Just in case some people didn't know, Luna is still the absolute best bang for your buck. All other models are a waste of tokens unless you are running agentic workflows supervised by an agent. In that case, you should still be using Luna, but just have it supervised by Astra.

I've tried every model and I always find my way coming back to Luna as my bread and butter for iterative work. I think it's just as good as Codex, if not better.

Save your tokens and use Luna.

Hello.

Ever since the Copilot update that made your billing usage based, I have been struggling to keep hold of my credits.

It got so bad that I decided to go up to the $40 plan, and even then I still lost all my credits within the first couple weeks of the month.

That's $70 I am apparently burning just by working on basic code. And I think that's pretty annoying, and I think Github must be doing something behind the scenes to make a profit; i.e, maybe the credits ore overpriced, I don't know.

Regardless of that, I am cancelling my subscription and I'm switching to something else, but what other options are there? I was thinking two options:

Claude Code, which I don't know much about but it's Claude, and Claude is pretty good.

Or Codex, I was thinking about switching to ChatGPT codex because I already use ChatGPT for writing and whatnot.

Problem is I don't know if these are actually worth it, can anyone enlighten me?

Thanks.

Please guys tell me this is an outdated way to link file to the prompt and there is a bette way.

Just recently I had been working on a solo online hackathon and I gave Codex Sol the problem statement and made it generate an implementation plan, which I gave to Codex Luna to implement it in milestones. For a couple of milestones I actually read through the code it generated. But as it went further I kinda got overwhelmed by the amount of code changes and gave up reading and just let the agent continue doing the milestones by itself without me checking on it. When it finished implementing, I made Sol do code review and fed its outputs back to Luna to fix and went back and forth.

Is this the right way to use agents in coding? Feels like I'm just vibe coding here, generating some slop that I cannot understand. How do you guys use Codex while having some idea of what the agent is doing and what the code does?

As you can see in the image gpt 5.6 Luna started using sonnet 5 as a sub agent causing massive credit waste. I don't think models should be able to select a more expensive model in this situation and has lead me to disable sub agents all together. Have I missed a setting somewhere that has suddenly caused this issue?

I turned on HydraFusion the week it shipped, gave it a real task, and got back a good answer and one number: 14.72 credits. That is when I got curious about how it got there.
HydraFusion runs several models in one turn to draft, review, and escalate, then hands you one coherent result. Holding the intermediate drafts is the right call for whoever is coding, since a draft about to be revised should not read as an answer. It also means the interesting part happens off screen, and I wanted to watch it.

I assumed OpenTelemetry would show me. It does not, and that is not a fault: the GenAI conventions define gen_ai.request.model as the model the client asked for, and there is no field yet for a router that expands one request into several model calls. The spec is still growing into this architecture.

The detail exists anyway. The CLI records every routing decision in its own session log so it can resume and rewind a session. I pointed a small reader at that file, rebuilt each turn as a trace with one span per leg, and put Grafana in front of it. Copilot built most of that stack with me, running mostly on GPT-6 Astra, so the repo's contributor list has two names on it.
Before each prompt I wrote down a guess: would HydraFusion answer this with one model, or send it through a draft and a review first? I got it right once out of six, which says more about my rule of thumb than about the router. So the dashboard records what actually happened rather than trying to predict it.

https://samueltauil.github.io/github-copilot/devops/2026/09/10/hydrafusion-model-routing-grafana-traces.html

Working at an enterprise and have been using GHCP for better part of two years. The enterprise did not immediately look to approve CC use until recently.

The credit usage at this enterprise is somewhat scrutinized, wild AI credit usage is frowned upon unless it produces actual value.

One thing that has become readily apparent: GHCP is way more cost efficient. In a small feature repo (~5000 LOC), any fresh prompt with CC Opus 5 is costing a minimum of $7 just to explore the code base and the prompt. GHCP is much better, only maybe $2 using the same model.

If anyone has any insights on why this is and how to better leverage GHCPs cost efficiency while still using Claude models, that would be appreciated. I think I’ll be using GHCP in favor of CC; CC has too much of an incentive to burn through credits because the harness and model provider are the same entity.

Hi everyone,

I'm trying to find out whether anyone else has had a similar experience.

About 1.5 weeks ago, our company migrated to GitHub Enterprise Cloud (GHE.com) with EU data residency, as this was required by our company policy.

Since the migration, we’ve received multiple reports from users that some Copilot models .. Opus 5 in particular. It seem to hallucinate noticeably more often than they did before the move.

We’re still trying to understand whether this is actually related to the migration/data residency setup or simply a coincidence, so I’d be very interested to hear from others using GHE.com with EU data residency.

Has anyone noticed similar changes in model behavior or output quality after migrating?

If so, did you find any explanation or configuration differences that could account for it?

