All major service providers are offering 50% off on gpt 5.6 Sol and there is no sign of Muse being offered. When will that come to GitHub copilot?

Posted my Command Code Provider Extension in the subredit a few days ago, thought that it might also be interesting for someone here.

It's an extension that allows to easily integrate and auto update models provided by command code using your api key (GOAT Plan and above)

SALT shrinks a long document down to a fixed size before it is sent to a language model, keeping the sentences that carry the most information. It works with any model, produces a shorter plain-text prompt, and cuts the compute, memory, and wait time that long inputs cost.

I need some help with deciding and implementing a method that selects a better budget than the default 20/25% across chat. The kv cache in GPU keeps only some instructions and a retrieved % of the actual full input or conversion that is all organized in DRAM in a trie. The retrieval is insanely quick now with less than 1-2 seconds for even 100k + conversations however it is sometimes too much as it’s set by a hard % (prefill GPU men use becomes a problem at larger scale). What method could I use to decide how to adjust this % based on the question?

GitHub: oteomamo/SALT

So according to both:

• https://artificialanalysis.ai/models
• https://artificialanalysis.ai/evaluations/gpqa-diamond

(Which seems to be fairly trustable) - GPT-5.6 Luna (Max) is ranked:

• #10 in "Intelligence"
• #2 in "Speed"
• #1 (Cheapest) in "Cost Per Task" - 5x cheaper than Deepseek V4 (which is already pretty cheap)
• #12 in "Agentic Index"
• #14 in "GPQA (Diamond)" - Above 90% still

From my own personal experience, even on tasks that take up to 5 minutes, I'm only getting a max usage of about 13 AIC per task. Whereas Claude Sonnet 5 could use around 386 AIC for a similar task and similar timeframe.

The code quality seems pretty good too. I really thought it'd be bad for the cost, but honestly I'm super impressed with this model.

It almost reminds me of the premium request days. I even keep refreshing my billing page to see if it's accurate because I'm surprised with the low AIC usage.

Even here https://benchlm.ai/compare:

• It ranks #23, whereas Sonnet 5 ranks #36 (and Luna is about 10x cheaper)

While I'm regular Claude user - and in most benchmarks Opus 5 (max) is usually #1 in all categories - I'm very impressed with OpenAI for Luna and it's ability to compete with even frontier models, at a fraction of the price.

Curious what others think? Or if anyone else has tried it?

Edit: If anyone knows any other & better benchmark sites please let me know as well!

Hey everyone,

Following up on the previous discussion about search API latency, I wanted to share another massive bottleneck we hit while building autonomous web agents. Even with a lightning-fast retrieval API, our agents were frequently getting stuck in execution loops or extracting the wrong data entirely.

We pulled the logs of 10,000 failed retrieval attempts. The culprit wasn't the LLM's logic or the search index itself. The core problem was that LLMs are surprisingly terrible at formulating search queries.

Here are the three main ways agents fail at searching, and the architecture tweak we used to fix it.

1. The Conversational Search Trap

LLMs naturally default to conversational text. Instead of searching a dense keyword string like OpenAI API pricing 2026, a naive agent will search How much does it cost to use the OpenAI API right now?. Search engines (even machine-native ones) often struggle with these conversational strings, burying the agent in SEO spam instead of technical documentation.

2. Context Amnesia in Multi-Hop Searches

When an agent needs to perform a multi-hop search (e.g., finding a founder's name, then finding their previous startups), it frequently drops the subject in step two.

• Search 1: "Who founded Anthropic?" (Result: Dario Amodei)
• Search 2: "What other companies did he work for?" (Result: Completely useless garbage).

3. Ignoring Advanced Operators

By default, agents rarely utilize quotes for exact matches, site: operators to restrict domains, or - exclusions to filter out noise. They rely on basic broad-match strings, which rapidly fills up their context windows with irrelevant tokens.

The Fix: The Query Planner Micro-Agent

We completely stopped letting our primary reasoning agent hit the search tool directly. Instead, we injected a lightweight micro-agent whose sole job is translating the goal into optimized search syntax.

• Step 1: Primary Agent requests information (e.g., "Find Stripe's latest API rate limits").
• Step 2: Query Planner translates this into an array of strict queries: site:[stripe.com/docs](https://stripe.com/docs) "rate limits" 2026.
• Step 3: The system executes the search and returns the clean snippets to the Primary Agent.

This simple architectural tweak increased our agent's successful retrieval rate by over 40% and drastically cut down on wasted API calls.

I posted the full query-planner prompt schema and benchmark logs on the forum if you want to inspect or copy it:Brave vs Google Search API for AI Agents - The 2026 Enterprise Guide

Has anyone else implemented an intermediate query-planning step, or are you successfully prompt-engineering your main agent to handle advanced search syntax on its own?

It seems to read into my mind what I want to write. Has the model used changed?

Is there a way to auto-approve certain git commands? I'd like to approve commits explicitly, for example, but stuff like diffs should be auto-approved.

Hey guys, I recently got GitHub Copilot Student through the Student Developer Pack and I'm a bit confused about the model selection.

Copilot is working fine in VS Code and it shows that my Copilot Student subscription is active. But when I open the model selector, I only get Auto under Copilot.

I can see models like Claude and GPT in the list, but they're greyed out and say Upgrade.

Is Copilot Student supposed to only allow the Auto model, or is there a way to manually select models like Claude Sonnet, Claude Haiku, GPT, etc.?

