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.<p>And by benchmarks (unless they gamed them), seems to be at around Opus 4.7 level, which is what Elon mentioned in <a href="https://x.com/elonmusk/status/2074911038286295049" rel="nofollow">https://x.com/elonmusk/status/2074911038286295049</a>.<p>I guess the Cursor data was very useful.
The $2/6 pricing seems to only apply for context under 200K.<p>Above that (max context is 500K) pricing doubles to $4/12.<p><a href="https://docs.x.ai/developers/models/grok-4.5" rel="nofollow">https://docs.x.ai/developers/models/grok-4.5</a>
Womp. Didn't see this anywhere else.<p>No longer feels as inexpensive. Will likely just include this in the rolodex of <200k context tasks, like being one of my review agents.
That's very notable and left out of the announcement.
I have a theory that xAI has one of the largest clusters but with far less traffic + tokens to process bc its less popular than its competition, and xAI can pass the savings on to the end user.
Why would having more costs and less income allow them to pass savings on to the end user?
They already invested in the massive datacentres of GPUs sitting idle. They have fewer users so they can deliver more inference per user - more thinking, larger models.
More like they have a less focus on margins and more on cost recovery.
SpaceX, like Tesla, seems to have the same "portrayals over profits" mindset investors. So it doesn't even really matter whether or not xAI is making any money.
Now if they could have an "equivalent" to Claude's $100 plan with similar compute limits. I have the $40 a month version of Grok and I get a max of like 8 hours of "non-stop" Grok Build coding, per month.
The model is available through Cursor which has $20, $60 and $200 plans. I assume the $60 version might work better for you?
Grok Build sucks compare to composer 2.5.
Just use compose 2.5 and you'll have basically unlimited usage on the 40$ plan.
Every time I use Composer 2.5 I have to spend a bunch of time cleaning up its mistakes. It is unusable compared to GPT 5.4 or 5.5.<p>My time is more valuable that I will use a model that doesn’t f** up my code base.
It is hard to evaluate the model performance of Composer 2.5 when Cursor's harness is so awful compared to the others on the market.
Suppose eventually that gravy train will disappear, might as well use it then.
Composer 2.5 is so underrated IMO. I built a really feature rich application, insanely complicated, close to 200k LOC since it came out and for the most part it ran like a champ. Only used CLaude a couple times to get it unstuck. 8 hours a day and I'm paying about 30 a month.
> I built a really feature rich application, insanely complicated, close to 200k LOC<p>If you listed it, how many features/LOC or vice-versa? Really hard to know if 200K LOC is good or bad, at the surface it sounds like too much, but I don't know what the application was either.
How does it compare to Chinese APIs? It doesn't seem like xAI is meaningfully more competent or any single bit more honest than Chinese labs anyway, so you might as well send tasks straight to China unless theirs is substantially cheaper.
Around Opus 4.7 level would be the same as Sonnet 5 while being cheaper overall.<p>I wonder how good their subscription discount is on both their subscription types.
Sonnet 5 is a huge token hog, though, it uses far more reasoning tokens than Opus models while being priced at $2/$10 with promo, and $3/$15 (usual Sonnet price) afterwards.
I'll probably get hate for it, but I was not impressed by Fable, I felt like it was just Opus with more tokens for thinking. I feel like the second I turned on Fable I drained my usage more quickly, despite them billing it as though it were Opus level of usage. The value is just not there for me. I wish they could make Haiku remain low-cost and drastically more capable to the point you could use only Haiku.
Fable needs more... ambitious tasks than Opus to tell the difference and let me tell you the difference <i>is</i> there.<p>Simple tasks are simply saturated just like simple benchmarks. There's a level of intelligence where you simply don't need more for some things.
I felt the same tbh; I notice more the regressions in the weeks before a new release than any potential improvement the new model might have actually brought.<p>It may also depend on the workload. At work everything is very domain specific with barely (if any) public training data; both need thorough review and careful hand holding, meanwhile at home Fable is scared of libtorch and falls back to Opus even if it's not touching the ML parts.