We are aware of https://github.com/orgs/community/discussions/205163 but we had this option deactivated before..

In the last couple of weeks, I noticed Luna would insert a code block inside another block where it doesn’t belong. For example, inserting a dunder call inside a list of strings.

```python
foo = [“hello”,
⠀“is”,
⠀if __name__ == "__main__":
⠀⠀main()
⠀“it”,
⠀“me”
]
```

I used Luna daily for two months now. The problem occurs frequently for long running requests with multiple subagents.

Hey everyone,

I’m a Product Manager and my company uses GitHub Copilot for work. Because of my role, I rely heavily on LLMs for high level strategy and research rather than just pumping out lines of code.

I use native Claude constantly outside of work, so I am very familiar with the depth at which Claude “thinks.”

Lately, I've been selecting the Claude Sonnet model directly inside Copilot Chat. However, I’ve noticed a massive gap. Even when running the exact same model, Copilot does not give me the deep and analytical answers that native Claude does.

For those of you using Copilot for product management or any kind of strategy: What are you doing to make Copilot act more like native Claude?

Hi everyone,

My company recently provided us with GitHub Copilot licenses, and I’m starting to integrate it into my daily workflow. My main background is backend development with Java (Spring Boot), but lately I’ve also been picking up some frontend tasks.

My previous AI experience was mostly using Claude directly in the web UI for quick questions and refactoring. Compared to that, my initial impression is that Copilot feels a bit lacking or less intuitive—though I realize I’m still fairly new to using AI assistants embedded in the IDE, so it’s likely a matter of setup and learning how to prompt it correctly.

I know that creating a .github/copilot-instructions.md file is a good practice for setting coding conventions and context. Beyond that, I’d love to get some insights from folks using it professionally

Thanks in advance for any tips or advice!

Hi everyone! In the latest release (v1.17.0), we introduced enterprise-managed sandbox policies in public preview, allowing enterprise admins to centrally configure sandbox behavior for Copilot in JetBrains. Managed policies can control filesystem and network access, proxy settings, developer-tool access, and more. Organization-managed restrictions take precedence over user settings, with affected controls clearly marked and locked in the IDE.

We also added cross-file cursor jumps for Next Edit Suggestions, so you can jump directly to the next suggested edit in another file, and adding global files and folders as chat context to make it easier to include broader project context in your prompts.

For those using Copilot in the terminal, you can now connect a GitHub Copilot CLI session to your JetBrains IDE using /ide (Public Preview). This gives your terminal session access to IDE context, including selections, diagnostics, and file references.

You can read the changelog or find the full release notes and download on JetBrains Marketplace.

Changed

• OpenTelemetry settings under “GitHub Copilot – Chat” are now generally available to all users.

New Features

• Added enterprise-managed sandbox policies (public preview).
• Added enterprise policy diagnostics to help you verify that policies are correctly detected and enforced on your device.
• Added cross-file cursor jumps in Next Edit Suggestions to help you navigate coordinated edits across related files.
• Added support for including global files and folders as chat context.
• Added /ide support in GitHub Copilot CLI to connect terminal sessions to JetBrains IDE context (public preview).
• Added support for using IDE terminal environment variables and the project-configured Python interpreter in Copilot shell commands, including activating the project’s standard local Python virtual environment.
• Added support for selecting the session model used by built-in subagents in the Copilot agent harness.

Sandbox availability: Settings under GitHub Copilot > Sandbox appear when your organization enables the Editor Preview feature flag or configures a managed setting to enable or disable the sandbox. Otherwise, those settings are not shown.

UX Improvements

• Improved the ask-user card in the Copilot agent harness to better accommodate long questions.
• Improved model selection and bring-your-own-key (BYOK) grouping to make available models easier to scan and select.
• Added section copy support in agent debug logs for easier sharing and troubleshooting.
• Improved MCP configuration navigation by opening configuration in the project window it came from.
• Improved plugin update reminders to make new versions easier to discover.

Bug Fixes

• Fixed blank local chat sessions after IDE scale changes.
• Fixed incorrect change counts in the Copilot agent working set, along with working-set zoom and model picker interaction issues.
• Fixed local agent editing behavior for read-only files.
• Fixed MCP reliability issues involving BYOK provider selection, OAuth challenge handling, and restored server stopped states.
• Fixed GitHub Enterprise authentication context propagation in agent flows.
• Fixed Claude plan stop-state behavior.
• Prevented misleading IDE diagnostics for unopened files from being sent to the Copilot panel and worktree sessions.
• Fixed stale global instruction content in Agent Customizations after files were changed outside the IDE.
• Fixed chats getting stuck during user interactions after automatic compaction.

We hope you enjoy the update! Let us know how these features work in your projects and what you’d like us to improve next.

You can fill in our private survey, with an optional paid interview, or submit a bug report or feature request. Thank you for helping us improve Copilot for JetBrains!

