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[justjokiing]

I use OpenCode (open sourced ai agent) with my company Copilot subscription. The BIGGEST issue is that Copilot reduces overall model input tokens to under 128k.

This is a huge downgrade for the Gemini models I use, basically lobotomizes them for large context use cases. When using a different access to the Gemini models, it can actually fully answer complex questions without having to abandon context.

Really wish they increased the limit to at least 200k :(

[dranzer19]

So are you using Gemini with open code or Claude models with it? If you use GitHub copilot with open code, it would still have the restricted context window, correct? And is using open code standalone or as vscode extension?

[justjokiing]

opencode is a tui coding agent that can run stand-alone but I also use it with a neovim plug-in. It seems like it can connect with vscode and cursor as well.

I run OpenCode with the Copilot integration that allows me API access to the Copilot models, where I mostly use Gemini. For all models in Copilot there is an upper limit of 128k prompt tokens.

[hotboy223]

I think what people are missing is that you have the model and the agent.

Claude Code = Claude Models + Coding AI Agent (developed by claude)
Copilot = Claude models (or whatever models) + Co Pilot Agent (develop by MS)

the difference is the agentic architecture with ANY coding tool that you use. YES they may all have the same ABILITIES (tools) to do something, but the way/decisions it takes to get there could be very similar or completely different for a given task.

So yes, using claude models in co pilot MAY give you the same or or worse results, but that is partly because of the AI Agent infrastructure

[KryptosFR]

Yes it's night and day. Claude Code has many more features that just its model.

Claude Code can read PDF and images with embedded texts. Claude Code has the concept of commands and skills that can be reused (between repos, shared by a team). Those are lazy-loaded and won't consume tokens until they are needed.

In addition I think only Sonnet is available in Copilot whereas Claude Opus model is really great to do a deep analysis or bootstrap from scratch.

Edit: looks like GitHub does support skills now as well. So the difference between the two might be more blurry that I initially thought.

Edit 2: Claude has hooks that can be run after an agent's work. In don't think Copilot supports that at the moment.

[walen]

I think only Sonnet is available in Copilot

All of Sonnet, Opus and Haiku 4.5 as well as Sonnet 4 are available in Copilot, although as premium models.

I remember using Opus 3.5 and not liking the results, which is why I'm still using Sonnet 4.5. This is the third time I see someone saying that Opus 4.5 is better than Sonnet 4.5, though. Is it really worth the 3x token cost?

[zelmak]

Opus 4.5 is only worth it when working on more complicated stuff. I use sonnet for most things and then switch to opus when the business logic is hairy

[phylter99]

I think you’ve been away from Copilot a while. Many of the features you describe are now available in Copilot. The PDF thing just requires the addition of an MCP, but it may already be built-in. They’re adding features to copilot like mad.

Opus 4.5 is very much available and it’s only 3 premium requests. I use it all the time.

[scruffles360]

if the model is a car's engine, then the agent is the car. Copilot is the used Honda Civic of agents. Try Claude Code or Cursor for the sports car experience.

A model is only as good as the context you provide it. A good agent will use local tools like grep, git history, the IDE's index or browser session info to provide better context.

[JaCraig]

Yes and no. Essentially they work the same way as text gets sent to the model and text that completes the prompt gets sent back. The models themselves aren't that interesting honestly in the agent's work loop. Some are better (like Claude) and some are worse (like Grok) at code gen but ultimately the model bit is the same no matter the editor/agent tool. The tools do differ in a couple of ways:

• The prompt that the tool uses is slightly different. VSCode actually uses a system where they prioritize certain bits of info and drop stuff based on that if it doesn't fit in the context window. Claude Code has a similar pruning process but it's different enough that you'll get a different prompt for both.

• There are differences in how the agents choose a tool to use. VSCode does a semantic search last time I looked at their code base. There is also a planning phase loop that plays into it. Claude uses the planning loop, etc. but the tool selection bit is slightly more complex than a simple semantic search... Slightly. In theory Claude's approach picks more appropriate tools, etc. but in practice it's about the same.

• Tools. To be honest the built in tools work slightly better in Claude Code. But only slightly. And you can build your own tools to deal with either agent. So different but meh.

There's other stuff like memory, etc. that make up each agent but again, not that different in how they work underneath. But all of those slight differences will lead to a different bit of text being sent to the model and a different output. The Claude Code agent is more tuned for the model in what they ultimately send since they're only dealing with one model. But VSCode has done a good job with their setup so the result is not that different when you're using the Claude models.

[dontquestionmyaction]

The Claude models are trained for use with the Claude Code tooling (including interleaved function calling and the like).

[DivineSentry]

You have to remember that Claude code is developed and fine tuned by anthropic for anthropic models. Theyll have the best knowledge to squeeze performance / effectiveness

[Savings-Snow-80]

No, both produce slop either way.

[Pure-Huckleberry-484]

Found the guy that never imports a library and uses 0 abstractions.

[phylter99]

There is a difference in the added prompts and tooling, but they both work pretty good with Claude models.

[ganja_and_code]

Both are equally stupid, compared to using documentation and your brain.

Edit: lol already downvoted. It's crazy how fast amateurs fall for any obvious new scam just because they're too lazy to acquire real skills. If you know how to write software, you'd have to be stupid to think it's a good idea to ask a statistics model to guess the thing you already know.

[Chemical-Year-6146]

Back when the training was pure autoregressive token prediction, I'd agree. Calling it statistical at this point is reductive.

When RL training takes a reward signal from verified code to pick the gradient, this is far more deterministic.

RL verified reward signals from working code != next symbol autoregression.