Aion-1.0 is a multi-model system designed for high performance across various tasks, including reasoning and coding. It is built on DeepSeek-R1, augmented with additional models and techniques such as Tree of Thoughts (ToT) and Mixture of E…
Model details →AionLabs: Aion-1.0 vs Xiaomi: MiMo-V2.6-Pro
Xiaomi: MiMo-V2.6-Pro wins on 5 of 6 axes — pricing and capability skew in its favour for most workloads.
MiMo-V2.6-Pro is the flagship foundation model developed by Xiaomi. Built at a scale of over 1T parameters, it is designed to push the ceiling of capability for the most demanding...
Model details →Side-by-side comparison
| Capability | AionLabs: Aion-1.0 | Xiaomi: MiMo-V2.6-Pro | Winner |
|---|---|---|---|
| Context window Maximum number of input tokens the model can attend to in a single request. | 131K | 1.0M | 🏆 Xiaomi: MiMo-V2.6-Pro |
| Input price (per 1M) Cost per million input tokens billed by the provider. | $4.00 | $0.43 | 🏆 Xiaomi: MiMo-V2.6-Pro |
| Output price (per 1M) Cost per million output tokens billed by the provider. | $8.00 | $0.87 | 🏆 Xiaomi: MiMo-V2.6-Pro |
| Tool / function calling First-class support for emitting structured tool calls. | — | Yes | 🏆 Xiaomi: MiMo-V2.6-Pro |
| Vision input Accepts image inputs alongside text. | — | Yes | 🏆 Xiaomi: MiMo-V2.6-Pro |
| Reasoning mode Internal chain-of-thought / extended-thinking support. | Yes | Yes | Tie |
Frequently asked questions
Is AionLabs: Aion-1.0 better than Xiaomi: MiMo-V2.6-Pro?
Xiaomi: MiMo-V2.6-Pro wins on 5 of 6 axes — pricing and capability skew in its favour for most workloads.
What's the price difference between AionLabs: Aion-1.0 and Xiaomi: MiMo-V2.6-Pro?
Input: $4.00 vs $0.43 per 1M tokens. Output: $8.00 vs $0.87 per 1M tokens.
What context windows do AionLabs: Aion-1.0 and Xiaomi: MiMo-V2.6-Pro support?
AionLabs: Aion-1.0 supports up to 131K tokens. Xiaomi: MiMo-V2.6-Pro supports up to 1.0M tokens.
Do both AionLabs: Aion-1.0 and Xiaomi: MiMo-V2.6-Pro support tool calling?
AionLabs: Aion-1.0: not advertised. Xiaomi: MiMo-V2.6-Pro: yes.