MiMo V2.6 Flash
mimo-v2.6-flashCompare mimo-v2.6-flash 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
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 V2.6 Flash
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 V2.6 Flash
适合频繁使用的多模态助手
MiMo-V2.6-Flash 可以处理文字、图片、视频和音频，输出文本，面向能力与效率兼顾的工作。它适合整理演示内容、分析界面问题、辅助日常编程，以及在智能体流程中持续接收反馈并完成下一步。
不只是把素材变成摘要
可以让模型围绕具体目标分析素材，例如从产品演示中提取操作步骤，结合截图判断异常状态，或从需求讨论中区分已经决定与尚待确认的内容。给出需要观察的对象和判断标准，有助于得到可以继续使用的结果，而不是一段宽泛介绍。
对于代码工作，可以把需求、相关实现和界面表现一起提供，让模型先确认问题，再提出范围清楚的改动。应用接入工具后，还可以把测试输出或新截图作为反馈，形成观察、修改和检查的循环。
让每轮工作有明确产物
Flash适合把任务拆成可检查的小阶段。例如先整理问题，再选择处理顺序，最后生成说明或代码；每阶段都保留来源与完成标志。长对话应保存已完成内容，避免每次反馈都重新设计整个方案。音频与视频是理解材料的输入，新的配音或视频制作仍应安排相应生成流程。
Use cases and prompting
演示资料整理示例
“根据产品演示视频和讲解音频，整理一份试用说明。只保留实际演示过的功能，按用户要完成的事情分组。每项写出操作入口、关键步骤和结果；画面看不清或讲解含糊的地方列为待确认。”
有重要小字时，补充清晰截图，并标明对应位置。
怎样把它接到日常开发中？
每轮给一个明确改动，附相关代码与预期行为；应用执行后把错误、测试结果或截图交回，再继续修正。
多模态材料太多，怎样减少无关输出？
先说明任务目标和关注范围，指定需要比较的片段或信息。要求按固定结构返回，保留能支持结论的证据。
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-v2.6-flash
What is mimo-v2.6-flash?
Compare mimo-v2.6-flash API pricing, supported endpoints, capabilities and access options on Modelsell.
How do I call mimo-v2.6-flash?
Create an API key with access to mimo-v2.6-flash, 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-v2.6-flash 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-v2.6-flash?
The model catalog lists a context window of 1050000 tokens. Check the selected endpoint for request limits.
What is the maximum output of mimo-v2.6-flash?
The model catalog lists a maximum output of 131072 tokens. Your request settings may set a lower limit.
How should I evaluate mimo-v2.6-flash for my project?
Start with the use cases and prompting guidance on this page, then evaluate the model with representative inputs from your project.