculative decoding and W8A8 quantization for efficient serving, particularly on Ascend hardware, with vLLM-Ascend providing first-class support from v0.17.0rc1 onward. The model targets workflows where both rich input understanding and extended context are needed, such as document analysis, multimodal assistants, and tool-driven agents, while aiming to be cheaper and more efficient than its predecessor with frontier-class benchmark performance.

As an open-weights release under the Apache 2.0 license, Qwen3.5-397B-A17B is well suited to organizations that want on-premises or private-cloud control over a high-capacity MoE model without paying frontier proprietary prices. The combination of multimodal input handling, long-context inference, and accelerated decoding paths makes it a practical choice for teams building agentic pipelines that mix vision, text, and tool use. For buyers who need even larger context windows, Alibaba is also offering Qwen3.5-Plus with a one-million-token context, giving teams a clear upgrade path within the same model lineage when workloads demand it.

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Provider
Scaleway
Model key
qwen3.5-397b-a17b
Release date
Mar 17, 2026
Last updated
Mar 17, 2026
Knowledge cutoff
2025-04
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.60
Output token cost
$3.60

Limits

Output tokens
16,384 tokens
Context window
256,000 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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CoverageBenchmark

InferenceX (SemiAnalysis) provides a technical deep-dive on Qwen3.5-397B-A17B, describing it as a native vision-language foundation model with 397B total parameters and 17B activated per forward pass, released under Apache 2.0 with weights published on Hugging Face as a post-trained checkpoint compatible with Transform The model unifies thinking and non-thinking behavior in one checkpoint, operating in thinking mode by default and emitting ... tokens before answers; unlike Qwen3, it drops the /think and /nothink soft switches, so non-thinking responses are obtained via API parameters. Alibaba frames the model as targeting reasoning,

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