DeepSeek vs Xiaomi MiMo: Token Pricing, Speed & Intelligence
Full comparison of DeepSeek and Xiaomi MiMo — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Rates sourced from public APIs — verify before use.
DeepSeek
DeepSeek V4 Pro and V4.1-Flash — 1M context, open-weight, peak/off-peak pricing
DeepSeek's September 2026 API offers two models: DeepSeek V4 Pro (deepseek-v4-pro-0813, frontier) and DeepSeek V4.1-Flash (deepseek-flash, efficient). Peak hours (01:00–10:00 UTC weekdays) are 2× the off-peak rate. Both models support 1M token context, JSON output, tool calls, and the Anthropic API format.
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
DeepSeek
Xiaomi MiMo
Key differentiators
DeepSeek V4 Pro delivers frontier-class intelligence at competitive off-peak rates — open-weight, 1M context, and compatible with both OpenAI and Anthropic SDKs.
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
DeepSeek FAQs
What are the latest DeepSeek models?
DeepSeek's September 2026 API offers DeepSeek V4 Pro (deepseek-v4-pro-0813) and DeepSeek V4.1-Flash (deepseek-flash). Both support 1M token context windows, JSON output, tool calls, and the Anthropic API format.
What is DeepSeek V4 Pro pricing?
DeepSeek V4 Pro uses peak/off-peak pricing — off-peak rates apply on weekends and Chinese public holidays; peak hours (01:00–10:00 UTC weekdays) are 2× the off-peak rate. Cache hits are billed at a reduced rate. Check api-docs.deepseek.com/quick_start/pricing for current rates.
What is DeepSeek V4.1-Flash pricing?
DeepSeek V4.1-Flash uses the same peak/off-peak structure as V4 Pro, with off-peak rates significantly cheaper than peak. It also supports vision inputs. Check api-docs.deepseek.com/quick_start/pricing for current rates.
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
DeepSeek — DeepSeek V4 Pro and V4.1-Flash — 1M context, open-weight, peak/off-peak pricing
DeepSeek's September 2026 API offers two models: DeepSeek V4 Pro (deepseek-v4-pro-0813, frontier) and DeepSeek V4.1-Flash (deepseek-flash, efficient). Peak hours (01:00–10:00 UTC weekdays) are 2× the off-peak rate. Both models support 1M token context, JSON output, tool calls, and the Anthropic API format.
DeepSeek V4 Pro delivers frontier-class intelligence at competitive off-peak rates — open-weight, 1M context, and compatible with both OpenAI and Anthropic SDKs.
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
DeepSeek
- V4 Pro off-peak: competitive per-1M pricing — frontier intelligence at a fraction of Western provider cost
- V4.1-Flash off-peak: fast and cost-efficient — cheapest DeepSeek model tier
- Both models open-weight — can be self-hosted
- Data residency in China — may not meet GDPR/compliance requirements
- API reliability can lag Western providers during peak demand
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
DeepSeek
- Data residency in China — may not meet GDPR/compliance requirements
- API reliability can lag Western providers during peak demand
- V4.1-Flash does not support vision inputs
Xiaomi MiMo
- Smaller ecosystem than OpenAI or Anthropic
- Limited English-language documentation
- Newer platform — less battle-tested at scale
Provider category context
DeepSeek 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 DeepSeek and Xiaomi MiMo are frontier labs with proprietary models. Choose based on benchmark performance for your specific task: DeepSeek leads on v4 pro off-peak: competitive per-1m pricing — frontier intelligence at a fraction of western provider cost, 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.