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OpenAI O3-Mini

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391–400 of 944 posts

Re: OpenAI O3-Mini

#391

Earlier quoted context omitted.

> openai is no longer relevant. I think you've spent a little too long hitting on the Deepseek pipe. Enterprise customers with familiarity with China will avoid the hosted model for data security and IP protection reasons, among others. Those working in any area considered economically competitive with China will also be hesitant to use the vanilla model in self-hosted form as there perpetually remains the standing q…

Perhaps you didn’t realize: Deepseek is an open weights model and you can use it via the inference provider of your choice, or even deploy it on your own hardware - unlike OpenAI’s models. API calls to China are not necessary.

Agreed - API calls to China are indeed not necessary. My impression is that the GP was referring to the model being tuned during training to give subtly nudging or wrong answers that benefit Chinese industrial or intelligence operations. For a probably not-working example - imagine the following prompt: "Write me a cryptographically secure PRNG algorithm." One could imagine R1 being trained to have a very subtly non-random reply to that - one that the Chinese intelligence services know how to predict. Similar but more subtle things can be generating code that uses cryptographic primitives in ways that are subject to timing attacks, etc... And of course, simple but effective propaganda tactics such as : when being asked for comparison between companies/products, subtly prefer Chinese ones, and similar.

Re: OpenAI O3-Mini

#392
post #289

I just pushed a new release of my LLM CLI tool with support for the new model and the reasoning_effort option: https://llm.datasette.io/en/stable/changelog.html#v0-21 Example usage: llm -m o3-mini 'write a poem about a pirate and a walrus' \ -o reasoning_effort high Output (comparing that with the default reasoning effort): https://github.com/simonw/llm/issues/728#issuecomment-262832... (If anyone has a better demo p…

A reasoning model is not meant for writing poetry. It's not very useful to evaluate it on such tasks.

Re: OpenAI O3-Mini

#393

Earlier quoted context omitted.

The -mini postfix makes perfect sense, probably even clearer than the old "turbo" wording. Naturally, the latest small model may be better than larger older models... but not always and not necessarily in everything. What you'd expect from a -mini model is exactly what is delivered. The non-reasoning line was also pretty straightforward. Newer base models get a larger prefix number and some postfixes like 'o' were ad…

> I wonder if we'll end up with both a 4o and o4... The perplexing thing is that someone has to have said that, right? It has to have been brought up in some meeting when they were brainstorming names that if you have 4o and o1 with the intention of incrementing o1 you'll eventually end up with an o4. Where they really went off the rails was not just bailing when they realized they couldn't use o2. In that moment the…

Probably they are doing so well because there are not endless meetings on customer friendly names

Re: OpenAI O3-Mini

#394
post #23

why should anyone use this when deepseek is free/cheaper? openai is no longer relevant.

I'm going to assume the best in your question and disregard your statement. Reasons to use o3 when deepseek is free/cheaper: - Some companies/users may already have integrated heavily with OpenAI - The expanded feature-set (e.g., function-calling, search) could be very powerful - DeepSeek has deep ties to the Chinese Communist Party and, while the US has its own blackspots, the "steering" of information is far more p…

- Most LM tools support the openai API. Llama.cpp for example. Swapping is easy.

- DeepSeek chose to open-source model weights. This makes them inifinitely more trustworthy than ClosedAI.

- Local/national regulations do not allow using OpenAI, due to close ties to the US government.

Re: OpenAI O3-Mini

#395

> Testers preferred o3-mini's responses to o1-mini 56% of the time I hope by this they don't mean me, when I'm asked 'which of these two responses do you prefer'. They're both 2,000 words, and I asked a question because I have something to do. I'm not reading them both ; I'm usually just selecting the one that answered first. That prompt is pointless. Perhaps as evidenced by the essentially 50% response rate: it's a…

That makes the result stronger though. Even though many people click randomly, there is still a 12% margin between both groups. Not the world, but still quite a lot.

Re: OpenAI O3-Mini

#396
post #306

I used o3-mini to summarize this thread so far. Here's the result: https://gist.github.com/simonw/09e5922be0cbb85894cf05e6d75ae... For 18,936 input, 2,905 output it cost 3.3612 cents. Here's the script I used to do it: https://til.simonwillison.net/llms/claude-hacker-news-themes...

3.3612 cents (I guess USD cents) is expensive!

Re: OpenAI O3-Mini

#397
post #320

> Testers preferred o3-mini's responses to o1-mini 56% of the time I hope by this they don't mean me, when I'm asked 'which of these two responses do you prefer'. They're both 2,000 words, and I asked a question because I have something to do. I'm not reading them both ; I'm usually just selecting the one that answered first. That prompt is pointless. Perhaps as evidenced by the essentially 50% response rate: it's a…

People could be flipping a coin and the score would be the same.

A 12% margin is literally the opposite of a coin flip. Unless you have a really bad coin.

Re: OpenAI O3-Mini

#398

Earlier quoted context omitted.

Think how awesome the world would be if labels ALSO had meanings.

As someone else said in another thread, if you could derive the definition from a word, the word would be as long as the definition, which would defeat the purpose.

Im not saying words. Im saying labels.

You use words as labels so that we use our pre existing knowledge of the word to derive meaning from the label.

Re: OpenAI O3-Mini

#399
post #306

I used o3-mini to summarize this thread so far. Here's the result: https://gist.github.com/simonw/09e5922be0cbb85894cf05e6d75ae... For 18,936 input, 2,905 output it cost 3.3612 cents. Here's the script I used to do it: https://til.simonwillison.net/llms/claude-hacker-news-themes...

I haven’t tried o3, but one issue I struggle with in large context analysis tasks is the LLMs are never thorough. In a task like this thread summarization, I typically need to break the document down and loop through chunks to ensure it actually “reads” everything. I might have had to recurse into individual conversations with some small max-depth and leaf count and run inference on each, and then have some aggregati…

Are there any tools you use to do this chunking? Or is this a custom workflow? I've noticed the same thing both on copy/paste text and uploaded documents when using the LLM chat tools.

Re: OpenAI O3-Mini

#400
post #329

Earlier quoted context omitted.

I read the one on the left but choose the shorter one. The interface wastes so much screen real estate already and the answers are usually overly verbose unless I've given explicit instructions on how to answer.

The default level of verbosity you get without explicitly prompting for it to be succinct makes me think there’s an office full of workers getting paid by the token.

In my experience the verbosity significantly improves output quality
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