Fireworks AI vs Xiaomi MiMo: Token Pricing, Speed & Intelligence
Full comparison of Fireworks 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.
Fireworks AI
H100/H200/B200/B300/GB300 on-demand — Serverless Training API, Forge 2026 conference
Fireworks AI provides optimised inference for open-weight models with a focus on production reliability and low latency. On-demand GPU compute (H100, H200, B200, B300, GB300) is available alongside serverless inference. The Serverless Training API enables fine-tuning for GLM 5.3, Qwen 3.8 27B, Kimi K3, DeepSeek V4 Flash, and Muse Glimmer 30B without managing GPU infrastructure. Fireworks hosted its inaugural Forge 2026 conference.
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
Fireworks AI
Xiaomi MiMo
Key differentiators
Production-grade reliability with the latest GPU hardware (B300/GB300) and a Serverless Training API — the most complete platform for open-weight model inference and fine-tuning.
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
Fireworks AI FAQs
What GPU hardware does Fireworks AI offer?
Fireworks AI offers on-demand H100, H200, B200, B300, and GB300 GPU instances for dedicated deployments, alongside their serverless inference API for pay-per-token access.
What is the Fireworks Serverless Training API?
Fireworks AI's Serverless Training API lets you fine-tune open-weight models without managing GPU infrastructure. Upload your training data, run fine-tuning jobs, and deploy the resulting model via their inference API.
What models does Fireworks AI offer?
Fireworks AI hosts Llama 3.3 70B, DeepSeek R1, Mixtral, and other popular open-weight models. They focus on production-ready models with optimised inference rather than the broadest possible catalog.
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
Fireworks AI — H100/H200/B200/B300/GB300 on-demand — Serverless Training API, Forge 2026 conference
Fireworks AI provides optimised inference for open-weight models with a focus on production reliability and low latency. On-demand GPU compute (H100, H200, B200, B300, GB300) is available alongside serverless inference. The Serverless Training API enables fine-tuning for GLM 5.3, Qwen 3.8 27B, Kimi K3, DeepSeek V4 Flash, and Muse Glimmer 30B without managing GPU infrastructure. Fireworks hosted its inaugural Forge 2026 conference.
Production-grade reliability with the latest GPU hardware (B300/GB300) and a Serverless Training API — the most complete platform for open-weight model inference and fine-tuning.
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
Fireworks AI
- 320+ tokens/sec on Llama 3.3 70B — fast GPU inference
- H100/H200/B200/B300/GB300 on-demand GPU access
- Serverless Training API for fine-tuning without infrastructure
- Slightly higher pricing than budget alternatives
- Smaller model catalog than Together AI
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
Fireworks AI
- Slightly higher pricing than budget alternatives
- Smaller model catalog than Together AI
- Serverless Training is newer and less battle-tested
Xiaomi MiMo
- Smaller ecosystem than OpenAI or Anthropic
- Limited English-language documentation
- Newer platform — less battle-tested at scale
Provider category context
Fireworks AI is a inference api, founded in 2022. Xiaomi MiMo is a frontier lab, founded in 2024 (as LLM service). Fireworks 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 Fireworks AI if you need 320+ tokens/sec on llama 3.3 70b — fast gpu inference. 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.