This is just getting to be a bit much, seems like they are trying to cover for the fact that they haven't actually done much. All these models feel like they took the exact same base model, tweaked a few things and released it as an entirely new model rather than updating the existing ones. In fact based on some of the other comments here it sounds like these are just updates to their existing model, but they release them as new models to create more media buzz.

Incredible how resilient Claude models have been for best-in-coding class.

[1] But by only about 1%, and inclusive of Claude's "custom scaffold" augmentation (which in practice I assume almost no one uses?). The new OpenAI models might still be effectively best in class now (or likely beating Claude with similar augmentation?).

Using o4-mini-high, it actually did produce a working implementation after a bit of prompting. So yeah, today, this test passed which is cool.

> Codex CLI is fully open-source at https://github.com/openai/codex today.

Lets see what the pricing looks like.

Didn’t the pivot to RL from pretraining happen because the scaling “law” didn’t deliver the expected gains? (Or at least because O(log) increases in model performance became unreasonably costly?) I see they’ve finally resigned themselves to calling these trends, not laws, but trends are often fleeting. Why should we expect this one to hold for much longer?

Claude 3.7/3.5 are the only models that seem to be able to handle "pure agent" usecases well (agent in a loop, not in an agentic workflow scaffold[0]).

OpenAI has made a bet on reasoning models as the core to a purely agentic loop, but it hasn't worked particularly well yet (in my own tests, though folks have hacked a Claude Code workaround[1]).

o3-mini has been better at some technical problems than 3.7/3.5 (particularly refactoring, in my experience), but still struggles with long chains of tool calling.

My hunch is that these models were tuned _with_ OpenAI Codex[2], which is presumably what Anthropic was doing internally with Claude Code on 3.5/3.7

tl;dr - GPT-3 launched with completions (predict the next token), then OpenAI fine-tuned that model on "chat completions" which then led GPT-3.5/GPT-4, and ultimately the success of ChatGPT. This new agent paradigm, requires fine-tuning on the LLM interacting with itself (thinking) and with the outside world (tools), sans any human input.

[0]https://www.anthropic.com/engineering/building-effective-age...

[1]https://github.com/1rgs/claude-code-proxy

[2]https://openai.com/index/openai-codex/

• o3 Pricing:

  - Input: $10.00  

  - Cached Input: $2.50  

  - Output: $40.00

  - Input: $15.00  

  - Cached Input: $7.50  

  - Output: $60.00

To the extent that reasoning is noisy and models can go astray during it, this helps inject truth back into the reasoning loop.

Is there some well known equivalent to Moores Law for token use? We're headed in a direction where LLM control loops can run 24/7 generating tokens to reason about live sensor data, and calling tools to act on it.

{Size}-{Quarter/Year}-{Speed/Accuracy}-{Specialty}

Where:

* Size is XS/S/M/L/XL/XXL to indicate overall capability level

* Quarter/Year like Q2-25

* Speed/Accuracy indicated as Fast/Balanced/Precise

* Optional specialty tag like Code/Vision/Science/etc

Example model names:

* L-Q2-25-Fast-Code (Large model from Q2 2025, optimized for speed, specializes in coding)

* M-Q4-24-Balanced (Medium model from Q4 2024, balanced speed/accuracy)

They even provide a description in the UI of each before you select it, and it defaults to a model for you.

If you just want an answer of what you should use and can't be bothered to research them, just use o3(4)-mini and call it a day.

GPT-4o mini: The new moon in August 2025 will occur on August 12.

Llama 3.3 70B: The new moon in August 2025 is expected to occur on August 16, 2025.

Claude 3 Haiku: The new moon in August 2025 will occur on August 23, 2025.

o3-mini: Based on astronomical calculations, the new moon in August 2025 is expected to occur on August 7, 2025 (UTC). [...]

Mistral Small 3: To determine the date of the new moon in August 2025, we can use astronomical data or a reliable astronomical calendar. As of my last update in October 2023, I don't have real-time data access, but I can guide you on how to find this information. [...]

I got different answers, mostly wrong. My calendars (both paper and app versions) show me 23. august as the date.

And btw, when I asked those AIs which entries in a robots.text file would block most Chinese search engines, one of them (Claude) told me that it can't tell because that might be discriminatory: "I apologize, but I do not feel comfortable providing recommendations about how to block specific search engines in a robots.txt file. That could be seen as attempting to circumvent or manipulate search engine policies, which goes against my principles."

Is there a reputable, non-blogspam site that offers a 'cheat sheet' of sorts for what models to use, in particular for development? Not just openAI, but across the main cloud offerings and feasible local models?

I know there are the benchmarks, and directories like huggingface, and you can get a 'feel' for things by scanning threads here or other forums.

I'm thinking more of something that provides use-case tailored "top 3" choices by collecting and summarizing different data points. For example:

* agent & tool based dev (cloud) - [top 3 models] * agent & tool based dev (local) - m1, m2, m,3 * code review / high level analysis - ... * general tech questions - ... * technical writing (ADRs, needs assessments, etc) - ...

Part of the problem is how quickly the landscape changes everyday, and also just relying on benchmarks isn't enough: it ignores cost, and more importantly ignores actual user experience (which I realize is incredibly hard to aggregate & quantify).

Both seem to be better at prompt following and have more up to date knowledge.

But honestly, if o3 was only at the same level as o1, it'd still be an upgrade since it's cheaper. o1 is difficult to justify in the API due to cost.

So one failure that could be resolved with better integration on the back end and then an open problem with image generation in general.

I assume this announcement is all 256k, while the base model 4.1 just shot up this week to a million.

    system
      OpenAI rejected the request (request ID: req_06727eaf1c5d1e3f900760d10ca565a7). Please verify your settings and try again.

  ChatGPT Plus, Pro, and Team users will see o3, o4-mini, and o4-mini-high in the model selector starting today, replacing o1, o3‑mini, and o3‑mini‑high.

Good thing I stopped working a few hours ago

EDIT: Altman tweeted o3-pro is coming out in a few weeks, looks like that guy misspoke :(

Why are they doing this?

It has been getting better IMO.

    o1, o1-mini,
    o1-pro, o3,
    o4-mini, gpt-4,
    gpt-4o, gpt-4-turbo,
    gpt-4.5, gpt-4.1,
    gpt-4o-mini, gpt-4.1-mini,
    gpt-4.1-nano, gpt-3.5-turbo

During the live-stream the subtitles are shown line by line.

When subtitles are auto-generated, they pop up word by word, which I assume would need to happen during a real live stream.

Line-by-line subtitles are shown if the uploader provides captions by themselves for an existing video, the only way OpenAI could provide captions ahead of time, is if the "live-stream" isn't actually live.