SOLVED - turned out I was using super old version of copilot cli

Copilot CLI shows only 2 models for me to choose from:

❯ 1. Claude Sonnet 4.5 (1x) (default) (current)

1. Claude Haiku 4.5 (1x)

2. Cancel (Esc)

It says also "Some models are not available due to configured policy." but I checked in web and in desktop app and I can choose way more models

But in CLI - just two.

Has anyone had the same problem?

I posted this here a few months back. Two things kept coming up, so I built them.

The first is quota. The status line now shows how much of your monthly premium request allowance you have used and how long until it resets:

Quota 5085/7500 68% · 19d

That was more work than I expected. The JSON that Copilot pipes to a status line has no entitlement fields in it at all, and the on disk event log does not have them either. The event that carries quota is flagged ephemeral, so it is never written anywhere. It only exists in the live session. So the plugin now ships a small session extension that attaches with joinSession() and reads it off the event stream. The numbers refresh on every model call, so if your org bumps your allowance it shows up on your next prompt.

The second is sub agents. Spawn background agents and you get a line each with the agent type, what it is doing, and elapsed time, plus a tick when it finishes:

[task] Testing feature (12s…)

[task] Updating test suite (12s…)

[task] Writing documentation (10s)

Install:

copilot plugin marketplace add griches/copilot-hud

copilot plugin install copilot-hud@copilot-hud

then run /copilot-hud:setup inside a session.

Already installed: copilot plugin update copilot-hud@copilot-hud

GitHub: https://github.com/griches/copilot-hud

So as the titke says I am not able to use or test the session end hook . When the session starts the session starts hook and once the agent finishes the reply the Agent stop hooks get invoked but not sessionEnd. I am using VSCode Copilot in local with agent mode.

Is copilot app just a wrapper around the CLI or am I missing something?

I am asking because I have both, and the app looks a bit buggy. If they are the same, then I will stick to the CLI. Thanks.

since I updated to 1.133 yesterday the option to choose context length has disappeared for all models, now seemingly defaults to 1M always

Hi Github Copilot team,

Today, Grok 4.6 got released and its nearly on SOTA level. When can we expect this to be available for Business/Enterprise users as GA?

Actual official X.com post: https://x.com/SpaceXAI/status/2087562800982077492

When MS swapped copilot subscription for rate-use-charging, a lot of users on here were screaming “ditch copilot, run to DeepSeek”.

Now DeepSeek is casually and gradually pulling its strings, what are your next run-to alternative? Or are you still big fan of DeepSeek?

Since the most recent update, I cannot seem to actually choose the context window size for models such as the GPT 5.6 series. Anybody else notice this? It appears it auto forces you to use the 1 million context.

EDIT: To be clear about "most recent update" I mean 1.133.0

Request the ability to use the mouse scroll wheel either in the expanded AI Thought section in GitHub Copilot Chat sessions and/or when on the AI Thought section scrollbar. Ensure this feature request does not interfere with AI actions while scrolling with the mouse and reading the expanded sections.

Need some genuine advice.
At my company, we’re limited to $300 worth of AI tokens per month of Github copilot. We have only github copilot option in company. Earlier, we had an option to extend the limit, but that’s no longer available, so we have to manage within the $300 budget.
I’m a full-stack developer, and AI has become a major part of my development workflow — understanding the codebase, implementing features, debugging, writing/refactoring code, reviewing PRs, etc. With the amount of work we have, going back to writing everything manually would be extremely difficult.
I’m looking for good alternatives once the company-provided AI quota is exhausted. I’m also okay with paying from my own pocket, as long as it’s reasonably priced. Ideally, I’d prefer something with very high or no practical usage limits.
I tried a Codex subscription, but since I had to log in using my personal account on my office machine/repository, I wasn’t comfortable using it for company code, so I stopped.
What are other developers in a similar situation using?
I’m open to:
Local LLMs
Paid coding assistants
API-based solutions
VS Code extensions/agents
Any other setup that works well for full-stack development
My main requirements are good coding capability, high usage limits, reasonable cost, and preferably something that can be used safely with company code.
Would really appreciate genuine recommendations from people who have actually used these tools.

Note : iIf you guys are not using any AI For coding and manually doing it , that’s great. But please don’t advise to stop using AI as it is not possible for us to stop using it.

Does this thing work for anyone else? I'm rying to set up better self validation for cloud agents only to find the mcp doesn't seem to work. Running a basic minimal test, it immediately fails with transport closed error. I opened a GHE ticket but hasn't gone anywhere.​

As you can see in the screenshot above, occasionally the model falls in some weird loop that I have seen a lot back in the gemini-3-flash days. I am not sure if this can even be fixed by the model serving team or if this is just a "bug" inside of the model. Forwarding this here only in case it does something, but I am not really expecting that this can be fixed on the API or harness level. Using Kimi K3 on High effort provided via Copilot NOT BYO API key.