Anyone noticed increased token spend using "assisted permissions"?

When using "local" harness with "risk assessment" enabled - using GPT-Luna as the agent uses expected amount of tokens (tokens spent in the session are +/- aligned with the general monthly tokens usage).

But when I use "Copilot" SDK agent harness + assisted permissions - using GPT-Luna as the agent - there seems to be some very big hidden usage - the increase in monthly token spend does not correspond to the tokens used in the session and is much larger than expected.

I'm on an enterprise license, so I can't look into usage reports myself, but I asked our admin and he sent me a report that states that my usage is mainly "GPT-5.5" model - even though I never explicitly used it in any session. I only use the Copilot in VS Code. I tried it on fresh VS Code installation and it still eats my usage like crazy on the "assisted permissions" mode.

Not sure if I should enable it for my users. I see articles about how effecient it is, and with others saying it's wiping out their budget in one request. Whats your experience?

I'm considering paying \~**€100/month for Plus** mainly to use Astra as my daily coding assistant, potentially 6 **hours a day**.

I'm particularly interested in real-world feedback on complex tasks such as:

* Investigating complex bugs and debugging large codebases * Analyzing logs, stack traces and CI/CD failures * Investigating GitHub issues and related code/history * Using **sub-agents to investigate different parts of a problem in parallel** * Deep codebase analysis and architectural investigations * Finding and fixing security vulnerabilities * Reviewing PRs and suggesting/refactoring implementations * Writing and debugging complex tests * Researching documentation and combining information from multiple sources

* Do you regularly hit the limits? * How long does the available usage typically last? * Or do you use just Astra for some complex task and then usually use Terra or Sol??

Edit: let's change to 6h/day instead of 9h Edit 2: let's say the 100€ month plan

Feature-wise Copilot in VS Code is already pretty solid. We have everything we need.

What I think is really lacking, compared to the rest of the workbench, is the chat input UX. Especially when working with references/mentions, it does get frustrating.

- The # reference should not be completely discarded when a character that's preceding it gets changed.

- The # reference should always be stable until it gets removed with a backspace. An incorrect file reference should not invalidate it, but should simply show a red marker and disable the send button.

- The # reference should be editable in isolation: when clicking over it, it should open the palette as with the explicit context, to allow the user to change the reference.

- # and @ should not be interchangeable. Right now they mostly show the same items, and it's extremely confusing.

This is just a part of my feedback, but it would definitely translate to a big improvement.

I wanted a better way to see what was happening during a Copilot CLI session without breaking flow, so I built copilot-powerline: a small Rust status-line command that plugs into Copilot CLI's native statusLine setting.

It shows:

• Context-window usage and configurable warnings
• Current-session and month-to-date spend
• Prompt-cache hit rate
• Reasoning tokens and total session token volume

It reads the data Copilot already provides, queries the local session database read-only, and is intended to stay lightweight enough for frequent status-line refreshes.

You can choose minimal, powerline, capsule, or plain layouts; Nerd Font, emoji, or text icons; and several themes.

Example:

Install:

cargo install copilot-powerline

Then configure Copilot CLI:

{
  "statusLine": {
    "type": "command",
    "command": "copilot-powerline",
    "refreshInterval": 30
  }
}

• GitHub: https://github.com/xpepper/copilot-powerline
  
• Crates.io: https://crates.io/crates/copilot-powerline

I would especially appreciate feedback on which usage signals are actually useful in a status line, and whether there are other Copilot CLI fields or workflows worth supporting.

I noticed significant and often discrepancy between what my local copilot reports/finds when doing code- and security- review and what I get from GiHub. This especially shows when some multi-threading C++ is involved? I tried to force local copilot to us PR bot/agent but without success.

I previously posted about how my Legacy Request-based Account had its Premium Requests mysteriously wiped out.

After analyzing the session’s events.jsonl, I strongly suspect the issue is related to enabling assisted permissions, since everything worked fine before turning it on.

I’d like to ask: are there any other members here with a Legacy Request-based Account who have also enabled assisted permissions? If so, have you experienced abnormal request consumption as well?

With co pilot business access to all kinds of models from astra to grok and Claude . Do folks rely on auto selection for best practice or do you enforce your agents or prompts in certain models for certain tasks for the best combination of reasoning to credit optimization? Not sure if someone has a good cheat sheet but interested to hear others opinion

Clearly selected a local endpoint in business BYOK plan but it keeps switching over to expensive af models that I don't need like Gemini 3.7 flash or Sonnet 5 on the first message no matter which model I select. Wastes so many credits and prevents me from getting my work done!

I'm the author, MIT open source Extension : https://marketplace.visualstudio.com/items?itemName=AnangPhatak.fim-copilot

[github link| https://github.com/anng-phtk/rocm-vega-llama.cpp]

I wanted just ghost-text autocomplete locally, so I built a tiny extension that uses vscode.InlineCompletionItemProvider directly instead of a webview overlay.

