BaiLian

Qwen3.5 Plus

AlibabaToken-based
Alias:qwen3.5-plus
Create API Key

Compare qwen3.5-plus API pricing, supported endpoints, capabilities and access options on voguesly.

textimagevideofunction_callingtoolsstructured_outputjson_modeweb_searchcachingvisionreasoningcontext:1000000
Starting price
Input / Output · 1M
Context
1M
Maximum input window
Modalities
→

Pricing by Supplier

official
-40%
官方接口直连
Input/ 1M tokens
$0.06576$0.1096
Output/ 1M tokens
$0.39456$0.6576
Cache Read/ 1M tokens
$0.006576$0.01096
Alibaba
阿里巴巴百炼官方
Input/ 1M tokens
$0.1096
Output/ 1M tokens
$0.6576
Cache Read/ 1M tokens
$0.01096

Capabilities / Supported modalities

ReasoningFunction callingToolsStructured outputJSON modeWeb searchCode interpreter
Input
Output

Provider & data privacy

Provider
Alibaba (Qwen)Docs
Tokenizer
Qwen tokenizer (tiktoken-compat)
License
Tongyi Qianwen LicenseOpen weights
Data retention48 daysNot used for upstream training by default

Performance

Benchmarks

Scores on standardized evaluations. Higher percentages are better — and rank percentile shows

Metrics sourced fromArtificial Analysis 2026-09-30·Qwen3.5 Plus

This model is not included in the current benchmark snapshot.

Missing models or measurements are not zero scores.

Benchmark charts preserve the source model selection and reasoning settings. Missing models or measurements are not zero scores, and benchmark cost or speed is not this site’s service commitment.

About Qwen3.5 Plus

把分散材料连成一份分析

Qwen3.5 Plus 可结合文字、图片和视频完成问答、分析与内容生成。它适合同时阅读需求说明、界面截图和数据图表,找出信息之间的联系,再整理成方案、检查清单或实现步骤。产品评审、研究材料梳理和工程协作,都可以把相关资料与判断标准一起交给它。

它提供百万 Token 级上下文,可容纳较长的项目材料。实际使用时,给文件、页面和代码片段添加编号,明确哪些是当前规则、哪些是历史讨论,再要求每个结论对应证据。分析视频时,可指定关注的动作、场景变化或操作步骤;若任务依赖讲话内容,应另行提供转写文本。

怎样处理需要多步判断的任务?

模型支持混合思考模式,可用 enable_thinking 控制。需要解释设计取舍、排查代码问题或核对多项约束时,开启思考并要求最终回复列出结论、依据与尚未确定的条件。简单改写和字段整理可以关闭思考,缩短处理流程。

函数调用适合把分析连接到真实数据:模型选择工具并生成参数,应用执行查询、计算或业务操作后,再回传结果继续回答。联网搜索和代码解释器等内置工具需要在调用时配置;最终结论应依据实际工具结果。

能直接生成程序需要的 JSON 吗?

需要 JSON Mode 时,应关闭思考,并明确字段、类型及缺失值规则。模型生成的结构仍要经过程序校验;涉及金额合计、日期计算或规则判定时,使用确定性的计算与校验步骤,便于发现遗漏。

Use cases and prompting

例如评审一个导出功能,上传需求文档、现有页面截图与相关代码后输入:

“目标是确认导出任务从创建到下载是否完整。请结合材料找出状态展示、失败处理和重复点击三方面的问题。先列影响用户的结论,再按‘材料编号、具体位置、依据、建议修改’组织表格;区分现有行为与建议方案。没有代码支持的判断写成待确认项,最后给出可执行的验收步骤。”

此类联合分析可设置 enable_thinking=true,并使用流式输出接收结果。若后续要把问题清单写入系统,再关闭思考,配置 response_format={"type":"json_object"},指定 JSON 字段后生成,并在保存前校验。

API access

API documentation

Code samples

RequestPOST/v1/chat/completions
Example request
Parameters
ParameterTypeDefault / rangeDescription
temperature
number
=10 ~ 2
Sampling temperature; lower is more deterministic
top_p
number
=10 ~ 1
Nucleus sampling probability mass
max_tokens
integer>= 1Maximum number of tokens in the response
frequency_penalty
number
=0-2 ~ 2
Penalises repetition of frequent tokens
presence_penalty
number
=0-2 ~ 2
Encourages introducing new topics
stop
array—Up to 4 strings that stop generation
seed
integer—Deterministic sampling seed (best-effort)
n
integer
=1>= 1
Number of completions to generate
stream
boolean
=false
Stream tokens via Server-Sent Events
response_format
object—Force JSON object or schema-conforming output
tools
array—Tool / function declarations the model may call
tool_choice
string
autononerequired
Tool-choice policy or specific tool name
logprobs
boolean
=false
Return per-token log probabilities
top_logprobs
integer0 ~ 20Number of top log probabilities returned per token
logit_bias
object—Per-token logit bias map
user
string—End-user identifier for abuse monitoring

Replace <YOUR_API_KEY> with the API key from your token settings.

Authentication

All requests must include Authorization: Bearer <TOKEN> header. Anthropic-formatted endpoints accept the x-api-key header instead.

Generate tokens from the Tokens page; you can scope them to specific models, groups, IPs, and rate-limits.

Supported parameters

Generation parameters
ParameterTypeDefault / rangeDescription
temperature
number
=10 ~ 2
Sampling temperature; lower is more deterministic
top_p
number
=10 ~ 1
Nucleus sampling probability mass
max_tokens
integer>= 1Maximum number of tokens in the response
frequency_penalty
number
=0-2 ~ 2
Penalises repetition of frequent tokens
presence_penalty
number
=0-2 ~ 2
Encourages introducing new topics
stop
array—Up to 4 strings that stop generation
seed
integer—Deterministic sampling seed (best-effort)
n
integer
=1>= 1
Number of completions to generate
stream
boolean
=false
Stream tokens via Server-Sent Events
response_format
object—Force JSON object or schema-conforming output
tools
array—Tool / function declarations the model may call
tool_choice
string
autononerequired
Tool-choice policy or specific tool name
logprobs
boolean
=false
Return per-token log probabilities
top_logprobs
integer0 ~ 20Number of top log probabilities returned per token
logit_bias
object—Per-token logit bias map
user
string—End-user identifier for abuse monitoring

Rate limits

SupplierRPMTPMRPD
AlibabaUnlimitedUnlimitedUnlimited
officialUnlimitedUnlimitedUnlimited

No restriction

Frequently asked questions about qwen3.5-plus

What is qwen3.5-plus?

Compare qwen3.5-plus API pricing, supported endpoints, capabilities and access options on voguesly.

How do I call qwen3.5-plus?

Create an API key with access to qwen3.5-plus, then use the exact model ID and a supported endpoint from the API access section. Request fields depend on the selected endpoint.

How is qwen3.5-plus priced?

Pricing depends on the selected provider group and the model billing unit. The current input, output, request, or media prices are shown on this page before sign-up.

What is the context window of qwen3.5-plus?

The model catalog lists a context window of 1000000 tokens. Check the selected endpoint for request limits.

How should I evaluate qwen3.5-plus for my project?

Start with the use cases and prompting guidance on this page, then evaluate the model with representative inputs from your project.