I'm not sure if you are aware, but you have to approach prompting Fable slightly differently from a model like Opus.<p>It's important to include the reason aka the why of your task [1] in your prompt. You'll get more mileage if you verbalize your thought process when prompting Fable. Anthropic say you should think of Fable as a "thought partner".<p>1: <a href="https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/prompting-claude-fable-5#give-the-reason-not-only-the-request" rel="nofollow">https://platform.claude.com/docs/en/build-with-claude/prompt...</a><p>2: You might find some of the example prompts listed here useful <a href="https://x.com/trq212/status/2073100352921215386" rel="nofollow">https://x.com/trq212/status/2073100352921215386</a>
Did you explicitly tell it to use Sonnet or Opus subagents and stick at or below high effort? Asking because such practices make a huge difference in the quality of output and the amount of tokens burned. I used one of my accounts to explore ultramax and it was just a token hog that might be worse than Opus.
I had it on whatever the recommended settings was, but maybe I should have told it to use Sonnet for most subtasks.<p>Even so, I'm just not that impressed, I felt like I got more done by just using Opus.
The comparison may be better against GPT 5.6 Terra (instead of Sol), which is $2.5/$15.
Annoying they didn't show benchmarks for several effort modes, since it seems like it might close the gap with Opus 4.8 by cranking tokens up?<p>Noam Brown (OpenAI) "Implications of Large-Scale Test-Time Compute" <a href="https://xcancel.com/i/article/2064210146558136827" rel="nofollow">https://xcancel.com/i/article/2064210146558136827</a>
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.
They have the same dreams as their competitors - finding a breakthrough that gives them an edge over the others and makes them dominant. And also, having the word 'AI' anywhere near your company makes all the right numbers go up, so having an in-house AI division that Musk can bundle with the other companies to pump their valuations with is very helpful to him, even if the product itself loses some money.
You could be typing the same about Google or a number of the other labs right now.<p>A diverse market full of choices keeps it from becoming the browser wars all over again.
Google is playing a different game. I don't really know what game they're playing, but they're not trying to beat Claude Code. They have coding capabilities and Antigravity, but I'd be surprised if it's much more than an afterthought. They're focusing on efficiency, models at the edge, human interaction, image and video, etc. in ways Anthropic, in particular, is not.<p>Google wants its AI to be pervasive in everyone's daily life. Merely being the best at coding is not how you get there.<p>I am more bullish on Google in AI than most folks, I think, as they have been focused on efficiency in a way most US vendors have not. They've published a ton of papers on ways to make LLMs more efficient and capable on smaller devices..
Google wants to own the on-device market for AI, and I don't see many credible competitors in that space.
Google at least is serving AI results on SRPs billions of times a day, and has pre-existing expertise in data center buildouts and custom silicon.<p>They have one of the more compelling cases for rolling their own.
Google is using AI at such scale internally they don't need external customers to recoup their investment.
> Google is using AI at such scale internally they don't need external customers to recoup their investment.<p>That's assuming their flagship product remains relevant in an AI-powered world.<p>Which brings to mind: most of the big shops product (chatgpt, claude, grok, etc...) ALL rely on search, and NONE of them actually have a running search stack.<p>Which means, they must all be calling Google, no?<p>How does Google make money from that?
> Which brings to mind: most of the big shops product (chatgpt, claude, grok, etc...) ALL rely on search, and NONE of them actually have a running search stack.<p>Don't they? Based on traffic to some websites I run the big AI labs are very actively doing a lot of crawling.
The funny thing about Google is that Google Search is happy to serve LLM labs search results if it drives their metrics up. Just like Google Cloud is happy to sell off compute to OAI an Anthropic to drive up their metrics.<p>Google also owns 15% of Anthropic and Hassabis, the leader of Deepmind, also is an early angel investor in Anthropic.<p>When you really break it down, it's not totally clear that Google would even care that much about being <i>the</i> SOTA LLM.
Google's ad revenue has done really well so far in the LLM era, and wasup 12% year over year in 2025, and forecasted to do the same next year.<p>And that changes, then that's all the more reason for them to be investing in AI.
> Which means, they must all be calling Google, no?<p>Incorrect. Alternate search providers exist, such as Bing (used by DuckDuckGo, for example) and Brave.
How is this any different than the browser wars? We use to have a diverse market full of choices, and now we have Chromium (almost all market share) and Firefox/Safari on the edges.