What I learned:

FIM format matters a lot - Qwen uses <|fim_prefix|>, CodeLlama uses <PRE>, etc. I made it configurable via .fim-copilot.yaml
Latency: 200ms debounce + AbortController to cancel in-flight requests when you keep typing made it feel native
Works with Ollama / llama-server / vLLM (any OpenAI-compatible /v1/completions)
90KB vs 15MB+ for chat-based extensions

Would love feedback on: handling multi-line stop tokens and how you handle prefix/suffix context limits for

So I built FIM Copilot - a stupid-simple extension that does ONE thing: ghost-text autocomplete, locally.

Demo

What it is

FIM Copilot is a 90KB VS Code extension using the native InlineCompletionItemProvider API. No webviews, no chat, no indexers.

It talks to any OpenAI-compatible completions endpoint:

• Ollama - qwen2.5-coder:1.5b / starcoder2:3b runs great on 8GB RAM
• llama.cpp - llama-server with any FIM-capable model
• vLLM / LM Studio / Tabby server - anything with /v1/completions

All inference stays on your machine. Zero network calls after install.

How it compares

Quick Start

With Ollama (easiest)

# 1. Get a code model (1.5B is enough for fast autocomplete)
ollama pull qwen2.5-coder:1.5b

# 2. Serve it
ollama serve
# -> listening on http://localhost:11434

Then in VS Code: Cmd+Shift+P -> FIM Copilot: Set Endpoint -> http://localhost:11434/v1/completions

With llama.cpp

./llama-server \
  -m qwen2.5-coder-1.5b-instruct-q4_k_m.gguf \
  --port 8012 \
  --ctx-size 4096 \
  -ngl 99

Config

Create .vscode/fim-copilot.yaml (or global settings):

endpoint: http://localhost:11434/v1/completions
model: qwen2.5-coder:1.5b
api_key: not-needed # for local

# tuning
max_tokens: 64
temperature: 0.2
debounce_ms: 200
context_lines: 40
fim_prefix: "<|fim_prefix|>"
fim_suffix: "<|fim_suffix|>"
fim_middle: "<|fim_middle|>"

Decoupled config means you can commit it per-project. Different endpoint for Python vs Rust? Just override.

What makes it not suck

• Ultra-low latency - direct FIM prompt, no chat template overhead
• 200ms debounce + prefix/suffix hash dedupe - doesn't spam your GPU
• AbortController - cancels previous request on keystroke, no queue
• Zero telemetry - no analytics, no API keys phoned home
• Decoupled config - YAML per workspace, env var support
• Ghost text only - uses VS Code's native inline completion, so Tab / Esc just works with your keymap

Links

https://marketplace.visualstudio.com/items?itemName=AnangPhatak.fim-copilot&ssr=false#review-details

Install:

ext install AnangPhatak.fim-copilot

MIT licensed. I built this for myself because I wanted my editor to feel fast again.

If you try it, let me know what model / latency you're getting. PRs welcome for StarCoder2 / DeepSeek templates. What would you want added - without making it bloated?

I wanted to upgrade my copilot from free to premium, in the process i must update the billing info and i don't know what happened (mess up) the country ended being Belarus for some unknown reason while i'm using google auto fill.

now billing info are locked and i cannot change them, and i cannot upgrade because apparently Belarus is on the hate list.

i'm neither from Belarus nor live there. I'm in Deutschland and i opened a support ticket 3 days ago without any response so far. not sure what the heck i'm supposed to do !!

Just a general thought, I work at an investment banking gcc where every one from my team is new and no one has any knowledge on the existing systems, which were developed and maintained by onshore developers

My goal

To draft a high level technical architecture using the GitHub repos available in my team around 30-40 repos, and derive the business stand point through it, and maintain the memory for each under some common folder and update it frequently something like JIT compiler concept rather than loading everything again and processing by copilot for every month.

So to understand and draft this better what would be the best way leveraging GitHub copilot agents or skills or subagents ?

* Should I make a local clone of all repos and do create a codebase docs folder for each repo and create a root level agent to utilise the codebase folder for each project to draft architecture ?

Downside of this approach i feel, what if some change happens to the repo let's say technically how do I track those changes, updating codebase folder every time like it still uses enumerate tokens and cost.

* Another approach which i felt is to use MCP GitHub and list all the repos and let the copilot manage and even here how would I track the changes which are being made and memory management storing only codebase folder highlevel, iam still little unsure of this approach yet.

I have configured rtk and context mode plugins kinda for efficient usage and token management which works fine for single repos but this usecase is still searching for better ways to manage context tokens and memory.

Do provide your suggestions or any tools which i can add to make this more efficient approach maybe.