How popular is Grok compared to other companies models for SWE tasks? I almost never hear it talked about against OpenAI's or Anthropic's products

Is there a reason the AI companies usually announce new products so close to each other. Like not just the same day but literally hours apart. GPT Live then an hour later Grok 4.5. As if they try to one up. I expect something new from Anhtropic as well today.

It seems to be extremely economical - 4x better reasoning efficiency compared to Opus while being priced at $2/$6. For comparison, GPT 5.4 is $2.5/$15, GPT 5.5/5.6 are $5/$30, Opus 4.8 is $5/$25, Fable is $10/$50.

And by benchmarks (unless they gamed them), seems to be at around Opus 4.7 level, which is what Elon mentioned in https://x.com/elonmusk/status/2074911038286295049.

I guess the Cursor data was very useful.

Of the 3 models I tried, Grok did the best at making an iOS app I wanted for personal use (a bike computer with specific qualities). (Claude just gave up and did an HTML/CSS implementation but I insisted on native SwiftUI+Metal.) Grok definitely fumbles sometimes, but I have been surprised what it CAN intuit versus me having to micromanage it.

(I am not an iOS developer, so getting something specific that I needed in a few hours/days was really helpful instead of spending months/years learning the language, APIs, etc.) (I am absolutely not "vibe-coding" Caddy btw, just tinkering with it for personal projects.)

Announcement from Cursor, whose team also trained the model: https://cursor.com/blog/grok-4-5.

Notably:

> Grok 4.5 and Composer 2.5 are two different model weight classes, and we're excited to support both sizes and weights. Composer 2.5 will remain offered, and we will release new models of this size going forward.

Not available for Europeans yet. :(

Its remarkable how Anthropic is able to maintain their edge against all competition. Anyone have any idea what the secret sauce is that has Anthropic at the top of all leaderboards for the past few years?

Isn't this the same Twitter company that was supposed to go bankrupt a few years ago? Now it is somehow part of a Space company that has an AI division inside of it?

I think we are going to be waiting a long time for Twitter / X to go bankrupt as it was (erroneously) predicted a long time ago.

The solar system diagram doesn't work for me. When I click on the planets, it will center on them. When I click on the sun, nothing happens. When I click on a planet next, it goes to the sun.

Every time I get excited about Grok’s performance on benchmarks and demo videos, I test it myself and end up disappointed.

I'll give this one a try with a grain of salt and lowering my levels of expectations

So basically since US stopped OpenAI and Anthropic for 4 weeks, it allowed all other AI Labs to almost catch up.

GLM 5.2 caught up, Cognition RL'ed Kimi 2.7, Grok 4.5 is out, DeepSeek v4 GA is out in a few days...

What is the moat? and why should we pay for the expensive tokens today instead of just waiting a few months/weeks and getting AI for significantly cheaper?

I must say, I feel like companies spending Millions on Anthropic tokens are just negative capex'ing and wasting money, even OpenAI is barely ok pricing...

Another subpar model. Why don't they go open weight?

Interesting. I experimented with Grok 4 for openclaw when they made clear they wanted to bring claw users in the fold. It was (as expected) more verbally fluid than 5.5, but had real trouble with agentic tool calling - the model felt like it hadn't been trained to think of tool calling as one of its primary modalities. I'll give this a try, the speed and the benchmarks look good. In my experience, Grok slightly punches above its weight in language fluidity, and seems to not benchmaxx on coding, so this is an encouraging release.

With each release from the the other major labs, it becomes harder for Google to tell a compelling story about Gemini 3.5.

still waiting for a proper gui for grok build

terminal is nice but codex desktop app is very useful

(from Cursor's blog)

> Training included trillions of tokens of Cursor data which capture a wide-range of user interactions with codebases and software tools. This dataset lets the model learn both from existing software as well as developer-agent interactions, capturing how developers work and how agents interact with their environments.

This is what the big money was for. Cursor is the first big player that had real-world data from real-world projects, before cc / codex were a thing.

> We used reinforcement learning on difficult problems in realistic environments spanning both software engineering and broader knowledge work. These environments teach the model to investigate problems, use tools, recover from mistakes, and verify results.

> Many of these problems had to be designed to be difficult enough that even frontier models fail at them. As models improve, existing tasks stop teaching them anything new, and problems that once required extensive reasoning become routine.

> We developed a distributed agent system to construct these environments at scale. Engineers specify a problem and how a solution is verified, and large groups of agents construct, test, and refine each environment.

This is where scale comes in. You use the previous gen model to prepare datasets for the next model iteration. The better the models, the better the data, the better the next models. (they also have a comparison with their composer2.5 training run, for people still thinking chinese models are "close to SotA"...)

Reports of xAIs demise (after giving a lot of compute to Anthropic) were slightly exaggerated, it seems.

> Grok 4.5 was trained across tens of thousands of NVIDIA GB300 GPUs

Props to them for including three benchmarks that actually seem to say something, instead of focusing on totally gamed benchmarks like regular SWE-Bench. That could mean this model is actually pretty close to the SOTA as the benchmarks indicate.

Most labs - including OpenAI and Anthropic, but also Google and Chinese labs - highlight their scores in benchmarks that have fixed, widely available answers. Those answers end up in the training data and so models can just regurgitate training data instead of actually doing the benchmark. As a result, most benchmarks often quoted are essentially meaningless for gauging model performance.

Terminal-Bench still publishes answers, but neither DeepSWE and SWE-Bench Pro do. Especially for DeepSWE it's been difficult for models to fake good results so far. SWE-Bench Pro does have weird outliers like good performance for e.g. the atrocious Muse Spark, but it also doesn't provide answers for the training data.

So either they're good, or they found a way to game DeepSWE. Given that the Cursor team previously published the well-received Composer 2.5 a good score here doesn't come out of nowhere, so this might hold up. Cursor has enormous amounts of training data to train good coding models with.

First impressions:

- Very fast, easily beats GPT 5.5/Opus 4.8/GLM 5.2 because of higher t/s (around 90?) and very high token efficiency

- Very good price, no contest vs GPT and Opus which are very overpriced if you pay API costs, and probably cheaper than GLM 5.2 when you take into account the token efficiency.

- Will take quite a while to get a feel for how smart it is, but it's definitely good, I'd say in the same tier as opus, occupying the lower end of that tier together with GLM 5.2.

Thanks for including a section on Token Efficiency (https://x.ai/news/grok-4-5#faster-than-flash-models), hope to see this more prominently in all model releases.

Can someone breakdown to me how this makes any sort of economical sense? Spending billions and billions to have the 3rd best model while even the number 1 and 2 players already seem to struggle making a profit. What am I missing here? Not trying to go full Ed Zitron but this doesn’t make sense to me.