Google invented the transformer architecture. You really <i>can't</i> say the same about them.
> A diverse market full of choices keeps it from becoming the browser wars all over again.<p>This is a great analogy but I worry you might be implying something I don't agree with but you didn't explicitly say what I'm worried about, so let me call it out:<p>Microsoft played a dirty game with I.E, but they are in the dirty game business. It wasn't only I.E, it was their OS, Office suite and everything else they do business in.<p>Google Chrome took advantage of that dirty game and now you have the Chromium engine that powers a lot of browserlike frameworks.<p>No one born in the LLM age even knows what I.E means or stands for, as it should be - a horribly designed, poorly working product foisted upon users via the Windows distribution system - a dishonorable product from an ethically corrupt company forever lost in history, right alongside Clippy and DCOM.<p>OTOH, I am glad that Microsoft played a dirty game with I.E and didn't just stop playing dirty there - they jacked up the price of Windows if an OEM even dared to bundle in Netscape Navigator instead - who knows, if they hadn't done that, there wouldn't have been a Google or Apple. We would all be using Windows and Windows Search and Windows Phone.<p>And without Google, we might not have had the modern LLM as we know it. We would have had some trashy Windows Autocomplete Copilot Clippy. Ugh!
The product is the stock.<p>It is very valuable when you have various bundles of services, such as satellites, AI, and so on, to keep pace with the majors so that you keep pace with their valuation.<p>These stacking valuations are not additive, they're multiplicative because you additionally market investors to the synergy between them.<p>Having the third best model statistically is extremely useful in this context.
The weaknesses can be multiplicative as well. One division bleeding capex can drag down all the rest, no matter how well they might be doing. And the P/E ratio on all of them is riding unrealistic expectations, which can actually be fine for a long time but forces growth even in areas where it doesn't make sense. (Maybe that's where the "let's build data centers in a high radiation hard vacuum!" nonsense comes in; you just need a story of how the P/E ratio is <i>possible</i> to justify in the future? No need to argue over likelihood, just have a tale to tell?)
I know that SpaceX have tremendous potential, the problem is that we account future potential that maybe not happening in 20 - 50 years
> future potential<p>Starlink doesn't qualify? Because that's a practically unbelievable track record. It's easy to say it's obvious, but it was only obvious in hindsight (or perhaps to Elon, but I think the reason that it was successful was actually more about him just being relentless)<p>I'm not an Elon acolyte, but as with his other enterprises (SpaceX, Tesla), he succeeded where others (Irridium etc) repeatedly failed.<p>It's really hard to argue that he got lucky when he keeps pulling these really extremely high capex and hard-tech <i>and</i> business successes off so cleanly, especially when you see the entrenched opposition (govt, politics, competitors) that's been arrayed against him.
My guess is that the use here is similar to the reason AWS started as Amazon selling their excess capacity.<p>Between Tesla, SpaceX, X, Boring Co and Neuralink they probably want the capability internally for a lot of different applications.<p>If the whole data centers in space thing works out AND people keep protesting/blocking data center build outs on land SpaceX will eventually dominate the entire AI industry just based on escaping scarcity.
Its less about the model; elon is trying to make SpaceXAI a hyper scaler that also happens to have a good model. Grok is just the cherry on top of a powerful AI cluster that can also rent compute to its competitors, like aws.
Commoditize your opponents USP then eat up their engineering talent / silicon / real estate when they fail, perhaps?<p>I’ll be the first to admit it seems ambitious / implausible to try to (1) undercut the megalabs (2) move everyone’s focus back to tweets and then (3) profit.<p>A bit like handing out free horses to undercut Standard Oil so that you can go back to reaping the profits of your wheel tapping business.
With that frame of mind, nothing would be done. Why make another search service if Altavista and Lycos already do it?
Likely doesn’t make sense, at least not immediate/mid term. They don’t have to aim for number one though, just for enough cash flow and growth.
The only thing I can possibly think of is that they could use it internally at possibly a lower cost and offer it to people who have a Tesla cheaply. Owning Cursor might help for integration or data collection.
