Qwen3.5-397B-A17B is the downloadable open-weight checkpoint; Qwen3.5-Plus is its managed hosted counterpart in Alibaba Cloud Model Studio. They are different ways to access the model, not two names for the same download. Alibaba reports strong results across several benchmarks, but those figures are vendor-reported—not independent test results. The available sources do not document a minimum local hardware setup, so the checkpoint’s size alone is not enough to recommend a particular computer or GPU.
What are Qwen3.5-397B-A17B and Qwen3.5-Plus?
Alibaba Cloud announced Qwen3.5-397B-A17B on February 17, 2026, as the first open-weight model in the Qwen3.5 series. The official Qwen repository provides its model weights and configuration files. Qwen3.5-Plus is the hosted version corresponding to that checkpoint, offered through Model Studio with managed API access and production features.
That distinction matters: downloading the repository is not the same as calling the hosted service. With the open-weight checkpoint, deployment and inference are self-managed. With Qwen3.5-Plus, Model Studio handles hosted inference, subject to its regional feature set, limits, and pricing.
What does the open-weight checkpoint include?
The official Qwen repository lists the model as Apache-2.0 and provides weights and configuration files in Transformers format. It lists compatibility with Transformers, vLLM, SGLang, and KTransformers. Consult the repository’s actual license text for questions about a particular use; the license label alone is not legal advice.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteThe evidence available here does not establish minimum GPU memory, system RAM, disk space, or a required multi-GPU configuration. A parameter count or repository download size is not, by itself, a reliable hardware recommendation. If you do not want to provision and operate inference infrastructure, the hosted API is the documented alternative.
How do the open weights and hosted API differ?
| Access path | What you get | What to plan for |
|---|---|---|
| Qwen3.5-397B-A17B | Downloadable model weights and configuration files; repository-listed compatibility with Transformers, vLLM, SGLang, and KTransformers. | You manage deployment and inference. An authoritative minimum hardware configuration is not established in the sources cited here. |
| Qwen3.5-Plus | Managed Model Studio API access, documented multimodal input, and production features that include function calling and structured outputs in listed regions. | Features and availability depend on deployment region; usage is charged by token rates that vary by region and input length. |
What are the model’s architecture and scale?
Alibaba describes Qwen3.5-397B-A17B as a native vision-language model that combines Gated Delta Networks, a form of linear attention, with sparse mixture-of-experts. Alibaba reports 397 billion total parameters and 17 billion activated per forward pass, and says the model supports 201 languages and dialects, up from 119 previously. These are publisher specifications, not independent measurements.
What does Model Studio document for Qwen3.5-Plus?
Model Studio’s Qwen3.5-Plus information page, last updated September 28, 2026, documents text, image, and video input with text output. It lists a 1,000,000-token context window, a maximum of 991,808 input tokens, and up to 65,536 output tokens. Those are documented service limits, not a guarantee that every feature is enabled in every deployment region.
Regional feature availability
The same documentation lists function calling, structured outputs, prefix completion, and context caching in the regions it covers. Web search is listed as supported in Beijing, Singapore, and Virginia, but unsupported in Frankfurt. Batch inference is listed as available in Beijing and unsupported in the other listed regions. Fine-tuning is marked unsupported. Check the Model Studio documentation for the intended deployment region before building around a particular capability.
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The documentation says the current unversioned model is functionally equivalent to the qwen3.5-plus-2026-02-15 snapshot. It also describes a later qwen3.5-plus-2026-04-20 snapshot with improved agentic coding and inference speed. Do not assume the dated snapshots are identical: pin the intended model identifier when version-specific behavior matters.
What do Alibaba’s benchmark results show?
Alibaba’s February 17, 2026 announcement publishes the following scores for Qwen3.5-397B-A17B. They are vendor-reported results; the sources available here do not establish an independent benchmark run. The figures show performance across different task types, not a single overall ranking.
| Benchmark | Alibaba-reported score | What it evaluates, broadly |
|---|---|---|
| MMLU-Pro | 87.8 | Broad knowledge and reasoning questions |
| IFBench | 76.5 | Instruction following |
| LongBench v2 | 63.2 | Long-context tasks |
| GPQA | 88.4 | Graduate-level science questions |
| LiveCodeBench v6 | 83.6 | Coding problems |
| BFCL-V4 | 72.9 | Function-calling tasks |
| BrowseComp | 69.0/78.6 | Preserved as the two-part figure printed in Alibaba’s table |
| HLE; HLE-Verified | 28.7; 37.6 | Humanity’s Last Exam and its verified set |
The announcement’s comparison table does not show Qwen3.5-397B-A17B leading every listed model on every task: it is not the highest listed score for MMLU-Pro, GPQA, LiveCodeBench v6, or HLE. Benchmark scores depend on the evaluation and comparison setup, so the table should not be read as proof that the model is universally better than competitors.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How much does the Qwen3.5-Plus API cost?
Model Studio’s documentation, last updated September 28, 2026, lists original API prices, excludes limited-time promotions, and varies rates by region and input length. For the Singapore international deployment, the documented rates are:
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware match| Input length | Input price per million tokens | Output price per million tokens |
|---|---|---|
| Up to 256k tokens | $0.40 | $2.40 |
| Above 256k through 1m tokens | $0.50 | $3.00 |
These are Singapore international rates, not a global price. Beijing, Frankfurt, and Virginia have separate tables; check the current Model Studio pricing page for your deployment region and account before estimating spend.
Quick Recap
Which access path should you choose?
- Choose the open-weight checkpoint if you need downloadable weights and are prepared to select, provision, and operate a compatible inference stack. Confirm hardware requirements for your intended deployment rather than inferring them from the model’s parameter count.
- Choose Qwen3.5-Plus if you want managed inference and can work within Model Studio’s regional feature availability, context and output limits, and token pricing.
- Check a specific tool or workflow first if your use case depends on web search, batch inference, or a dated model snapshot; availability and behavior are not interchangeable across every region or model identifier.
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