MiMo-V2-Flash API Pricing

Xiaomi has deprecated this model. The price is kept here for reference; check the provider's documentation before building on it.

Xiaomi · released Dec 16, 2025 · 262K context · API model ID mimo-v2-flash

MiMo-V2-Flash by Xiaomi API pricing: $0.14 per 1M input tokens and $0.28 per 1M output tokens (provider list price via models.dev, Oct 10, 2026).

Prices

MiMo-V2-Flash prices, USD per 1M tokens
PriceUSD per 1M tokens
Input$0.14
Output$0.28
Cache read$0.0028
Cache writeNot published

Xiaomi's list price for its own API, as compiled by models.dev and checked Oct 10, 2026, 09:00 UTC. Prices on cloud platforms and resellers can differ. Provider documentation

Estimated monthly cost

$84.00

per month

per day
$2.80
per request
$0.00028

Cost = input tokens × input price + output tokens × output price, 30 days a month. Cached input uses the cache read price. Cache write fees are not included.

Compare this workload across models

Monthly cost by workload

WorkloadPer month
Chatbot1,000 in / 500 out tokens, 10,000 requests a day$84.00/mo
RAG8,000 in / 500 out tokens, 5,000 requests a day$189.00/mo
Coding agent50,000 in / 2,000 out tokens, 1,000 requests a day, 80% cached input$62.16/mo

Context and capabilities

Context
262,144 tokens
Max output
65,536 tokens
Input
text
Output
text
Knowledge cutoff
Dec 1, 2024
Open weights
Yes
Tool calling
Yes
Reasoning
Yes
Structured outputs
No

Capabilities and limits as listed by models.dev.

Similarly priced models

Price history

No price changes since we started tracking on Oct 8, 2026.

Questions

How much does MiMo-V2-Flash cost per month?
For a chatbot handling 10,000 requests a day with 1,000 input and 500 output tokens each, MiMo-V2-Flash costs about $84.00 per month at Xiaomi's list prices ($0.14 per 1M input tokens, $0.28 per 1M output tokens).
Does MiMo-V2-Flash support prompt caching?
Yes. Cached input costs $0.0028 per 1M tokens, compared with $0.14 for regular input.
How long is MiMo-V2-Flash's context window?
MiMo-V2-Flash accepts up to 262,144 tokens (262K) of context and returns up to 65,536 tokens per response.