Grok runs tools stupid fast, just about as fast as Antigravity, running Gemini 3.5 Flash.
inference is profitable, these companies are in the red because they're paying a premium to get the compute now versus later (because compute is the only moat when open models are catching up)<p>we're literally looking at insane margins over compute, as energy gets cheaper, margins get wider - china focusing on cheap solar is probably going to be a key reason why their AI is so much cheaper
All they have to do to differentiate is differentiate the shape of worldview through RLAIF/RHLF and system prompts.
Grok is the #1 uncensored easily-available model, and it's also tightly integrated with Twitter.
I don't remember online discourses on filter avoidance for Grok to be any different from typical ones, except that it allegedly have tendency to take porn-biased interpretations of prompts, I think the "uncensored" pitch they had for a while was pure marketing in the end.
Is uncensored a selling point? What do people use uncensored Grok for (like, real use cases) that they can't or won't use other LLMs for? Literally the only thing I can think of is generating bad porn of unconsenting people.
I don't really have a use for a model that thinks "how many people are in this photo?" is a political question.
I mean absolutely read any thread about Fabel and it's fill with people complaining about how it instantly downgrades or refuses if anything has CVE in the name.<p>Other then that there is the whole alignment issue. Models that are 'nerfed' in just about any manner tend to exhibit reduced performance is seemingly unrelated areas.<p>That said Grok doesn't appear to be close enough to the frontier for that to matter. Maybe if they catch up it will.
Uncensored?
Great if you want to make virtual child porn, I guess.
SpaceX offers free AI usage to users, along with using AI to power their products so it is effective for them to avoid overpriced API pricing. The models can be designed specifically for their own data centers.
Surely grok has a built-in market with too-online, retired boomers. It's free real estate.
Elon Musk doesn't do normal finance. Trying to understand it will melt your brain.
> this doesn’t make sense to me<p>My hypothesis is that all the top providers realize that, lacking vendor lock in, all SOTA models in a year or so's time will be similar in capability. Also, open weights models are continuing to catch up in a year's time, sometimes less.<p>So they are trying to lure you in with differentiating, superior capabilities into their proprietary, non-open, non-standard agent harness.<p>It's the Hotel California playbook: These amazing capabilities are to attract you like moths to a flame and keep you warm and alive around the flame but waterboard and shock you if you attempt to move away from it. Like AWS Egress charges.
It’s Elon Musk. You try explaining it
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It sounds like they are building a honeypot for Russia, given Musk's open admiration for Putin.<p>No one sane would use this platform.
3rd best chat model? 5th or 6th maybe...<p>GPT<p>Qwen<p>Gemimi<p>MiniMax<p>Claude<p>Ollama<p>GLM<p>Kimi<p>DeepSeek
It's simple. Elon's top priority now is "killing the woke mind virus" at any cost, and his Nazi AI is a key tool for that. As long as twitter users take Grok at face value, and spread its talking points all over, it's worth it to him. It doesn't matter if it doesn't make economical sense, it only matters that Elon Musk personally wants to keep it going.
They want your code to be facist too.
I had to scroll down so far to see someone who speaks my language. Thank you. If Grok was the last model on the planet, I would not use it. For the very reason mentioned above. And no, none of the other tech CEOs are <i>that</i> comically evil that they’d take it upon themselves to cut aid from the world’s most vulnerable children while also being the world’s richest man. The optics of that alone… Never letting it go.
People are saying, "There are only a couple of frontier labs. This is a really hard problem and not many people can do it."<p>Elon's reaction to these kinds of statements is oddly predictable.
Frontier is one thing, but low-cost really good models are another. All the chatbots and day-to-day corporate bots are likely to use models that offer the best performance at the lowest cost. I think Grok has an angle here if they can build customer trust.
Grok build already punched above its weight and is the nicest TUI, claude and codex are clearly vibecoded by web developers that don't understand systems (eating SSDs, spaghetti logic, extinguishing kernel watch limits, etc). I think Anthropic and OpenAI are both engaged in their own theatre, trying to define and redefine what game they are even playing, trying to shift to immeasurables like safety or security or exclusivity. There's definitely room at the top.
