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What caused the switch? Also, are you still trying to use Claude models in OpenCode?

Sorry, I missed part of your question:

What caused the switch was that we're building AI solutions for sometimes price-conscious customers, so I was already familiar with the pattern of "Use a superior model for setting a standard, then fine-tuning a cheaper one to do that same work".

So I brought that into my own workflows (kind of) by using Opus 4.6 to do detailed planning and one 'exemplar' execution (with 'over documentation' of the choices), then after that, use Opus 4.6 only for planning, then "throw a load of MiniMax M2.5s at the problem".

They tend to do 90% of the job well, then I sometimes do a final pass with Opus 4.6 again to mop up any issues, this saves me a lot of tokens/money.

This pattern wasn't possible with Claude Code, thus my move to Open Code.


You can access anthropic models with subscription pricing via a copilot license.

Pretty sure that's against TOS.

Edit: it's not. https://github.blog/changelog/2026-01-16-github-copilot-now-...

They must be eating insane amounts of $$$ for this. I wouldn't expect it to last


No, Claude on GitHub Copilot is billed at 3X the usage rate of the other models e.g. GPT-5.4 and you get an extremely truncated context window.

See https://models.dev for a comparison against the normal "vanilla" API.


Yes I regularly plan in Opus 4.6 and execute in “lesser” models ie MiniMax

The only similarity is that they both say "you’re absolutely right" when you point out their obvious mistakes

_hyper-competent collaborator who may completely make things up occasionally and will sometimes give different answers to the same question*_

So, indistinguishable from a human then

No. A competent human doesn't make things up, he admits ignorance. He also only very rarely changes answers he previously gave.

I’ve used M2.5 in OpenCode using their Zen inference. I found it to be decent. Did not really seem comparable to Opus 4.5 for "quality" output. As in, I often tweaked the output more when using M2.5.

I think the best thing was the speed. If it is going to be wrong, I would prefer it to be wrong quickly.


They have the details up on their site now: https://www.minimax.io/models/text/m27

There is a blog post now: https://mistral.ai/news/leanstral

> Leanstral > Our first open-source code agent designed for Lean 4, built for formal proof engineering in realistic repositories. 119B parameters with 6.5B active.

Mentioned in the 2.5.0 release of the Vibe CLI tool: https://github.com/mistralai/mistral-vibe/releases/tag/v2.5.... A HuggingFace page is linked for the weights but it returns a 404: https://huggingface.co/mistralai/Leanstral-120B-A6B-2603


Mentioned in the 2.5.0 release of the Vibe CLI tool: https://github.com/mistralai/mistral-vibe/releases/tag/v2.5....

This article sure uses a lot of em dashes. I see 9 in the article body.


> You're absolutely right

Bot detected


But crucially they used "--" and not "—" which means they're safe. Unless it's learning. I may still be peeved that my beloved em dash has been tainted. :(


Of course they'll learn. LLM bots have been spotted on HN using that hipster all lower case style of writing.

i can write like this if i want. or if i were a clever ai bot.


No need to be clever, just add the instruction to write in that way.


I think that's the joke.


I found the key insight -- when a human tries to sound like an LLM, that's perceived by other humans as humor.


The issue is clear


Not sarcasm. Not cynism. Just pure humor.


Oh my God, this is peak GPT.


Never admit when someone else is right. They'll forget they were right and begin to think they won a fight.

Or something. You're right.


The blog post includes a video showcasing the improvements. Looks really impressive: https://blog.google/innovation-and-ai/models-and-research/ge...


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