by Xiaomi
Xiaomi: MiMo-V2.5.
multimodal
open weights
1M ctx
Cheapest input
$0.4/M
on DeepInfra
Cheapest output
$2.0/M
on DeepInfra
Fastest
65 tok/s
on OpenRouter
Hosted equiv.
~$0.72/hr
@ 100 tok/s on DeepInfra
MiMo-V2.5 is a native omnimodal model by Xiaomi. It delivers Pro-level agentic performance at roughly half the inference cost, while surpassing MiMo-V2-Omni in multimodal perception across image and video understanding..
Where to use it
Cheapest hosted endpoints.
Performance
Speed across providers.
Tokens/sec and time-to-first-token measured against the same prompt template on each provider's API.
| Provider | Tokens/sec | TTFT | Total |
|---|---|---|---|
| OpenRouter | 65.5 | 3998 ms | 5103 ms |
FAQ
Frequently asked.
How do I run Xiaomi: MiMo-V2.5?
Xiaomi: MiMo-V2.5 is open-weight, so you can self-host on rented GPUs. See the Run It Yourself tab for GPU configurations + cost estimates, or use one of the hosted inference providers listed on this page.
Where can I access Xiaomi: MiMo-V2.5?
Xiaomi: MiMo-V2.5 is available via DeepInfra, OpenRouter. Each access option lists its own pricing (per million tokens or hourly hosting).
How much does it cost to run Xiaomi: MiMo-V2.5?
API pricing starts at $0.4/M input tokens and $2.0/M output tokens. Self-hosting cost depends on the GPU you rent — see the Run It Yourself tab.
Is Xiaomi: MiMo-V2.5 open-source or proprietary?
Xiaomi: MiMo-V2.5 is open-weight under the license. You can download and self-host it.
API pricing
Per provider
What it costs per month across providers.
Estimate your monthly bill for Xiaomi: MiMo-V2.5 across every host that publishes per-token pricing. Slide your token volumes; the chart + table re-rank cheapest-first.
Cheapest
$8.0
OpenRouter
$/M input
$0.4
per million tokens
$/M output
$2.0
per million tokens
Providers
2
with priced rows
Monthly bill
Cheapest provider on the left.
Total monthly cost — input + output tokens combined.
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Bill breakdown.
Full calculator
Want to compare token volumes across other models too?
Open the standalone API pricing tool →
Context window
How much it can remember.
1M tokens
≈ 786,432 English words
4K
32K
128K
1M
Capabilities
What it can do.
·
Vision input
·
Audio input
·
Video input
·
Function calling
·
Tool use
·
JSON mode
✓
Streaming
·
Fine-tuning