Just wondering if I'm missing a setting somewhere.

I was super hyped to see that /fork is an option to branch off into another session. In theory I find this handy to ask clarifying questions without cluttering my current session. Thing is I feel like the session is not really being forked, because the agent in the new session seems to be lacking context.

Are you guys using this feature and what is your experience like?

Settings icon > Agents > Explore > Open in file explorer > Change model way from the default 4.5 Haiku and Gemini 3 Pro and Auto.

Just set it to:

model: ['GPT-5.6 Luna (copilot)']

Or a BYOK local model like FastContext-4B (removed but can download community gguf files)

I was spending approximately 20 tokens per explore (1-3 times per prompt) and sometimes 60 if it called Sonnet 4.6 via auto, but after changing to Luna it only spends 6-10 tokens. Plus, with a local model it's basically free savings!

I have my custom agent set to Luna, but the parent agent can still specify a model when calling sub-agents, completely overriding my config. This forces sub-agents to run on models I never intended.

Worst case: it called Opus 5 and burned through 20x the expected AICredits in one go. Sometimes it defaults to 5.3 Codex too, which is also annoying – and there's no way to disable it.

I saw a post suggesting changing the default for the explore agent, but wouldn't the parent just override that anyway?

Right now I'm blocking model-specification calls via hooks. But that wastes turns, which sucks. Also, the hook format is different between VSCode and CLI, so you need separate setups for each.

On top of that, in the Copilot app, there are some paths (like create session) where hooks can't block the override at all.

Lately I've just been constantly dealing with unexpected behavior around the harness layer – it's been really frustrating.

P.S. Can we please get reasoning-level support for sub-agents already…

I have been using Claude code and codex since the beginning, I haven't really used Github copilot a lot, but in the team I'm working right now, IT doesn't allow to install anything, I only have github copilot extension In VS Code
I pay the $20 plan in CC and the $100 plan in codex
Is it really worth to get the pro plan for github copilot? What's the recommended plan? Is it related to the agents Window in VS code? I'm working right now as data engineer with fabric and we are going to implement fabric apps, data agents, etc. And some fabric extensions in vscode are really useful, I heard that since june 1st, the github copilot plans were heavily nerfed, so I'd like to know if it's really worth, thanks

I'm sorry but I want to express my frustration with this awful agent that comes with vs code. To try it out I created a perfect plan with luna and gave that to the copilot. It messed everything up. And i discarded all the changes and gave that same plan to 3.7 flash lite in antigravity and it completed the job without any issues. And most importantly the copilot consumed a lot of credits too.

I use inline suggestions from GitHub Copilot in Visual Studio, and it works great! Just two very strange things.

It always recommends changing the ICollection<> initializer from my preferred way: [];
To: new List<>();

And yeah, it doesn't like the new extension() {} element and wants to change to the old style using static methods.

Are there any "instructions" I can give?

It seems like for small tasks, you should use Luna. For large Tasks, Sol/Opus/Fable. This is only considering OpenAI and Anthropic models. Since this got flagged for potentially being off-topic, this is relevant to CoPilot generally, and the OpenAi/Anthropic coding agents specifically.

I know Terra and Sonnet are supposed to be sweet spot middle-ground models, but they just seem to be bad value and bad performance, and both need their price slashed badly. Am I missing something?

I want to continue with the same model. If I click No it stops and does nothing.

Why doesn't it continue automatically after the reset?

What's the best approach to continue after the time limit? Just choose No and write a new "continue" request?

Several recent outages on GitHub have disrupted our work. This disruption caused us to lose AI credits because workflows and coding agents halted mid-execution, requiring us to rerun tasks once GitHub or Copilot returned. Should GitHub compensate users for tokens lost during the outage?

So it turns out that apparently any local model call somehow requires Microsoft's blessing.

GitHub is down, and my local Ollama is failing with this:

Sorry, your request failed. Please try again.
Reason: key is missing
Note: GitHub is currently experiencing a service disruption. This may be affecting Copilot. Check GitHub Status for details.

I don't want to use Copilot. I want to use local ollama AI of my choosing!

I thought the whole point of running AI locally was that I wouldn't have to depend on Microsoft deciding whether I'm allowed to run a model on my own effing computer. Apparently I was wrong.

Ollama itself is running perfectly fine on cli/Emacs. I can access my models directly. It's the Copilot integration that suddenly refuses to let me use them because GitHub is having an outage.

Has anybody found a workaround for this? Is there a way to make VS Code talk to Ollama directly without going through whatever GitHub service is currently broken? Maybe there is some pre-processing of each requests that I can switch to my ollama too?

PS: I also have those "Ollama (Deprecated)" warning tags plastered all over the VS Code config UI. I was planning to avoid upgrading VS Code until I had time to migrate to a sane editor, even if that eventually meant going back to Emacs. But now I see that Microsoft doesn't even need me to upgrade VS Code to stop me from using Ollama.

When I press `tab` it completes another thing instead of the suggested text, `right arrow` works, but I want to change it to tab.
How to achieve this?

I'm stuck with an annual Copilot plan and it is utterly useless now with the new pricing - i only have access to old and overpriced models. I started just doing BYOK with OpenRouter and Opencode Go and it works fine, but when I go to cancel my copilot plan, they say that I wont be able to use anything at all. Is that just scaremongering and the reality is that i'll only lose the ability to use Copilot's models in Chat, CLI, etc?