Xiaomi MiMo
MiMo-V2.5-Pro, MiMo-V2.5, MiMo-V2.5-Pro-UltraSpeed — reasoning-focused models from Xiaomi
Xiaomi MiMo is the LLM inference platform from Xiaomi, offering the MiMo model family: MiMo-V2.5-Pro (flagship, 1M context), MiMo-V2.5 (efficient), and MiMo-V2.5-Pro-UltraSpeed (high-throughput). The MiMo models are reasoning-focused, with strong performance on math and coding benchmarks. Pricing is published at platform.xiaomimimo.com/docs/pricing.
Xiaomi's reasoning-focused MiMo model family with 1M context on the Pro tier and a UltraSpeed variant for high-throughput inference at competitive per-token rates.
- MiMo-V2.5-Pro: 1M context window at competitive pricing
- MiMo-V2.5-Pro-UltraSpeed: high-throughput variant for latency-sensitive workloads
- Reasoning-focused architecture — strong on math and coding
- Competitive input/output pricing across the model family
- Smaller ecosystem than OpenAI or Anthropic
- Limited English-language documentation
- Newer platform — less battle-tested at scale
Xiaomi MiMo Models
| Model | Modality | Tier | Price | Cached /1M | TTFT | Throughput | Context | Intel. | Caps | |
|---|---|---|---|---|---|---|---|---|---|---|
MiMo-V2.5 MiMo-V2.5 | Chat | efficient | $0.14/ $0.28 in / out per 1M EST Oct 2026 | — | — | — | 33K | — | ||
MiMo-V2.5-Pro MiMo-V2.5 | Chat | advanced | $0.43/ $0.87 in / out per 1M EST Oct 2026 | — | — | — | 1.0M | — | ||
MiMo-V2.5-Pro-UltraSpeed MiMo-V2.5 | Chat | advanced | $1.30/ $2.61 in / out per 1M EST Oct 2026 | — | — | — | 1.0M | — |
Xiaomi MiMo — Frequently Asked Questions (FAQs)
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Full comparison tableXiaomi MiMo LLM pricing overview
Xiaomi MiMo is a proprietary AI lab that develops and hosts its own large language models. Xiaomi MiMo is the LLM inference platform from Xiaomi, offering the MiMo model family: MiMo-V2.5-Pro (flagship, 1M context), MiMo-V2.5 (efficient), and MiMo-V2.5-Pro-UltraSpeed (high-throughput). The MiMo models are reasoning-focused, with strong performance on math and coding benchmarks. Pricing is published at platform.xiaomimimo.com/docs/pricing. Common use cases include Reasoning, Math, Coding, Long-context. Headquartered in Beijing, China, founded 2024 (as LLM service). All models are accessible via a REST API compatible with standard OpenAI-style request formats, enabling drop-in integration with most LLM frameworks and orchestration tools.
Xiaomi MiMo vs other LLM providers
Xiaomi MiMo competes with OpenAI, Anthropic, Google, Groq, Together AI, Mistral, Cohere, and other inference API providers across dimensions of price, throughput, latency, context window, and model intelligence. Xiaomi's reasoning-focused MiMo model family with 1M context on the Pro tier and a UltraSpeed variant for high-throughput inference at competitive per-token rates. Use the LLM provider comparison tool to see Xiaomi MiMo token pricing, latency, and throughput side-by-side with any other provider. The full LLM pricing table shows all providers ranked by input token cost, output token cost, and throughput in a single sortable view.
Why choose Xiaomi MiMo?
Xiaomi MiMo stands out because: Xiaomi's reasoning-focused MiMo model family with 1M context on the Pro tier and a UltraSpeed variant for high-throughput inference at competitive per-token rates.. The API currently offers 3 models, spanning 2 advanced, 1 efficient tiers. For teams running high-volume inference workloads, prompt caching and batch API endpoints can reduce effective input token costs by 50–90% — check the context window cost guide for a full breakdown of caching economics.
Understanding Xiaomi MiMo token pricing
Xiaomi MiMo charges separately for input (prompt) and output (completion) tokens, priced per 1M tokens in USD. Output tokens are typically 3–5× more expensive than input tokens due to the compute cost of autoregressive generation. The cheapest {meta.name} input rate is currently $0.14/1M tokens. Prices shown are sourced from Xiaomi MiMo's public pricing page and updated every 15 minutes. Need help estimating your spend? Read the LLM API cost calculator guide — it covers tokens, context windows, prompt caching, and batch discounts with worked examples for RAG, chat history, and document processing workloads.
Xiaomi MiMo context window and model capabilities
Context window size directly affects both capability and cost — every token in the context window is charged as an input token. Xiaomi MiMo's largest context window is 1.0M tokens. For RAG and document processing workloads, longer context windows enable richer retrieval but increase per-call costs proportionally. Prompt caching — where supported — stores the KV state of repeated prefixes and charges 75–90% less for cache hits, making it the most impactful cost optimization for applications with consistent system prompts or retrieved documents. See the LLM context window cost guide for a full analysis of how context length affects your API bill.
Self-hosted vs managed inference: when Xiaomi MiMo makes sense
Managed inference APIs like Xiaomi MiMo eliminate infrastructure overhead — no GPU provisioning, driver management, or model serving stack to maintain. The trade-off is cost at scale: a single H100 at ~$2.50/hr can serve ~500K tokens/min of Llama 3.3 70B, which at Xiaomi MiMo API rates would cost significantly more per token. The break-even point depends on your request volume, latency requirements, and engineering capacity. For teams processing fewer than ~10M tokens/day, managed APIs are almost always cheaper when total cost of ownership is considered. Above that threshold, self-hosted inference on rented GPU compute typically wins on unit economics. Read the cheapest GPU cloud guide for a full break-even analysis. Historical Xiaomi MiMo token price data is available in the LLM price history charts.
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