I don’t exactly see orgs lining up to switch (and train) their employees between claude desktop and codex and whatever copilot is doing. There’s probably some inertia to those harnesses/integrations on top of the llms themselves.

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Escapade5160 a day ago | parent | next [-]

Most large orgs do not need to train end users. They just need to add glm-5.2 to their router and their in house harness will pick it up. Then slowly limit usage on anthropic models and people will swap willingly. It's a simple /model command in every harness.

	▲	torginus a day ago \| parent [-]
		Yeah, most big orgs are pushing the idea of 'whitelabel' LLMs. Even if they choose to hang on to Claude Opus, they won't name it, they'll just call it the 'extra mode' and 'auto mode' will eventually switch to a local LLM in their harness.

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mgambati a day ago | parent | prev | next [-]

The inertia is legal and financial. People are paying Anthropic through AWS accounts because the simple reason of not dealing making new contract and legal agreements is enough of reason of the inertia.

But, eventually, I’m quite sure that AWS will also provide open models with those contracts without any inertia. Copilot is already offering Kimi.

My company has a deal with Devin and they provide new models all the time, and open models are becoming the most used ones by our internal metrics, especially because the company is very worried about cost.

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zmgsabst a day ago | parent | next [-]

AWS already supports Llama and GLM in its Bedrock service for hosted models.

They’re much cheaper to run, eg, Llama 3.3 Instruct 70B is 5-10x cheaper than Sonnet 5.

https://aws.amazon.com/bedrock/pricing/

Say you have 20% of usecases that require the more expensive model — but in 80% you could just use Llama instead of Sonnet (eg, for basic queries of a document). That saves 80% of that 80%, or 65% of your total bill!

That is the kind of “swap” that’s likely to occur in automated tooling as pricing pressure kicks in — “can you save 65% on our AI bill by switching Bedrock over in 80% of uses?”

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regularfry a day ago | parent [-]

Bedrock is really out of date with the models it offers, to the extent that I'm not sure they even have plans to update what's on there now they have the deal with Anthropic. They're still offering Qwen 3, not even 3.5 and certainly not 3.6. GLM 5 is the newest z.AI model they have, when it's 5.2 that would be the one to worry Sonnet.

There are some ok models on there (Qwen 3 Coder Next is usable and fast, for instance) but the lack of updates in a fast-moving field makes it something I don't want to recommend to my org.

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whattheheckheck 17 hours ago | parent [-]

Because these models are not going to stand up legally

	▲	regularfry 14 hours ago \| parent [-]
		That's a stretch but it doesn't change the outcome.

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skeptic_ai a day ago | parent | prev [-]

Also they pay for legal liability of code produced

	▲	Yizahi 13 hours ago \| parent [-]
		Maybe for a fantasy of legal liability of output produced. I haven't heard of any LLM corpo being held liable for any output they generate. Even NYT lawsuit is going nowhere for 3 years in courts already, despite being the most grounded.

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bayarearefugee a day ago | parent | prev | next [-]

What "training" do you have to do to get a professional developer to switch LLMs or harnesses? Its literally just download the other one, point it to your code base and start typing into that text box instead of the other one.

	▲	iknowstuff 12 hours ago \| parent [-]
		devs are the easiest, yeah.

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saghm a day ago | parent | prev | next [-]

What would "training" even entail for that? As far as I can tell, using these tools directly is basically identical in terms of what you need to know. If you happen to have a bunch of custom configurations, maybe you need to invest some time into porting them, but it's not clear to me why you think that anyone would need to be trained if they spent months using one tool and then suddenly had t switch to the other.

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peab a day ago | parent | prev [-]

Enterprises switched from openai to anthropic this year - anthropic overtook openai for the first time. I don't see why they wouldn't switch again.

There's barely any moat. All the data is with connectors, memory is near useless