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200k context window $1.1/m for input $4.4/m for output I assume thinking medium and hard would consume more tokens. I feel the timing is bad for this release especially when deepseek R1 is still peaking. People will compare and might get disappointed with this model.
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The model looks quite a bit better in the benchmarks so unless they overfit the model on them it would probably perform better than deepseek.
My vibe question checking suggests otherwise. Even o3-mini-high is not as good as r1, even though it's faster than r1. Considering o3-mini is more expensive per token. It's not clear o3-mini-high is cheaper than r1 either even r1 probably consumes more token per answer.
why should anyone use this when deepseek is free/cheaper? openai is no longer relevant.
> 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…
Deepseek released the weights of their top language model. I can host and run it myself. Does OpenAI do the same?
Thanks, but no thanks! I won't be using ClosedAI.
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At the least they should care more about UX. I have no idea how to restore the sidebar on chatgpt on desktop lol
Click the 'open sidebar' icon in the top left corner of the screen.
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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…
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…
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You cannot compare GPT-4o and o*(-mini) because GPT-4o is not a reasoning model.
Sure you can. "Reasoning" is ultimately an implementation detail, and the only thing that matters for capabilities is results, not process.
O3-mini solved this prompt. DeepSeek R1 had a mental breakdown. The prompt: “Bob is facing forward. To his left is Ann, to his right is Cathy. Ann and Cathy are facing backwards. Who is on Ann’s left?”
R1 or R1-Distill? They are not the same thing. I think DeepSeek made a mistake releasing them at the same time and calling them all R1. Full R1 solves this prompt easily for me.