Xiaomi

What is Xiaomi in AI?

Xiaomi is a Beijing-based consumer-electronics and EV group founded in 2010. It entered open-source AI in 2025 with MiMo-7B, a reasoning-first model; Xiaomi reported that MiMo-7B-RL matched OpenAI o1-mini on maths and code reasoning.

The lineup expanded with MiMo-VL, MiMo-Audio and MiMo-Embodied, followed by the 309B-parameter MiMo-V2-Flash and then the MiMo-V2.5 series in late April 2026. These models form the basis of the Xiaomi MiMo API offering.

MiMo-V2.5 and MiMo-V2.5-Pro weights, tokenizers and model cards are published on Hugging Face under the MIT licence. Xiaomi also provides day-0 support for vLLM and SGLang.

Xiaomi at a glance

Xiaomi MiMo main AI capabilities

• Long-context processing: Analyse inputs up to 1M tokens with either MiMo v2.5 or MiMo v2.5-Pro.
• Multimodal understanding: Process text, images, video and audio with MiMo v2.5; MiMo v2.5-Pro accepts text only.
• Agentic coding: Build coding and agent workflows with native tool calling using either model, with MiMo v2.5-Pro specifically optimised for them.
• Structured output: Return machine-readable structured responses from either MiMo v2.5 or MiMo v2.5-Pro.
• Streaming generation: Stream text responses progressively with both Xiaomi MiMo models.

When should you choose Xiaomi MiMo?

If you need a 1M-token context and token price is a binding constraint, MiMo v2.5 and MiMo v2.5-Pro are candidates to benchmark on your workload. Eden AI lists MiMo v2.5-Pro at $0.43 per 1M input tokens and $0.87 per 1M output tokens.

If you need to interpret audio, images and video through one text-output LLM, MiMo v2.5 can replace a workflow that separately chains speech-to-text and vision models. MiMo v2.5-Pro does not accept image, audio or video input.

If your legal review requires a permissive open-source licence and you may later self-host the same weights, the MIT licence is a material advantage. Xiaomi publishes MiMo-V2.5 and MiMo-V2.5-Pro on Hugging Face and states that commercial use, secondary training and fine-tuning require no additional authorisation.

If you require EU or US data residency, check the available regional hosts before choosing the provider. Xiaomi hosts MiMo v2.5 and MiMo v2.5-Pro in AP on Eden AI, while other providers may offer the same Xiaomi models in other regions. 

Xiaomi MiMo pros and cons

Xiaomi MiMo models, features and capabilities on Eden AI

Eden AI offers two Xiaomi MiMo models: mimo-v2.5 and mimo-v2.5-pro. Both provide a 1M-token context window and are hosted by Xiaomi in the AP region on Eden AI.

Available Xiaomi models

MiMo v2.5 vs MiMo v2.5-Pro: which should you use?

MiMo v2.5-Pro is not simply a bigger version of MiMo v2.5: it increases model scale and agent focus but drops image, video and audio input.

Choose mimo-v2.5-pro when your workload is text-based and prioritises coding or agentic execution; its 1.02T-parameter MoE activates 42B parameters per token.

Choose mimo-v2.5 when you need image, video or audio understanding alongside text; it keeps the same 1M-token context at $0.14 input and $0.28 output per 1M tokens.

Supported Xiaomi MiMo capabilities

Xiaomi MiMo architecture and benchmarks

How the MiMo v2.5 models are built

MiMo v2.5 uses a sparse Mixture-of-Experts architecture with 310B total parameters, 15B activated parameters and 48 layers, while MiMo v2.5-Pro scales to 1.02T total parameters, 42B activated parameters and 70 layers. Sparse activation reduces the compute used per token, while hybrid attention combines sliding-window and global attention to handle long contexts more efficiently.

Both models use Multi-Token Prediction to improve generation efficiency. MiMo v2.5 also includes a 729M-parameter, 28-layer Vision Transformer for image and video understanding, plus a 261M-parameter, 24-layer Audio Transformer initialised from MiMo-Audio for native audio input.

Published MiMo v2.5 benchmark results

Xiaomi also reports that MiMo-V2.5-Pro uses 40–60% fewer tokens than Claude Opus 4.6 or GPT-5.4 to reach comparable results on agentic benchmarks such as ClawEval. For multi-step agent runs, that makes cost per completed task a more useful comparison than cost per million tokens alone.

What to test before you commit

• Retrieval accuracy at your real context length: Test the model at the context sizes your application actually uses, not only near the 1M-token limit.
• Cost per completed task: Measure the full agent loop, including tool calls, retries and generated tokens, rather than comparing token prices alone.
• Multimodal accuracy: Test MiMo v2.5 on your own audio, image and video samples before relying on its multimodal inputs in production.
• Latency from your region: Measure end-to-end response time from your deployment location because Xiaomi-hosted MiMo models run in the AP region on Eden AI.

Xiaomi MiMo API output: what data can be generated?

The Xiaomi MiMo API returns text outputs from text, multimodal or agentic inputs, depending on which MiMo model you use.

Important note on Xiaomi MiMo accuracy and reliability

All published MiMo benchmark figures in this page come from Xiaomi, so you should validate performance on your own tasks before production use. Eden AI lets you compare Xiaomi MiMo against other models on the same prompts and application data through its model comparison features.

A 1M-token context window defines the maximum supported input size, not guaranteed uniform recall across every part of that window. Test retrieval and reasoning at the context lengths and document structures your application will actually use.

Xiaomi is newer to commercial model serving than dedicated AI labs, so you should configure a fallback provider before putting MiMo on a critical production path. Eden AI routing and fallback features let you switch to another provider when Xiaomi is unavailable or unsuitable for a request.

