Since Xiaomi introduced its MiMo subscription plan for around US$6 per month, many AI users have been impressed by the headline number: 4.1 billion credits every month. At first glance, that sounds enormous and appears far more generous than many competing AI services.
However, there is an important detail that is often overlooked: credits are not tokens.
Many people see the number 4.1 billion and immediately compare it with token quotas from other providers. In reality, credits are simply Xiaomi’s internal billing unit. To understand whether the plan is truly cheap or expensive, those credits must first be converted into actual token costs.
Real Cost Calculation
Using an exchange rate of Rp17,500 per US dollar, the MiMo subscription costs approximately Rp105,000 per month.
For MiMo V2.5, the credit consumption rates are:
- Input (Cache Hit): 2 credits
- Input (Cache Miss): 100 credits
- Output: 200 credits
To keep the comparison simple and conservative, let’s assume all usage is charged as cache misses.
With 4.1 billion credits per month:
4,100,000,000 ÷ 100 = 41,000,000 tokens
This means the Rp105,000 subscription effectively provides about 41 million input tokens.
The cost per million tokens becomes:
Rp105,000 ÷ 41 = Rp2,561 per 1 million tokens
For output tokens:
4,100,000,000 ÷ 200 = 20,500,000 tokens
Rp105,000 ÷ 20.5 = Rp5,122 per 1 million output tokens
Comparison with DeepSeek
Now let’s compare that with DeepSeek.
At approximately US$0.435 per 1 million tokens, and using the same exchange rate:
0.435 × Rp17,500 = Rp7,613 per 1 million tokens
The comparison looks like this:
MiMo V2.5: Rp2,561 per 1 million tokens DeepSeek: Rp7,613 per 1 million tokens
The difference is:
Rp7,613 - Rp2,561 = Rp5,052 cheaper
In percentage terms:
(Rp5,052 ÷ Rp7,613) × 100 = 66.4% cheaper
Or viewed another way:
Rp7,613 ÷ Rp2,561 = 2.97x cheaper
In other words, for the same amount of money, MiMo V2.5 can provide almost three times as many tokens as DeepSeek.
What about MiMo V2.5 Pro?
Its credit pricing is higher:
- Input (Cache Miss): 300 credits
- Output: 600 credits
The calculation becomes:
4,100,000,000 ÷ 300 = 13,666,666 tokens
Cost per million tokens:
Rp105,000 ÷ 13.66 = Rp7,686 per 1 million tokens
The comparison then becomes:
MiMo V2.5: Rp2,561 per 1 million tokens DeepSeek: Rp7,613 per 1 million tokens MiMo V2.5 Pro: Rp7,686 per 1 million tokens
The difference between DeepSeek and MiMo V2.5 Pro is only:
Rp7,686 - Rp7,613 = Rp73
Or:
(Rp73 ÷ Rp7,613) × 100 = 0.96% more expensive
In practice, that difference is almost negligible. MiMo V2.5 Pro and DeepSeek operate at nearly the same cost level.
Conclusion
From a purely economic perspective, the conclusion is straightforward. If your goal is to maximize the number of tokens you receive for the lowest possible cost, MiMo V2.5 is the clear winner. At roughly Rp2,561 per million tokens, it saves about Rp5,052 per million tokens compared to DeepSeek, representing a 66% cost reduction.
MiMo V2.5 Pro, on the other hand, competes in a different category. Its pricing is almost identical to DeepSeek, meaning the decision between the two is more likely to depend on model quality, coding performance, reasoning capabilities, and personal preference rather than cost alone.
It is also worth remembering that all of the calculations above use a conservative assumption where every request is treated as a cache miss. In real-world usage, especially with OpenClaw, OpenCode, KiloCode, and other AI coding assistants, a large portion of context is often served from cache. Since MiMo’s cache-hit pricing is extremely low, actual usage costs can be significantly lower than the estimates shown here.
The bottom line is simple: don’t be fooled by the headline number of billions of credits. Credits are not tokens. Nevertheless, once the numbers are converted properly, MiMo V2.5 still delivers remarkably aggressive pricing. For users focused on coding, agent workflows, document analysis, or heavy daily AI usage, MiMo V2.5 currently provides nearly three times as many tokens as DeepSeek for the same amount of money.