mimo-v2.5-asrCompare mimo-v2.5-asr API pricing, supported endpoints, capabilities and access options on voguesly.
| Price/ request |
|---|
$0.01 |
BPE (vendor-specific)Scores on standardized evaluations. Higher percentages are better — and rank percentile shows
Metrics sourced fromArtificial Analysis 2026-09-30·MiMo V2.5 Asr
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MiMo-V2.5-ASR 用于语音识别,接收音频并返回转写文本。适合会议录音、双语访谈、语音备忘、方言内容和歌词整理,也面向噪声、远场与多人场景。转写完成后,可以继续交给文本模型生成纪要、摘要或内容标签。
它支持自动语言识别,也可以选择中文或英文模式。中英术语交替的产品讨论、采访和技术会议,优先使用 auto,让识别过程处理语言切换。若内容明确为单一语言,可选择 zh 或 en。姓名、品牌、数字和专业缩写需要结合原录音复核。
输入应为真实音频内容,当前支持 MP3 与 WAV,编码后数据大小不超过 10 MB。长录音可以按自然停顿切段,保留片段顺序与原始时间位置;不要在词语中间截断,避免拼接时重复或漏字。它也支持流式文本结果,方便应用逐步显示识别内容。
语音识别与会议总结是不同步骤。该模型主要还原说了什么,不负责替你完成业务问答或推断未说出的决定。制作纪要时,先检查转写中的关键事实,再整理行动项;制作字幕时,应另外核对时间对齐与阅读节奏。
嘈杂录音也能用吗? 可以尝试,尽量保留清楚的人声,避免过度降噪损伤语音;重要内容回听确认。
带音乐的歌词如何整理? 提交音频进行识别,再逐句对照原曲检查人声、重复句与听不清的部分。
准备 MP3 或 WAV,确认编码后大小符合限制,再按音频输入格式提交。语言可选 auto、zh 或 en,中英混说使用 auto。
例如,一段包含中文讨论与英文技术名词的采访,先转写为原始文本,再回听检查人名、产品名和数值;确认后交给文本模型整理问答稿。
长录音按自然段切分,保存片段编号与时间范围,合并时检查首尾重叠和缺字。字幕任务额外核对时间轴;会议纪要额外核对负责人、日期与决定,避免将识别文本直接当成最终记录。
Use the linked documentation for model-specific request parameters and examples.
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.
| Supplier | RPM | TPM | RPD |
|---|---|---|---|
| XiaomiMIMO | Unlimited | Unlimited | Unlimited |
No restriction
Compare mimo-v2.5-asr API pricing, supported endpoints, capabilities and access options on voguesly.
Create an API key with access to mimo-v2.5-asr, then use the exact model ID and a supported endpoint from the API access section. Request fields depend on the selected endpoint.
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.
The model catalog lists a context window of 8192 tokens. Check the selected endpoint for request limits.
The model catalog lists a maximum output of 2048 tokens. Your request settings may set a lower limit.
Start with the use cases and prompting guidance on this page, then evaluate the model with representative inputs from your project.
