Voyage AI vs Xiaomi MiMo: Token Pricing, Speed & Intelligence
Full comparison of Voyage 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.
Voyage AI
Voyage 3.5 — strong MTEB retrieval quality for RAG and semantic search
Voyage AI specialises in embedding and reranking models for retrieval-augmented generation (RAG) and semantic search. Voyage 3.5 and its variants rank highly on the MTEB leaderboard for retrieval quality. The 2026 lineup adds Voyage 3.5 Lite (cost-efficient), Voyage Code 3 (code-optimised embeddings), and Voyage Multimodal 3 (image + text embeddings). Voyage AI is the default embedding provider for Anthropic's Claude documentation and many production RAG systems. Check voyageai.com for current rates.
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
Voyage AI
Xiaomi MiMo
Key differentiators
Voyage 3.5 Lite delivers strong MTEB retrieval quality at cost-efficient pricing — with specialised variants for code and multimodal content.
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
Voyage AI FAQs
What is Voyage AI used for?
Voyage AI provides embedding and reranking models for RAG pipelines, semantic search, and document retrieval. It does not offer chat or text generation models — it is purpose-built for the retrieval layer of AI applications.
How does Voyage AI compare to OpenAI embeddings?
Voyage 3.5 consistently scores well on MTEB benchmarks relative to OpenAI text-embedding-3-large, often at lower per-token cost. Check voyageai.com and openai.com/api/pricing for current rates to compare.
What is Voyage Code 3?
Voyage Code 3 is a code-optimised embedding model designed for code search and retrieval. It outperforms general-purpose embeddings on code-related retrieval tasks, making it the preferred choice for code search applications.
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
Voyage AI — Voyage 3.5 — strong MTEB retrieval quality for RAG and semantic search
Voyage AI specialises in embedding and reranking models for retrieval-augmented generation (RAG) and semantic search. Voyage 3.5 and its variants rank highly on the MTEB leaderboard for retrieval quality. The 2026 lineup adds Voyage 3.5 Lite (cost-efficient), Voyage Code 3 (code-optimised embeddings), and Voyage Multimodal 3 (image + text embeddings). Voyage AI is the default embedding provider for Anthropic's Claude documentation and many production RAG systems. Check voyageai.com for current rates.
Voyage 3.5 Lite delivers strong MTEB retrieval quality at cost-efficient pricing — with specialised variants for code and multimodal content.
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
Voyage AI
- Voyage 3.5 ranks highly on MTEB retrieval benchmarks
- Voyage 3.5 Lite: cost-efficient embedding option
- Voyage Code 3 for code-optimised embeddings
- Embeddings and reranking only — no chat or generation models
- Smaller ecosystem than OpenAI
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
Voyage AI
- Embeddings and reranking only — no chat or generation models
- Smaller ecosystem than OpenAI
- No open-weight models
Xiaomi MiMo
- Smaller ecosystem than OpenAI or Anthropic
- Limited English-language documentation
- Newer platform — less battle-tested at scale
Provider category context
Voyage AI is a inference api, founded in 2023. Xiaomi MiMo is a frontier lab, founded in 2024 (as LLM service). Voyage AI as an inference API provider hosts open-weight models — typically offering lower prices for equivalent capability tiers. Xiaomi MiMo as a frontier lab trains and serves proprietary models with capabilities not available elsewhere.
How to choose between them
Choose Voyage AI if you need voyage 3.5 ranks highly on mteb retrieval benchmarks. Choose Xiaomi MiMo if you need mimo-v2.5-pro: 1m context window at competitive pricing. For high-volume production workloads, run a cost comparison using the token pricing table above with your actual prompt/completion token ratio — the cheapest provider depends heavily on your input-to-output token ratio.