[commandar]

They're multipliers against your quota of requests. GPT-4.1 is "free" with a copilot sub, and then the premium models would burn credits against a multiplier. So higher multipliers count more against your monthly quota.

GPT5, Sonnet 4, and Gemini Pro 2.5 are all 1x. Opus is 10x, for comparison.

https://docs.github.com/en/copilot/reference/ai-models/suppo...

Also worth keeping in mind that Copilot has reduced context windows even for the premium models, which has a very real impact on agentic performance.

[indigodaddy]

Thanks for the info. Would you consider GH Copilot the best bang for buck currently, or would you recommend just going with the Claude $20 plan? I'm definitely not looking to spend a lot of money, just want to see what kind of mileage I can get on low-end plans

[commandar]

It's going to depend heavily on your usage.

I use Copilot because work is paying for it and it can be made usable, but requires being really deliberate about managing context to keep things on the rails. It's nice that it gives you access to a pretty decent selection of models, though.

At home, I'm mostly using the $100 Claude plan. It's definitely not cheap, but I've found it has a pretty decent balance for my casual experiments with agentic coding.

Another option to seriously consider is setting up an account with OpenRouter and just tossing some cash into your bucket on occasion. OpenRouter lets you arbitrarily make API requests to pretty much any model you want. I've been occasionally tossing $10 or so into mine and I'll use it when I've hit my usage limits with Claude or if I want to see how another model will attack a particular task.

FWIW, I use Roo code for all of this, so it's pretty easy for me to switch between models/providers as I need to.

[ewoodrich]

I consider the $10/mo to be an incredible value ... but only because of the unlimited 4.1 usage that can be provided to other compatible extensions (Roo Code, Cline support it) with the VS Code LM API.

Unlike some other workarounds this is a fully supported workflow and does not break Copilot terms of service with reasonable personal usage. (As far as I understand at least. Copilot has full visibility into which tools are using it to make chat requests so it isn't disguising or impersonating Copilot itself. When first setting it up there's a native VS Code approval prompt to allow tool access to Copilot and the LM API is publicly documented).

But anything unlimited in the LLM space feels like it's on borrowed time, especially with 3rd party tool support, so I wouldn't be surprised if they impose stricter quotas for the LM API in the future or remove the unlimited limit entirely).

[indigodaddy]

Great feedback, thank you! I see a lot of 4.1 bashing-- how's your experience been with it?

[ewoodrich]

It's my workhorse model with Roo Code given the cost - or lack thereof. I was about to cancel Copilot after they massively cut the premium limits until they swapped out 4o with 4.1 for the base model. 4.1 is just decent enough for simple, uncreative tasks and is pretty reliable as far as tool use (especially compared to 4o) so I have had a lot of success with it.

For any problem with a lot of reasoning or problem solving I use "architect" mode first with Gemini 2.5 Pro or Claude Sonnet 3.7/4 to break it into discrete subtasks that 4.1 can follow pretty successfully. This approach is very cost effective as Gemini can do a lot of high level planning quickly and cheaply.

I'm sure a lot of the experience depends on how 4.1 is being used, I've fine tuned my custom Roo code configuration to work around its limits without a lot of sacrifices, I'm sure using it out of the box with Copilot is asking a lot more from a weaker model on its own.

[indigodaddy]

Sounds like you’ve figured out a good workflow for yourself. When you switch back and forth between models like that, do they know about all the previous interactions and context? (They must right?)

[ewoodrich]

Yes, either through passing the full context of the current task/conversation to a new model to continue working from that point on, or through the intermediary step of the plan document generated by Gemini or whichever larger model that is then passed back to 4.1 to implement.

The latter is a commonly recommended strategy in general for any large task even with more powerful models to keep context manageable and allow recovering easily if on step 9/10 the LLM loses it and starts mangling all the previous work it did. That way you don't have to start all over from the last good checkpoint or commit.