qwen3.8-flashCompare qwen3.8-flash API pricing, supported endpoints, capabilities and access options on Modelsell.
Qwen tokenizer (tiktoken-compat)Scores on standardized evaluations. Higher percentages are better — and rank percentile shows
Metrics sourced fromArtificial Analysis 2026-09-30·Qwen3.8 Flash
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Qwen3.8 Flash 是千问系列的多模态推理模型,可以阅读文字、理解图片和视频,再用文本给出分析结果。对于需要反复处理材料的团队,它适合承担文档摘要、图表解释、代码库问答、内容整理和业务助手等工作,让日常任务从收集信息推进到形成结论。
它的视觉能力很适合处理单靠文字难以描述的问题。上传一张报表截图,可以要求它比较不同渠道的走势、找出异常区间并解释图例;提供产品演示视频,可以让它梳理操作流程、总结用户卡点,并按片段列出需要复查的位置。做长视频分析时,先说明关心的事件和输出粒度,比笼统地要求“看一下视频”更容易得到有用结果。
在开发和自动化场景中,它也可以结合文档与代码分析功能依赖、解释错误,或通过函数调用请求外部系统提供信息。思考模式适合需要多步判断的任务;简单改写、分类和字段提取则应把要求写得简洁明确。需要把结果交给程序处理时,可以使用结构化输出,让字段名称和数据类型在请求中保持固定。
模型会输出文字分析,并不会因为看过视频就生成一段新视频。涉及报表数字或画面中的小字,建议提供清晰原图或原始数据;把观察到的内容和推测原因分开输出,也更方便人工复核。
上传清晰的报表截图,说明统计口径、比较对象和需要做的决定。视频任务则说明关注的动作或事件,要求按片段整理发现。
提示词示例: “请分析这张渠道转化报表。先逐项列出能够读清的指标,再比较本周与上周的变化。把结论分成‘图中直接可见’和‘需要额外数据验证’,最后给出三项排查建议。看不清的数值不要补写。”
/v1/chat/completions| Parameter | Type | Default / range | Description |
|---|---|---|---|
temperature | number | = 10 ~ 2 | Sampling temperature; lower is more deterministic |
top_p | number | = 10 ~ 1 | Nucleus sampling probability mass |
max_tokens | integer | >= 1 | Maximum 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 | integer | 0 ~ 20 | Number 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.
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.
| Parameter | Type | Default / range | Description |
|---|---|---|---|
temperature | number | = 10 ~ 2 | Sampling temperature; lower is more deterministic |
top_p | number | = 10 ~ 1 | Nucleus sampling probability mass |
max_tokens | integer | >= 1 | Maximum 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 | integer | 0 ~ 20 | Number of top log probabilities returned per token |
logit_bias | object | — | Per-token logit bias map |
user | string | — | End-user identifier for abuse monitoring |
| Supplier | RPM | TPM | RPD |
|---|---|---|---|
| Alibaba | Unlimited | Unlimited | Unlimited |
| official | Unlimited | Unlimited | Unlimited |
No restriction
Compare qwen3.8-flash API pricing, supported endpoints, capabilities and access options on Modelsell.
Create an API key with access to qwen3.8-flash, then use the exact model ID and a supported endpoint from the API access section. Request fields depend on the selected endpoint.
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Start with the use cases and prompting guidance on this page, then evaluate the model with representative inputs from your project.