What can you build with Xiaomi MiMo?

Use case 1: Million-token document and repository analysis at low cost

Use mimo-v2.5 when you need to analyse large document collections or code repositories without first building a retrieval layer. Its 1M-token context lets you pass substantial source material directly into the prompt, while structured output can return extracted fields, summaries or comparisons in a predictable format. 

At $0.14 per million input tokens, loading a whole document set into context can cost less than building and maintaining a retrieval pipeline. Retrieval is still the better choice for continuously growing corpora, repeated queries or workloads where only small relevant passages should be processed.

Use case 2: Agentic coding and long-horizon developer workflows

Use mimo-v2.5-pro for coding agents that need to inspect repositories, call tools, modify files and iterate across multiple steps. Its 1M-token context supports large codebases, while native tool calling and structured output let the model interact with external functions and return machine-readable actions. 

Xiaomi reports that MiMo-V2.5-Pro uses 40–60% fewer tokens than Claude Opus 4.6 or GPT-5.4 on comparable agentic benchmarks. In long-running agent loops, that can reduce total execution cost even when the model’s per-token price is not the only variable.

Use case 3: Multimodal and audio understanding in a single call

Use mimo-v2.5 when one workflow needs to understand meeting audio, screenshots and video together and return a text result. Its 261M-parameter Audio Transformer, initialised from MiMo-Audio, handles audio input, while its 729M-parameter Vision Transformer processes images and video. 

You can therefore submit several input types in one request instead of chaining a speech-to-text provider, a separate vision model and an LLM. The output remains text, so this supports transcription-adjacent understanding, summarisation and question answering rather than speech synthesis.

Xiaomi MiMo use cases by industry

Why use Xiaomi MiMo through Eden AI?

Eden AI gives you access to the Xiaomi MiMo API through the same API, billing layer and monitoring stack used for 60+ other AI providers, without requiring a separate Xiaomi integration.

Key benefits of using Xiaomi MiMo on Eden AI

• One integration: Use one Eden AI API key for Xiaomi MiMo and 60+ other AI providers.
• Faster model comparison: Test Xiaomi MiMo against alternative models using the same prompts and application data.
• Provider switching: Change the model string instead of maintaining a separate integration for every provider.
• Fallback and routing: Route requests to another provider when Xiaomi is unavailable or unsuitable for a workload.
• Unified cost control: Track Xiaomi usage, model spend and billing alongside your other providers in one place.

One API for Xiaomi and 60+ AI providers

You can call xiaomi/mimo-v2.5 and xiaomi/mimo-v2.5-pro through Eden AI using the same API and API key as models from 60+ other providers. You do not need a Xiaomi account, Chinese platform onboarding or a separate billing relationship to use the models through Eden AI.

Compare Xiaomi MiMo with other AI models

Eden AI lets you compare Xiaomi MiMo against models from Moonshot AI, DeepSeek, Qwen and other providers using identical prompts and application data. Test context handling, coding performance, multimodal accuracy, latency and cost per completed task before selecting a model for production.

Add fallback and routing for production reliability

Fallback is particularly relevant for Xiaomi MiMo because Xiaomi-hosted models on Eden AI run in the AP region and Xiaomi is newer to commercial model serving than dedicated AI labs. You can configure a non-AP provider for workloads that the AP-only Xiaomi deployment cannot serve, while a second provider also reduces dependence on one serving platform when availability or latency changes.

Monitor usage, billing and costs in one place

Eden AI consolidates Xiaomi MiMo usage, cost tracking and billing with your other AI providers. This makes it easier to compare real workload costs across models, including the total cost of multi-step agent runs rather than relying only on advertised token rates.

Best Xiaomi MiMo alternatives and comparisons on Eden AI 

Xiaomi vs Qwen

Xiaomi MiMo and Qwen both come from large Chinese technology groups, but Qwen offers far more model choice on Eden AI, with 97 Qwen models available across different sizes, reasoning profiles and modalities. Xiaomi keeps the choice narrower with two 1M-context MiMo models, while mimo-v2.5 combines text, image, video and audio input in one model. 

Choose Qwen when catalogue breadth, ecosystem maturity or specialised variants matter; choose Xiaomi when MIT licensing, simpler model selection and low-cost multimodal input are priorities.

Xiaomi vs DeepSeek

Xiaomi MiMo and DeepSeek both offer open-weight models with 1M-token options, but they target different trade-offs. Xiaomi mimo-v2.5 costs $0.14 input and $0.28 output per 1M tokens and adds image, video and audio understanding, while DeepSeek V4 Flash costs $0.44 input and $1.32 output and V4 Pro costs $1.32 input and $3.96 output on Eden AI. 

Choose Xiaomi for lower-cost long-context or multimodal workloads; choose DeepSeek when you prefer its dedicated reasoning models or its behaviour performs better on your coding and reasoning tests.

Xiaomi vs Moonshot AI

Xiaomi MiMo and Moonshot AI both offer 1M-token models on Eden AI, but they differ sharply on licence, price and model scale. Xiaomi publishes MiMo-V2.5 under the MIT licence, while Moonshot uses the bespoke Kimi K3 License, whose revenue-triggered conditions mean it does not meet the Open Source Initiative definition. Xiaomi mimo-v2.5 costs $0.14 input and $0.28 output per 1M tokens, versus $3.00 input and $15.00 output for Kimi K3. 

Choose Xiaomi for permissive licensing, multimodal input and lower cost; choose Moonshot when you want the larger frontier Kimi K3 model.

Xiaomi MiMo API FAQ