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DeepSeek Raised Its API Prices. I Kept V4 Flash and Changed the Infrastructure Instead

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DeepSeek Raised Its API Prices. I Kept V4 Flash and Changed the Infrastructure Instead
S
Sergei Solod

A price increase usually makes you ask one question:

What model should I switch to?

That was my first thought too.

On August 17, I checked my DeepSeek API usage and noticed that the balance was disappearing roughly five times faster than I was used to.

I knew a pricing update was coming, but I had mentally filed it away as a normal increase. Maybe 20%. Maybe 50%. Even twice the old price would have been annoying but manageable.

Once I looked at the actual numbers, I realized this was different.

Instead of immediately benchmarking Qwen, GLM, Kimi, MiniMax, Mistral and every other model I could find, I tried to separate two things that I had previously treated as one:

the model I wanted to use

and

the company selling me the compute to run it

That distinction ended up being much more useful than immediately changing models.

Before the change, DeepSeek V4 Flash was extremely inexpensive.

The old rates I had been using were:

Input, cache miss:  $0.14 / 1M tokens
Cached input:       $0.0028 / 1M tokens
Output:             $0.28 / 1M tokens

The current pricing for deepseek-v4-flash, corresponding to DeepSeek-V4-Flash-0731, is split between off-peak and peak periods.

Off-peak:

Input, cache miss:  $0.22 / 1M tokens
Cached input:       $0.007 / 1M tokens
Output:             $0.66 / 1M tokens

Peak:

Input, cache miss:  $0.44 / 1M tokens
Cached input:       $0.014 / 1M tokens
Output:             $1.32 / 1M tokens

DeepSeek lists the peak windows as 01:00 to 04:00 UTC and 06:00 to 10:00 UTC.

So when I say my spending appeared to increase by about five times, I am not claiming that every type of token received an identical 5x increase.

The exact change depends on the workload.

At peak pricing:

Cache-miss input
$0.14 -> $0.44
≈ 3.14x

Output
$0.28 -> $1.32
≈ 4.71x

Cached input
$0.0028 -> $0.014
= 5x

For an application that produces a significant amount of output, seeing the real bill move toward five times its previous level is not particularly surprising.

The question was what to do about it.

Replacing an LLM is not always the simplest engineering decision

It is easy to say:

DeepSeek became expensive. Just use another model.

Technically, I could.

There are plenty of alternatives now.

Qwen, GLM, Kimi, MiniMax, Mistral and others are all worth looking at.

But an LLM is not always interchangeable infrastructure.

Once you have a model working well inside an application, changing it can create a surprising amount of work.

Prompt behavior can change.

The same instruction may produce longer or shorter answers.

Tone can shift.

Sampling parameters may need adjustment.

Context handling can differ.

Edge cases you already solved can return in different forms.

You may save money on tokens and then spend engineering time trying to make the replacement behave like the model you already had.

I was not against changing models.

I simply did not want to start there.

Instead, I asked a narrower question:

Can I keep the exact same DeepSeek V4 Flash checkpoint and buy the inference somewhere else?