MiMo V2.5
mimo-v2.5Compare mimo-v2.5 API pricing, supported endpoints, capabilities and access options on Modelsell.
- Starting price
- Input / Output · 1M
- Context
- 1.1M
- Maximum input window
- Max output
- 131.1K
- Maximum tokens per response
- Modalities
- →
Pricing by Supplier
| Input/ 1M tokens | Output/ 1M tokens | Cache Read/ 1M tokens |
|---|---|---|
$0.14 | $0.28 | $0.0028 |
- Input/ 1M tokens
- $0.14
- Output/ 1M tokens
- $0.28
- Cache Read/ 1M tokens
- $0.0028
Capabilities / Supported modalities
Provider & data privacy
BPE (vendor-specific)Performance
Benchmarks
Scores on standardized evaluations. Higher percentages are better — and rank percentile shows
Metrics sourced fromArtificial Analysis 2026-09-30·MiMo V2.5
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 MiMo V2.5
理解多种输入，再继续完成工作
MiMo-V2.5 将文字、图片、视频和音频理解放在同一模型中，适合需要综合不同材料的任务。你可以提交产品演示、用户录音、页面截图与项目说明，让它把现象、诉求和规则联系起来，输出分析、代码、摘要或结构化记录。
原生多模态能力适合处理文字说明之外的信息。例如，整理一段教学录像的步骤与注意点，将访谈音频归纳成需求，或对照操作视频与文档检查实际流程。请求中写清关注的时间段、对象与问题，并要求引用可定位的依据，有助于团队重新核查原材料。
它也面向智能体和代码任务。开发环境提供文件读取、检索和执行工具后，可以让它分析项目、计划改动，并根据测试反馈继续推进。结合视觉或音频输入时，可以先把问题整理成可验证的工程要求，再进入实现，避免直接从模糊现象跳到修改代码。
百万 Token 级上下文便于保留较长资料与多步任务状态。长材料应按文件和主题命名，标注当前有效规则，并定期归纳已确认事实与待验证问题。对于需要机器读取的结果，预先定义字段、单位和缺失值，有利于衔接后续业务流程。
常见应用问题
能分析录音并生成配音吗？ 此模型可理解音频并输出文字；要把整理后的正文读出来，需要再使用语音合成服务。
多种材料发生冲突怎么办？ 要求分别保留各材料的说法与位置，指出冲突并列出确认步骤，避免将不同版本的信息直接合并。
Use cases and prompting
围绕同一问题提交多模态材料
为各份材料编号，指定比较维度与输出形式。
材料 A 是用户操作录像，B 是反馈录音，C 是当前功能说明。
分析用户为何无法完成批量上传。
先整理实际操作和用户诉求，再对照功能规则，列出能确认的问题与待验证假设。
每项标注材料和时间位置，最后生成可复现的测试步骤与开发任务。
代码任务提供相关文件和运行检查，媒体分析注明关注片段。长任务保留阶段进度，工具执行后使用真实结果修正判断；不要把未展示的功能视为已验证。
API access
API documentation
Code samples
/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.
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
| 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 |
Rate limits
| Supplier | RPM | TPM | RPD |
|---|---|---|---|
| XiaomiMIMO | Unlimited | Unlimited | Unlimited |
No restriction
Frequently asked questions about mimo-v2.5
What is mimo-v2.5?
Compare mimo-v2.5 API pricing, supported endpoints, capabilities and access options on Modelsell.
How do I call mimo-v2.5?
Create an API key with access to mimo-v2.5, then use the exact model ID and a supported endpoint from the API access section. Request fields depend on the selected endpoint.
How is mimo-v2.5 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 mimo-v2.5?
The model catalog lists a context window of 1050000 tokens. Check the selected endpoint for request limits.
What is the maximum output of mimo-v2.5?
The model catalog lists a maximum output of 131072 tokens. Your request settings may set a lower limit.
How should I evaluate mimo-v2.5 for my project?
Start with the use cases and prompting guidance on this page, then evaluate the model with representative inputs from your project.