r/GithubCopilot

After GitHub Copilot slashed its limits, we wondered: how much API-priced token value do other coding agents actually give? We did the math and here is what we have

I have been using opencode and codex for a while and iirc opencode using the linter to verify code, live, as it outputs, which helps curb tokens costs. I saw code quality increase by making some small changes to lint, and was wondering what other guardrails you guys use?

Aside from basic CI/CD workflows(worked scoped int tests, regression, building etc etc) is there anything you guys do to improve coding with codex?

Hey all,

today I tried out Sonnet 5 for the first time on our business plan. But it's just so slow. It takes 10 minutes for something that Gpt 5.4/5.4 mini or 5.5 do in 2-3 minutes.

Are there any tricks or settings to make it think and write faster? I'm using the default reasoning.

This used to be my favourite product and have developed lot of things with it's flat pricing era.

Now, it's just 2-3 hours of serious work and BOOM!, monthly quota expired within few hours. I seriously don't understand what kind of pricing model is this?

Can anyone please suggest an alternative?

In my experience, there is nothing close that compares to github copilot.
Its feature rich integration with VScode is lovely, its clean, simple, and with the Integrated browser, copilot CLI and extensions support for vscode, everything is possible with just Vscode and copilot.

Yes there is a whole world of local AI with softwares, I used few of them ollama, openrouter, cline, continue, aider etc. Basically you dont have to worry about huge hardware cost and low context size support with local AI and integrating multiple other softwares to make the local AI actually work.

If copilot was not there, it would have been hard, I likely would have gone with openrouter cloud models plus cline or something with VScode. Just happy to have copilot man. Eager to see deepseek, glm on copilot cli.

This post is not about pricing or subscription costs or tokens cost.

https://preview.redd.it/wxdpvkiuhibh1.png?width=655&format=png&auto=webp&s=eadc87de4c78afe81c94d03557ed626deace44f4

Even though no response was returned, it burned through 50% of my usage... I tried to create a ticket at Copilot's recommended page but it keeps just giving me AI recommendations which aren't helpful at all instead of creating a support ticket. Their AI is preventing me from creating a ticket apparently (probably not on purpose, but as a side effect because it's too "stupid"). Meh. Not sure what to do other than not use Fable (this is where it happened).

I have an annual subscription to GitHub Copilot Pro, but the GitHub Copilot plugins in both JetBrains and VS Code do not include the Claude Sonnet 5 model. Is anyone else experiencing this issue?

Hello folks,

I wanted to build out my own personal list of evaluations, early on into putting this together I realised I wanted a way to not just evaluate the model but also the agentic harness that the model is running within, as I find the majority of my use of LLMs is more and more inside of a suite of CLI agentic harnesses.

I've listed in the video a multitutde of motivations for why I built this, but the primary ones were all the hype announcements and wanting a way to see for myself what models and their capabilities were like in the actual tools I use.

A paper by Google over on Kaggle recently went as far as to state that which LLM being used inside of an agentic harness perhaps only contributes 10% towards how effecitve that harness will be for a given task. I am not sure I agree with the figure, but I do agree with the sentiment.

One question I keep asking myself is when do I need to switch from my qwen3.6-27b that I am running locally on my twin 3090 setup, to a cloud model. At the moment I am making this decision on vibes/gut feel, and I think that might be okay for when I am working closely with the model but I am using these cli tools headlessly in quite a few workflows now and not just personally but professionally, so I want to make sure I am picking the right combo for the task.

The repo can be found here: https://github.com/ScottRBK/eval-harness, there is an explanation of the architecture. I have added example evaluations as I built it out to help me think about the different patterns I have utilise for evaluations.

The evaluations are quite easy as they are about resources contained within the model weights. The idea behind it is though I (and anyone else whom might want to fork the repo and curate their own) will build out a private list of evals that are held away from public that people can use to evaluate existing and new models and harnesses as they are released.

