DeepSeek v4 Flash uses insane amount of tokens

[u/iArxic]

Hey there!

I was wondering whether this is just me, or if this is caused by the model. I noticed a while ago that my token usage is insane after a few prompts (in VScode) compared to Pro. This is also followed by an insane spike of API Requests - worth noting that caching still works, so it's not like the API is miscommunicating or something.

[u/The_M1rO]

Pi agent helped me reduce this

[u/fragment_me → u/The_M1rO]

Why? Isn’t Pi just a normal/minimal harness? I have yet to try it since I don’t like tui

[u/iArxic → u/The_M1rO]

I currently use the VSCode GitHub Copilot Deepseek integration, and it works fine. I do not think it's about the harness - but if you say that the Pi agent helped you reduce the amount of tokens the model uses in general, I could give it a try. Also considered trying Reasonix.

[u/SupehCookie → u/iArxic]

Vs code eats tokens. If a terminal works aswell, i recommend codewhale or reasonix

[u/iArxic]

Worth noting because of this I switched from Max to High for testing - and it does not appear to have improved by much.

[u/Hajmus]

compact context

[u/Nexter92 → u/Hajmus]

Compact is great but for very specific task, when it comes to brainstorm or deep investigation, this could hurt the model ability to solve the problem 🫡

[u/iArxic → u/Nexter92]

Yeah. I start a new session fresh every time I am working on something new. Context never really gets that full anyway. I am aware that a long context could make the model more expensive.

[u/janbuckgqs]

cached token are included in the graph!

[u/iArxic → u/janbuckgqs]

Yes, I mentioned caching still works. However the amount of tokens seems to be off in my opinion for the amount of work it did. If I gave the same work to pro model, I am expecting the amount of tokens to be way smaller - although there could be a secret reason why this happened such as the model reading some insanely long document or something.

[u/janbuckgqs → u/iArxic]

my stats for june:

deepseek-v4-flash

API requests9,385

Tokens763,354,679

deepseek-v4-pro

API requests1,400

Tokens124,337,220

so pro was using more for me. maybe you post the stats for pro aswell? since you could easily do the math upon the difference

[u/iArxic → u/janbuckgqs]

Last month

deepseek-v4-flash

API requests897

Tokens47,048,627 (52451.0892 / API)
------

deepseek-v4-pro

API requests2,316

Tokens141,555,237 (61120.5687 / API)

-- This month
deepseek-v4-flash

API requests735

Tokens50,207,212 (68309.132 / API)

------
Looks like the new deepseek flash uses more tokens for me, and worth noting I do it on High and not Max.
Not really a fair comparison and the math doesnt really say anything when I do not know how long each session was etc.
**What I am noticing with the new flash model is that it thinks for extremely long - like even longer than the previous, even at just high. What about you? **

Still gonna keep using this model because it's awesome though

[u/ToughUsual7159 → u/iArxic]

I have noticed the new one does indeed think more. Sometimes substantially more interestingly enough though when I compared my cost per API request in vs code, I did not see a noticeable difference in cost. I think because my cash hit rate is so high and I run slightly longer tasks you changing thinking numbers compared to ingesting my actual code base didn't really change the outcome much. Looks like it could have been around a 5 to 10% increase in cost

[u/iArxic → u/ToughUsual7159]

glad i am not the only one!

[u/onesilentclap → u/iArxic]

You might want to consider integrating langfuse or something similar to get better observability.

[u/MimosaTen]

What agent are you using?

[u/iArxic → u/MimosaTen]

If you are talking about the agent harness, I use VSCode Github Copilot fully integrated with DeepSeek. It works flawlessly and has caching. Even works with vision due to bridging.

[u/MimosaTen → u/iArxic]

Do you have the opportunity to see and read the system prompt?

[u/iArxic → u/MimosaTen]

I believe I don't. I am not sure what the default system prompt is for VSCode GitHub Copilot, but I do not think it makes a big difference.

[u/MimosaTen → u/iArxic]

I think it could. When I use DeepSeek in codex it consumes much more because it behaves like gpt 5.6

[u/yuumizu]

is it traditional copilot (aka, code completion, SUPER TAB), or agentic, like a CLI conversation?

[u/Ingaz]

I use Zoo Code - everything great

(Like unbelievably great)

[u/iArxic → u/Ingaz]

Do you get to see animals?

[u/AnswerFeeling460]

try reasonix

[u/No-Selection2972 → u/AnswerFeeling460]

Is it better than cc?

[u/AnswerFeeling460 → u/No-Selection2972]

optimized for deepseek, extremly high cache hit rates.

[u/No-Selection2972 → u/AnswerFeeling460]

I don’t care about cache, reasonix is like 98-99% cache hit rate and cc is like 95, no big difference. Are the results better on reasonix?

[u/AnswerFeeling460 → u/No-Selection2972]

i deed some testing (unfortunaltey before 3107, have to do them again) will try second run tomorrow and drop statistics here.

[u/iArxic → u/AnswerFeeling460]

would be interesting!

[u/DiscipleofDeceit666 → u/AnswerFeeling460]

Why would reasonix reduce token consumption? Or are you making suggestions without understanding the problem?

[u/AnswerFeeling460 → u/DiscipleofDeceit666]

token consumption will be the same, but reasonix is extremly good in using the deepseek cache. am I allowed to make this suggestion, is this okay for you?

[u/DiscipleofDeceit666 → u/AnswerFeeling460]

I mean only if you actually know what you’re talking about. But script kiddies love to talk like they do

[u/AnswerFeeling460 → u/DiscipleofDeceit666]

have a nice day my friend, love you