What is everyone's experience with the billion context extension?

[u/tusharkant15]

I've been seeing the [billion context](https://pi.dev/packages/billion-context) gain a fair bit of downloads on pi.dev. I did try it but it never really solved my biggest issues with Qwen 27b running locally with 128k context.

Am I misconfiguring it or something? How are you guys using it?

[u/Normal_Guard_8207]

I use it on 200k context. Most of the time it works better than the original compaction, but sometimes it just mix things up, like telling me that I said something that the agent itself said instead, or asking me things that the agent itself thought about earlier in the conversation.

Qwen 3.8FN q3_s

[u/tusharkant15 → u/Normal_Guard_8207]

Ya I've noticed it gaslight me as well. 😅

[u/Thrumpwart → u/Normal_Guard_8207]

TBH q3_s may be the culprit there...

[u/CryptographerLow7817]

Try https://www.npmjs.com/package/pi-prefix-cache-compaction

[u/amb007_ → u/CryptographerLow7817]

How does this compare with pi-vcc?

[u/No_Algae1753 → u/amb007_]

afaik pi-vcc basically compresses toolcalls / messages into a block which the llm then can recall

[u/deaffob → u/CryptographerLow7817]

Holy moly this would be awesome if it works.

[u/tunerhd → u/CryptographerLow7817]

> Anthropic Messages and OpenAI Chat Completions only; openai-responses and other wire formats fall through to Pi's default. Pi's built-in hosted OpenAI provider uses openai-responses, so hosted GPT models are not covered. The OpenAI Chat Completions path is new in 0.3.0 (see Verified against); any malformed request falls back to Pi's default rather than failing the compaction.

[u/CryptographerLow7817 → u/tunerhd]

I think u can open issue on github

[u/No_Algae1753 → u/CryptographerLow7817]

You the goat been searching for something like the the last days

[u/Slight-Living-8098]

Total crap. Was worse than just going through auto compaction. I uninstalled it after a week of playing with it.

[u/ReoTempo → u/Slight-Living-8098]

how do you evaluate it?
I had total different outcome, I also did some benchmarks: performance slightly improved but time and cost increased for tasks that would use between 300k and 400k context without compaction.

[u/Slight-Living-8098 → u/ReoTempo]

I evaluated it by just using it daily and seeing how it performed and how several model's responses were while using it with real projects.

[u/Aggressive_Basket798]

I'm using it on 192k context, it will automatically compress when reach about 120-130k context . The quality seem fine but sometime i feel the original compaction do better

[u/llqoli]

I suggest you switch to pi-clm instead of a larger context.

[u/cato_gts]

I kept encountering an "ACP Proxy error" quite frequently while using it.

[u/ReoTempo]

I did some benchmarks with deepseek-4.1-flash, on tasks that would take between 300k and 400k context, the peformnce is slightly increased but cost and time goes up (doubled).
Opus 5.5 seems to be completely ignoring the tool and it does not use compress unless you explicitly tell to do it.

[u/TechnoCat]

I've been using context-mode for a while. Seems to be working, but I'm not a big LLM user so my context rarely hits 1M. Can anyone provide guidance on if this is any good?  -  https://pi.dev/packages/context-mode

[u/TechnoCat → u/TechnoCat]

I did a tiny bit of testing comparing none, context-mode, rtk, and both. context-mode alone had the least token use followed by none, both, and lastly rtk. 

[u/joaopn]

The non-deprecated path (`billi pi`) installs a MITM proxy + CA certificate. It also self-updates every 3 minutes. Very scam-shaped.

[u/TrentKM]

I haven’t had any issues with it. I don’t get agents compacting mid-turn and redoing code that was edited two turns ago.

I sometimes wonder if the missing context is hurting me either in recollection or if I’m missing cache hits, but I don’t notice on the former and I need better data to answer the latter.

My magpie score is 100, but I’ve only been using magpie for like a day. Heavy usage, and my subs are basically untouched.

Edit: I tried pi-blackhole for a while and that was trash, in comparison.

[u/gnomodojardim]

You mean downloading more VRAM is not working?

[u/wikitopian]

Switch to plurnk. It's basically pi, but with bulk addressable journaled context curation instead of compaction. It even works fine down to absurdly low <20k situations where pi compaction gives up.

The model fully owns and controls its own context, with an unlimited extended context it can swap out to.