I also spent a good bit of time seeing how well cli agents themselves are able to build evaluations and have put together a list of skills that they can use alongside the tool. They do an okay job, but you need to really step through the logic of whatever they produce, they often produce quite brittle evaluations, so try getting them to stick to the example patterns already provided helps quite a bit.

An ideal position for me will be having the ability to ask the agent to generate an evaluation using the skills having just finished a session where I found a particular agent was struggling to complete a task. Theres often been a time where I've come across a problem that the agent has struggled to resolve and I've wished at that point I could make an evaluation out of it, but you are often in the middle of something and it ends up just as another item on my ever growing TODO: list.

This is my first time building an actual evaluation suite or framework of this kind for that matter. I have previously used existing frameworks, such as deepeval, so I was not toally unfamiliar with the topic but as with the other motivations already listed I built this as a learning exercise as well as to get a tool out of it.

If it is useful for you please get in touch and let me know, any feedback as well is also appreciated, as this is my first go and this kind of framework - i expect there is a lot that can be improved and I have potentially got wrong.

Enjoy the rest of your sunday folks.

What tricks have you all found that helps Fable 5 stop dropping to Opus 4.8?

I look after AI delivery in my organisation. We currently have Github Copilot as our enterprise AI software development harness, adoption has been slow and the engineers haven’t really built a practice around structuring the repos to be AI optimized , creating agents that do certain tasks well, etc..

The head of software development in our org is now making a push to move to Claude Code and transforming into a full on AI-SDLC. He suggested a rather weird set up (imo) where we build agents using bedrock and agentcore , and use the claude agent SDK.

I believe we can reach 90% of the value of AI assisted coding with copilot github as opposed to other harnesses , and should focus our effort on training engineers so that they build an AI friendly engineering practice/context/repo structure etc..

Is my instinct wrong here? I have been following this space for a while, and while Copilot 365, Copilot Github, and Copilot Studio have a bad rep for being the worst AI offering, Microsoft has been really improving their value over the past few months.

For some reason the response gets cut abruptly. The model returns "Thinking..." and then simply stops.

Is this a known issue on VS Code Insiders? How do I debug the root cause?

What would cause Visual Studio 2022 to not read/apply "ConfiguredBringYourOwnModel_v1.json"?

On my machine with both Visual Studio 2022 and Visual Studio 2026, I've configured Visual Studio 2026's GitHub Copilot to use Ollama and my custom models show up in both versions of Visual Studio.

However, a co-worker only has Visual Studio 2022 installed and even though they manually copy the same json file to "%LOCALAPPDATA%\Microsoft\VisualStudio\Copilot\BringYourOwnModel\ConfiguredBringYourOwnModel_v1.json", the custom models are not available for selection.

What is the missing piece to activate BYOM with an Ollama Provider in Visual Studio 2022?

We are using the latest versions of Visual Studio 2022 and using a GitHub account not tied to an Enterprise organization.

[
  {
    "Name": "Ollama",
    "IsApiKeyAvailable": true,
    "Models": [
      {
        "ProviderName": "Ollama",
        "IsCustom": false,
        "IsSelected": true,
        "CustomURL": "http://localhost:11434",
        "Id": "My Custom Model",
        "DisplayName": "My Custom Model",
        "IsToolCallingEnabled": true,
        "IsVisionEnabled": true,
        "MaxInputTokens": 1000000,
        "MaxOutputTokens": 8192
      }
    ],
    "Endpoint": 10
  }
]

Thank you.

For me, it just says: "Sorry, the response hit the length limit. Please rephrase your prompt."

Any tips on how to use it?

Is GitHub Copilot App similar to Claude Desktop app ? At first insight they look quite similar

But would like to know more about GitHub Copilot App and how we can leverage it to its max ?

I'm preparing a technical presentation for my team about GitHub Copilot. I'd like to include some of the latest features and practical, real-world use cases beyond basic code completion Any "hidden gem" features that aren't widely known.

Thanks in advance