Moonshot AI vs Xiaomi MiMo: Token Pricing, Speed & Intelligence
Full comparison of Moonshot AI and Xiaomi MiMo — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Rates sourced from public APIs — verify before use.
Moonshot AI
Kimi K3 — 1T MoE open-weight model, strong agentic and coding benchmark scores
Moonshot AI is a Chinese AI startup behind the Kimi model family. Kimi K3 is their latest 1-trillion-parameter MoE model, succeeding K2 with improved coding and agentic benchmark scores. Kimi K2.6 remains available as a stable production option. The Kimi API offers long-context processing up to 128K tokens with competitive pricing, and models are available via OpenRouter for global access.
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.
Key metric comparison
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Live token pricing
Strengths and weaknesses
Moonshot AI
Xiaomi MiMo
Key differentiators
Kimi K3 is a 1-trillion-parameter open-weight MoE model with strong coding and agentic benchmark scores — available for self-hosting or via the Moonshot API and OpenRouter.
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.
Frequently asked questions
Moonshot AI FAQs
What is Kimi K3?
Kimi K3 is Moonshot AI's latest 1-trillion-parameter mixture-of-experts model, succeeding K2. It activates approximately 32B parameters per token and leads on coding and agentic benchmarks as of September 2026.
What is the difference between Kimi K3 and K2.6?
Kimi K3 is the latest generation with improved benchmark scores. Kimi K2.6 is a stable, battle-tested version suitable for production workloads that prioritise reliability over cutting-edge performance.
Is Kimi K3 open-weight?
Yes. Kimi K3 weights are publicly available on Hugging Face, making it one of the largest open-weight models available. Teams can self-host it on multi-GPU clusters or access it via the Moonshot API.
Xiaomi MiMo FAQs
What is Xiaomi MiMo?
Xiaomi MiMo is Xiaomi's LLM inference platform, offering the MiMo-V2.5 model family. MiMo-V2.5-Pro supports 1M context, MiMo-V2.5 is the efficient tier, and MiMo-V2.5-Pro-UltraSpeed is optimised for high-throughput workloads. See platform.xiaomimimo.com/docs/pricing for current rates.
What context window does MiMo-V2.5-Pro support?
MiMo-V2.5-Pro supports a 1M token context window, enabling full-document and long-context workloads in a single API call.
How much does Xiaomi MiMo cost?
As of October 2026: MiMo-V2.5-Pro $0.435/$0.87 per 1M tokens (input/output), MiMo-V2.5 $0.14/$0.28, MiMo-V2.5-Pro-UltraSpeed $1.305/$2.61. See platform.xiaomimimo.com/docs/pricing for current rates.
Provider resources
Moonshot AI — Kimi K3 — 1T MoE open-weight model, strong agentic and coding benchmark scores
Moonshot AI is a Chinese AI startup behind the Kimi model family. Kimi K3 is their latest 1-trillion-parameter MoE model, succeeding K2 with improved coding and agentic benchmark scores. Kimi K2.6 remains available as a stable production option. The Kimi API offers long-context processing up to 128K tokens with competitive pricing, and models are available via OpenRouter for global access.
Kimi K3 is a 1-trillion-parameter open-weight MoE model with strong coding and agentic benchmark scores — available for self-hosting or via the Moonshot API and OpenRouter.
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.
Key strengths compared
Moonshot AI
- Kimi K3: 1T MoE open-weight model with strong coding and agentic benchmark scores
- Kimi K2.6 available as stable production option
- Competitive on agentic and tool-use benchmarks
- API primarily targets Chinese market — international latency may vary
- Smaller ecosystem than OpenAI or Anthropic
Xiaomi MiMo
- 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
- Smaller ecosystem than OpenAI or Anthropic
- Limited English-language documentation
Key weaknesses compared
Moonshot AI
- API primarily targets Chinese market — international latency may vary
- Smaller ecosystem than OpenAI or Anthropic
- Fewer third-party integrations available
Xiaomi MiMo
- Smaller ecosystem than OpenAI or Anthropic
- Limited English-language documentation
- Newer platform — less battle-tested at scale
Provider category context
Moonshot AI is a frontier lab, founded in 2023. Xiaomi MiMo is a frontier lab, founded in 2024 (as LLM service). Both are frontier lab providers — the comparison is primarily about pricing, model selection, and feature differentiation within the same tier.
How to choose between them
Both Moonshot AI and Xiaomi MiMo are frontier labs with proprietary models. Choose based on benchmark performance for your specific task: Moonshot AI leads on kimi k3: 1t moe open-weight model with strong coding and agentic benchmark scores, while Xiaomi MiMo leads on mimo-v2.5-pro: 1m context window at competitive pricing. For cost-sensitive workloads, compare the cheapest model tier from each provider in the pricing table above — the gap between efficient-tier models is often larger than between flagship models.