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Prompt guide
MediaQwen3.5 Plus

Qwen3.5-Plus: Alibaba Cloud Model Studio multimodal, long-context, and tool-calling configuration

Original source

Alibaba Cloud Model Studio (Bailian) official documentation

AuthorAlibaba Cloud Model Studio

Tabbit curation2026-08-19

Read original

One-sentence takeaway

To reproduce Qwen3.5-Plus's million-token context and Agent workflows, fix the model snapshot, deployment region, input modalities, and tool capabilities together.

Use cases

  • Suitable tasks: long-document/codebase analysis, image or video understanding, structured extraction, function calling, and web-search workflows.

  • Unsuitable tasks: fine-tuning the model, directly transferring web-search capabilities from one region to another, or requiring audio input not listed on the official page.

  • Applicable model versions: qwen3.5-plus defaults to qwen3.5-plus-2026-02-15; the page also lists the qwen3.5-plus-2026-04-20 snapshot.

  • Applicable client, Agent, or API: Alibaba Cloud Model Studio; compatibility interfaces and specific tool orchestration are subject to the service-side documentation.

  • Recommended inference tier and parameters: the official page does not disclose recommended temperature/top_p values; start with the service defaults, fix the snapshot, and set the output limit to no more than 65,536 tokens. For stable reproduction, do not mix the unfixed qwen3.5-plus alias with a fixed snapshot.

Ready-to-use content

The following configuration template is organized from the official capability table; it is not the official source code. Replace region, snapshot, and the tool allowlist for the actual tenant.

Model: qwen3.5-plus-2026-02-15
Region: <actual Model Studio deployment region>
Input: text + image/video (choose according to the task)
Output: text
Context window: no more than 1,000,000 tokens
Maximum input: no more than 991,808 tokens
Maximum output: no more than 65,536 tokens

Tool allowlist:
1. function calling: register only the functions required to complete the task, and specify the type, range, and failure return for each parameter.
2. structured outputs: specify a fixed JSON schema; return null for fields that cannot be confirmed, and do not guess.
3. web search: first confirm that the capability table for the current deployment region is marked “supported,” then require the model to cite sources for external facts.

System constraints:
- First determine the input modality and task objective, then select tools.
- Before each tool call, state its purpose, inputs, and expected result; when a tool fails, return a diagnosable error and stop retrying.
- The final output must contain only the agreed schema, along with the snapshot, region, and tool execution status.

Test/workflow steps

  1. Create or select a fixed qwen3.5-plus-2026-02-15 endpoint in Model Studio, and record the region and request time.

  2. Using the same text, image, or video input, run a no-tool baseline once and record input/output token counts and latency.

  3. Enable only one function, repeat the task, and check whether the function arguments conform to the schema; then add structured output and web search step by step.

  4. For inputs exceeding 128k and 256k, record the billing tier and cache-hit status separately; do not conflate capability tables from different regions.

  5. If switching to qwen3.5-plus-2026-04-20, rerun the complete baseline and compare Agentic coding, speed, and long-context performance separately.

Original evidence and data

  • The official capability table lists text, image, and video input and text output; function calling, structured outputs, and prefix continuation are all supported.

  • The China (Beijing) region table lists web search, context caching, and batch inference as supported; web search, caching, and batch capabilities in the international/global tables vary with the deployment scope.

  • The context window is 1,000,000, the maximum input length is 991,808, and the maximum output length is 65,536; in thinking mode, the maximum input is 983,616 and the maximum chain-of-thought length is 81,920.

  • In China (Beijing), the list price is tiered by input length: for no more than 128k, input is RMB 0.8 per million tokens and output is RMB 4.8 per million tokens; for 128k–256k, it is 2/12; for 256k–1m, it is 4/24. The page also lists international and global prices; the endpoint region determines the actual price.

  • The official description of the 2026-04-20 snapshot says that it improves Agentic coding and inference speed over 2026-02-15 and is suitable for coding agents, production workflows, and high-throughput scenarios; this is a version note, not an independent controlled evaluation.

Applicability boundaries

  • “Web search supported” is listed by region/deployment scope; support in the China region should not be taken to imply that international or global endpoints necessarily support it.

  • The page does not provide a complete system prompt, tool schema, or recommended temperature. The prompts and parameter limits in this article are a reusable organization of the material and should not be presented as official source text.

  • The official page does not list audio input; for audio, choose a model or conversion workflow that explicitly supports it.

  • 2026-04-20 is a separate snapshot; combining it with results from the default alias would undermine reproducibility.

Source excerpt or observation (compliance short quote only)

The official documentation describes the default version as “functionally equivalent to the snapshot model qwen3.5-plus-2026-02-15” and lists the 2026-04-20 snapshot separately; the configuration above follows that boundary.

Curated by Tabbit

Prompt material is summarized from public sources and Tabbit editorial notes. Check the original licensing and intended use before copying it.

Qwen3.5 Plus

Use in Tabbit

Qwen3.5 Plus

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