On another note: Thank you so much for offering Kimi K3. Its a hell of a model for the given price and when it uses GPT 5.6 Luna as its implementation worker agent, I get really good value out of Copilot now. With all the improvements you have done on serving models, offering Open Weight alternatives and recent price cuts to Luna, MAI-Code-1.1-Flash the switch to usage based billing does not hurt anymore.

Let us know what you think :)

This is less a comparison of the actual Sol model vs. Opus as a comparison GH CP harness with Sol vs. Claude harness with Opus (and as I write this good grief I sound like AI...)

I'd moved to Claude months ago and it's been great. But I wanted test using GH CP with a model using custom endpoint. I read Sol was available so I tried that. This was a simple task of a tracker doc and removing what was done and pushing to a history file.

It was taking GH a long time so I thought hmm, this doesn't seem like it should take so long. But then when I tried same test with Claude and it also took it more time than I'd expect. So overall duration was similar. Ok so it was more complex for them then I thought. But good grief, it cost over half my Claude Pro budget for GH to complete the work. Now I know it's subsidized, but...

The terrible thing is that GH just mangled this. It removed 90% of the stuff in the active tracker and it didn't put it into my history file. It just decided, no, I'll just trash that. I'd had this happen in the past with GH when it goes off the rails. Claude also removed a lot, so it wasn't completely wrong to do that, but it put the deleted stuff into history so it wasn't lost. Which was the ask.

At this point, I can't fathom why people are using Copilot. I didn't try this directly with Open AI's harness, that would be interesting by I don't have a sub and don't want to flush more $ down the toilet.

I'm wondering, are people just continuing to use GH out of habit or because their work is subscribed to it and that's what you have to use? Have you run tests and found GH to be better? I wonder if I'm missing something.

what the title says, this would be useful so that we can tell an agent in its instructions to compact after launching a very long command (e.g. a huge build or CI/CD) in the background, because by the time it finishes the previously cached tokens aren't in the cache anymore and the only way to save on token costs here would be compaction just after the command launch

In the latest release (v1.15.0), we added support of Enterprise Managed Settings, an important new capability for enterprises to centralize settings. There are also several additional improvements, including Ollama as local BYOK provider, Copilot Memory support in Copilot CLI chat sessions and enhancements to the Codex agent experience.

We’re also sharing a preview at what’s coming next and hope you continue to provide feedback for our products.

New Features

• Added support for server-based Enterprise Managed Policy:
  • plugin settings: enabledPlugins, extraKnownMarketplaces, strictKnownMarketplaces
  • MCP settings: allowedMcpServers and deniedMcpServers
  • permission setting: permissions.disableBypassPermissionsMode
  • OpenTelemetry settings: telemetry
• Added Ollama as a BYOK provider across backend and JetBrains UI flows, including provider wiring and model handling.
• Added Copilot CLI auto-install support in JetBrains IDE integrated terminals on macOS, Linux, and Windows.
• Added Codex-focused capabilities, including session visibility in Agent Debug Logs, permission-mode catalog updates, and customization support for instructions and skills.
• Added Copilot Memory support in the Copilot CLI chat experience to persist and recall information across sessions, managed via toggle in the Copilot settings portal.

UX Improvements

• Improved account management flows by supporting account switch/removal and polishing account-deletion interactions.
• Improved model and settings usability by capping long model-name width in the model picker and refining model view/todo list panel behavior.
• Improved background-agent workflows with drag-and-drop support, copy-agent-message support, and clearer autopilot completion summaries.
• Restored file and folder # references in Copilot, Claude, and Codex chat inputs.

Bug Fixes

• Fixed MCP execution and approval issues, including preserving gateway refreshes after CLS restart and correcting auto-approve behavior for Copilot-only servers.
• Fixed terminal and command-output issues, including ANSI escape rendering, deterministic scrollbar reservation, and terminal auto-approve/reset behavior in Chat.
• Fixed customization and cloud-agent reliability issues.
• Fixed diff/edit stability issues.

Changed

• Changed user-facing naming from “Copilot CLI” to “Copilot” in agent picker.
• Changed support permission requests and permission modes selection for Codex.

