Thats why run your own mcp, connect to chatgpt chat and do free coding. https://lemoncrow.com
8 karma · joined November 18, 2012
Avg turns/trial: 13.74 vs 16.82 (-18.3%)
Avg duration/trial: 448.8s vs 550.0s (-18.4%)
Total wall time: 55.5h vs 68.0h (-18.5%)
Cost(both @ 1hr tier): $279.40 vs $411.58 (-16%)So here it is. In the latest release, I added:
lc chatgpt serve
It directly connects to your local project directory, allow access through secure tunnel and connect using OAuth.
Follow the instructions to add your own MCP connector, connect it directly to your project, and start coding from ChatGPT for free.
You HAVE TO attach the tool on every message you send or Just create a new branch and reattach the tool. It works beautifully .
*until open ai decides to ban it
While I was exhausting my token limits, I searched for tools that claimed to be saving tokens. but what they don't say is the full agent $ cost saving.
That is why I created LemonCrow: to optimize agent context end to end rather than focusing on a single token category. Result: on average 30% cost saving and 25% faster. No model routing trick. Same tasks, Same quality, Same model, just cheaper and faster.
Agent cost includes more than output tokens: fresh input, cache writes, cache reads, and repeated context across turns all contribute to the bill. Every input/output/tools/context reread on every turn making cost O(n^2). Optimizing one category may not reduce total cost by the same percentage.
I ran matched benchmarks against a clean Claude Code baseline and few other tools.
Caveman — 300 runs per arm
Output tokens: −44.1% Total cost: $26.0058 → $26.0204 Cost difference: +0.06%
The answers were shorter, but the final bill was essentially unchanged.
CodeGraph — 35 runs per arm
Total token volume: −86.5% Total cost: $19.1064 → $15.9871 Cost difference: −16.3%
This was a real saving, but much smaller than the token reduction claims.
LemonCrow on the same tasks
Total cost: $19.1064 → $6.2869 Cost difference: −67.1% Total token volume: approximately −91.7%
Indexing quality is the main driver for cost. if Index can't give LLM in one turn what it is looking for it will have to rephrase and ask again or start using grep instead. I also measured retrieval quality across 7,213 examples from 14 repositories:
LemonCrow: 0.676 cocoindex-code: 0.557 codebase-memory-mcp: 0.502 ripgrep: 0.376 CodeGraph: 0.296
MRR measures retrieval ranking, not final answer quality.
LemonCrow was also tested on SWE-bench and Terminal-Bench. On SWE-bench Verified, it resolved 232/250 runs versus 202/250 for baseline while costing 29.5% less. Terminal bench 2.1 corectness 0.79-0.80.
Lemoncrow able to maintain same or higher quality at lower ~30% lower cost and ~25% faster.
Since I maintain LemonCrow, this is not an independent review. I published the raw data and asked the Caveman and CodeGraph maintainers to review the configurations and flag corrections.
https://github.com/lemoncrow-lab/lemoncrow/blob/main/BENCHMA...
But contact me on telegram and we can discuss this in detail.
I was tired of hearing every tools out there, promising 40-50-60 even 80% saving but when it comes to actual tasks they go fuzz.
So I benchmarked everyone to see where they stand. https://github.com/atelier-ws/atelier/blob/main/BENCHMARKS.m...