u/friedsonjm

Heck, I was spending +$100/month on Copilot anyway... hopefully I won't have to sign up for GPT as well.

I'm 1193 of my 1500 premium requests at no extra charge and the estimate says next month the cost would be $594.16 presumably minus the cost of the Pro+ plan. Obviously can't stay here. Pending what Google announces next week, I'm guessing Claude $100/mth plan and maybe some OpenRouter for GPT access to 5.3-Codex 5.5 etc. I wish I could figure out how much my Claude usage in Copilot would be in the $100/month Claude plan - how much of it would used, or how much over, etc.

Looking for possible options that might be more economically friendly that the upcoming Github Copilot api prices, I ran an (expensive) test of 4 alternative AI models and GPT 5.3 Codex as a control, using Openrouter for consistency. Task was to build a file manager for Macos with encryption capability (prompt at the end of this post.) Results were: Deepseek V4 - unable to finish, did a backend with no UI, prompted to create the UI started going around in circles until I gave up and killed it, less than $1 spent. Quen 3.6 created a structure with no details, lots of prompts later had spend ~$4.5 on Max, switched to Plus, gave up after a total spend of about $14. Mimo 2.5 Pro was unable to produce a working UI, gave up after spend of $4.5 5.3-Codex was the only model able to complete successfully, spend of about $11. Note that on the others I only stopped when it became clear they were not likely to be able to complete successfully. I had initially planned to try Kimi but figured I'd spent enough time and $ by this point and stopped. If someone wants to try some other models and post results that might be helpful. Prompt was:

Objective: Create a production-ready, highly reliable macOS/iOS file management and encryption utility.

App Requirements:

Architecture: Implement using Clean Architecture (MVVM-C) with a heavy focus on protocol-oriented programming. All services (Network, Crypto, Data) must be modular and decoupled from the UI to ensure high testability.

System Reliability (Priority 1): The app must be resilient against system interruptions (app suspension, network drops, file lock contention). Prioritize robust implementation of the FileSystem Watcher (DispatchSource) and Keychain security.

Performance (Priority 2): Implement a memory-efficient AES-GCM file encryption utility that processes data in chunks to handle files > 500MB without exceeding a 100MB memory footprint. Ensure the UI remains responsive using AsyncStream and strict background actor task isolation.

Data & Networking: Build a testable SwiftData store with migration support and a robust URLSession network service featuring exponential backoff and custom error types.

UI: Build a responsive SwiftUI interface featuring a NavigationSplitView sidebar, a virtualized table for large file lists, and an async-driven metadata preview panel. Users should be able to choose folder, file(s) for encryption/decryption and be able to modify suggested file names on saving.

Testing & Correction Requirement:

Automated Testing: You must provide a comprehensive XCTest suite covering 100% of the logic in the Data, Network, and Crypto layers.

Iterative Self-Correction: Once you provide the code and test suite, you must perform an "automated audit" of your own code. Identify potential edge cases, concurrency issues, or race conditions. If you identify an issue (or if I provide a failure case), you are required to rewrite the specific component, resolve the error, and re-run the relevant unit tests until the implementation is bug-free and all tests pass.

Evaluation:

I am evaluating your efficiency by the ratio of (High-Quality LOC) to (Total Tokens Consumed) and your ability to deliver a production-ready, test-passing codebase in the fewest number of turns. Focus on correctness and resilience over unnecessary verbosity.

Iterative Self-Correction: Once you provide the code and test suite, you must perform an "automated audit" of your own code. Identify potential edge cases, concurrency issues, or race conditions. If you identify an issue (or if I provide a failure case), you are required to rewrite the specific component, resolve the error, and re-run the relevant unit tests until the implementation is bug-free and all tests pass.

Evaluation:

I am evaluating your efficiency by the ratio of (High-Quality LOC) to (Total Tokens Consumed) and your ability to deliver a production-ready, test-passing codebase in the fewest number of turns. Focus on correctness and resilience over unnecessary verbosity.