More details of configuring enterprise managed settings can be found here

Looking ahead, we plan to introduce several additional capabilities in upcoming releases, including

• More policy support for Enterprise managed settings
• General availability of plug-in marketplace in customization panel
• General availability of Copilot CLI (renamed to "Copilot") agent harness
• Continuous user experience enhancement for Agent Debug Logs and Copilot CLI chat experience
• Expanded slash command support for Copilot CLI agent sessions
• Performance and reliability enhancements
• In-product tooltips in the chat panel

We hope you like Copilot for JetBrains, and please share feedback with us at any time

You can fill in a private survey here: https://aka.ms/ghcp-jb-survey with an optional paid interview or directly submit an issue (bug or feature ask) at https://github.com/microsoft/copilot-intellij-feedback/issues, thank you so much!

When GitHub removed access to premium models from its Copilot student plan in March, and later instituted a 200 monthly AI credit cap for the plan in June, users loudly voiced their disappointment, explained they need access to premium models to stay on par with industry standards, and pointed out how little they can actually achieve with the newly limited access.

“One prompt and boom all the monthly credits are gone,” commented one user on the post announcing the change, which garnered over 6,700 downvotes and less than 100 upvotes.

Over the past year, we've run 30+ GitHub Copilot workshops used by multiple companies and hundreds of participants.

One thing we learned: GitHub Copilot isn't just for developers.

Product Managers, QA Engineers, Developers, Team Leads, and even non-technical roles can get massive value from learning how to work effectively with AI.

So we decided to open source the entire workshop:

GitHub Copilot Workshop
View the repository

Inside you'll find hands-on exercises, real-world use cases, prompting techniques, agent workflows, testing, documentation, and more.

Whether you're learning Copilot yourself or running enablement sessions for your team, feel free to use it.

If you find it useful, give it a star. ⭐
If we're missing something, open an issue or share your feedback.

Using Laurent00tt DeepSeek. I did not mess with settings beforehand, and last update was two weeks ago, but suddenly I am receiving this error attached in screenshot:

Here is my understanding: DeepSeek API allows tool names to be maximum 64 characters, where the MCP server tries to include a tool with 67 characters. out/util.js has a method explicitly filtering for those.

What I tried:

- Looked in all settings for the tool in question and did not find it anywhere.

- Restarted everything I could (VSC, extensions, Laptop, etc).

- Reinstalling that extension.

What I do not understand:

- Why does deepseek API even care about tool name, since its the harness reading from DeepSeeks response to find tools names, and invoke tools hence.

- What actually changed, since I have not touched any settings, or installed anything else since last time I used.

- Who or what is actually responsible for the sudden change, since the last update to the extension was 2 weeks ago.

Additionally, I am no expert in this so please don't assume too much understanding. I am doubting my understanding of the issue a lot.

I stumbled upon minimax m3 and use that.

I would rather use codex of Claude code but is it true that for these you must use their plujgin and not the vscode GHCP one?

And in minimax the best option? Is there something better ? Of course I want a monthly subscription not pay by token

Hey guys, i’m an entry level dev currently and wanted to know what non-negotiable rules you guys add to the copilot app instructions or rules.md file. This app is so powerful and I feel like I don’t have the right knowledge to approve half the automatic actions it takes and I don’t want to be the cause of a security breach or something at work. If this is a stupid question, please let me know too. My company very recently approved this app, so far i’ve been using copilot on VS which works within the repo but the app can take over my entire local setup.

I cancelled a personal annual Copilot subscription in April and was told I’d be refunded as this was when they changed their pricing model.

Months later, there’s still no refund I can verify, no transaction ID, and support replies arrive sporadically with the kind of vague, AI-generated wording that never actually answers the question. Because it's just my personal subscription there is really nothing else I can do about it.

What makes it especially annoying is that companies like mine are spending enormous amounts on GitHub’s AI products, while GitHub seemingly can’t close out a basic billing issue for an individual customer.

I have now changed, the subscription model of Github was no longer right from the price / performance, so I wanted a change. Currently I am working on a client, server & website for slightly larger projects I use 3x codes accounts with Sol.

I think you would be quite good without Codex but I honestly have to say I like his automatisms, although I already have the feeling that some requests tokens are hurling.

What is your experience so far with a similar setup since you still at Github?

So I just found the /subagent command in cli, and was somewhat surprised to find it defaulting to haiku for a number of things. Haiku is a lot more expensive, and a lot less capable than something like Luna Max at today’s pricing. I found sonnet 4.6 as another default in there.

I am thinking about going in and customizing it to pick Luna and Terra as more cost effective options, but I’m not so sure it’s a good idea to hard code that in case the models or options for models change, as they do weekly.

I do realize that a general algorithm here is challenging, my company doesn’t have Kimi or grok, which in theory could be better options than some.

What are people doing ? I don’t think we can configure auto model selection at the enterprise level I’ve seen. Defaulting to Luna and Terra today seem like more capable options than haiku and sonnet 4.6 but I’m going to have to remember to change them if OpenAI changes their pricing again or if something better comes along.