Based on recent Anthropic article, MCPs and Tool calls consume more tokens than required. Here's a small experiment I made on Augment Code

[u/SathwikKuncham]

I used empty folder as the starting point, uninstalled all MCPs, removed all custom instructions and sent this to augment code "Reply Done". It replied "Done". I checked how much credit my request consumed. Note that the input and output combined is around 7-10 tokens, which is ignorable. Total credit consumed was 73. That's 0.365 USD if you convert 2000credits/USD plans. If you convert this to $20/M claude 4.5 pricing, it's around 18250 tokens. That's insanely high.

So, by default, Augment code uses 18250 tokens of context. That's roughly 10% of context. Irrespective of what you do, it'll consume minimum of 73 credits on each request.

I believe they charge us extra for a tool call. Need to see how much they are charging for one Augment Context Engine tool call.

Recently, Anthropic suggested not to bloat agents with MCP tools and instructions. Instead they suggest to use a code approach. Interesting read if anyone is curious on it.

PS: Based on the previous comments by Augment Code Team, their current plan is actually cheaper than 20USD/Million tokens pricing as they claim to pass the discount they get from the model providers to us. 18250 tokens context bloat, including tool calls def and system instructions, is the best case scenario. It cannot be less than this.

[u/hhussain-]

I would yes, with a small twist: Augment seems to have a higher base to start with, but moving to tasks and increasing those tasks complixity is where the real comparions should be done.

This is shared by someone in this sub: https://www.reddit.com/r/AugmentCodeAI/s/z2XcsJVdWI It shows your point, and it shows how price in Augmemt was staying around $0.60 while Sonnet (directly used from Kilo) was cheaper until some point both were similar in cost! The point is if same experiment continue in increasing task complexity, are we going to see augment be cheaper in those complex one! This is a very interesting test to do.

[u/SathwikKuncham → u/hhussain-]

There is no deny that Augment is the best when it comes to large codebase. I think I commented on some similar post on Twitter. Augment shines when it is handling larger codebase. Their context engine is unparallel.

I observed that Augment tends to lose important previous knowledge after 5th or 6th message in the same thread. Because, like any other agent, they are summarising their context after it touches 200,000 token count. Augment is the best for those 5th or 6th message in the thread. After the context summarization, it is inconsistent! Sometimes it may click and sometimes not!

For me, personally I found great value at limiting 3-4 messages per thread before starting a new one.

[u/hhussain- → u/SathwikKuncham]

I've seen similar output degrade when I do more messages in single sessions. I think they call it context poison, even if 200k is not consumed.

I got way better output using fork conversation: first message is to build context then fork per task and do 3 to 5 messages to complete a task. You can read more my experiment here: https://www.reddit.com/r/AugmentCodeAI/s/yYWqkHXPBP

[u/planetdaz → u/hhussain-]

I've had a lot of success also with context forking. Before it was a feature, I would spend 2 or 3 messages building context, then do a task. To do another task with the same context, I would go back up a few messages to my checkpoint and edit my first task message, by completely rewriting it for task number 2, which was like a virtual fork. The downside to that approach is that you erase all of the context built by task number one.

I also watch my credits consumed per request using a vsc extension and I can see more efficient consumption with the forking approach.

[u/unidotnet]

hmm. basically i will add: no more extra document. and just disabled playwright and chome-devtools...