SpaceX needs to keep raising many billions every year. The rockets part isn't going to make money for a long time, so diversion tactics<p><a href="https://news.ycombinator.com/item?id=48828648">https://news.ycombinator.com/item?id=48828648</a><p>Also Elon has a grudge with Sam Altman and wants to beat him
(from Cursor's blog)<p>> 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.<p>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.<p>> 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.<p>> 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.<p>> 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.<p>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"...)<p>Reports of xAIs demise (after giving a lot of compute to Anthropic) were slightly exaggerated, it seems.<p>> Grok 4.5 was trained across tens of thousands of NVIDIA GB300 GPUs
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.<p>(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.)
> Claude just gave up and did an HTML/CSS implementation but I insisted on native SwiftUI+Metal.<p>That sounds very odd and very contrary to my experience. You don’t say which model you actually used, but I never had opus 4.8 (or sonnet for that matter) ignore which language/stack i wanted to use.
It never happened to me, but Claude routinely ignores the single line I have in CLAUDE.md, so I wouldn't be entirely surprised.
Yeah, that makes no sense. I've never seen any model "just give up" and change to a wholly different stack on its own.
I do a lot of native iOS development using Opus 4.8 (and I used 4.7/4.6 before this). I have a very hard time with this comment, were you using Opus or something else?
Same. A few months ago I pointed Opus 4.6 at a mid-size Vue app and told it to create the iOS equivalent using SwiftUI, and it nailed it. I broke the process down to phases and reviewed each phase, but within about ten days I had a functioning iOS app that had full feature parity.
Let’s face it, there is no best model for something because the input is natural language.<p>Some models may fit better some users‘ way of prompting.
Yeah, I think this seems more true than "X is better at iOS than Y", the way you prompt seems a lot more important, and some models react differently to the same prompts.
I agree. There’s no chance Grok is better than Claude Code for this. And Claude is never so badly misaligned that it gives up and switches stacks.
I swear I have read either this exact or a very similar comment before. Same gist about a bike computer iOS app, and one of the models giving up.<p>As an aside, big thanks for Caddy! Really helped me get my greenfield project off the ground and it simply “just working” out of the box was one less source of errors I had to worry about when onboarding my team.
Was this in Claude Code for Claude? Did you use a weaker model like Haiku? Claude should absolutely not be as bad as you said.
I tried Claude Code with XCode once, I already use CC exclusively, either in the CLI or with Zed (mostly CLI now), and it was pretty unstable. I wish Apple would QA their products more. It seems to me the best way to use Claude Code for anything is stand-alone.
As someone also not happy with my bike computer (some truly horrific UI/UX decisions), could you share or explain what you made? I like your web server.
There's no way this is true.
<a href="https://tools.simonwillison.net/markdown-svg-renderer#url=https%3A%2F%2Fgist.github.com%2Fsimonw%2F63780e364cef9a13ff3529d03e00ab9e" rel="nofollow">https://tools.simonwillison.net/markdown-svg-renderer#url=ht...</a>
Announcement from Cursor, whose team also trained the model: <a href="https://cursor.com/blog/grok-4-5" rel="nofollow">https://cursor.com/blog/grok-4-5</a>.<p>Notably:<p>> 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.
Composer 2.5 is 1T total/32B active (based on Kimi 2.5), while Elon publicly said Grok 4.5 is 1.5T parameters total. Hardly a different weight class.<p>The API cost difference is ~2.5x, probably because xAI has much higher costs to recoup.
First impressions:<p>- 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<p>- 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.<p>- 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.
Concur.<p>Tried on a "this test suite is weaker than I'd like, too often depending on internal state rather than outcomes" problem via Cursor, asking it to "review and suggest solutions." It gave me a quality overview of the test approaches, strengths, weaknesses, and gaps then recommended a disciplined multi-prong approach based on a common, trusted testing library (<a href="https://hypothesis.readthedocs.io/en/latest/" rel="nofollow">https://hypothesis.readthedocs.io/en/latest/</a>). It broke down the things we could do this improvement pass or leave to later (staged scoping), identified some very hard/possibly-out-of-scope cases and gave me the option of focusing on them or not, and organized new tests in a logical way. After one round of feedback and plan tuning, I put it in agent mode and let it work. A few minutes later I had a much better test suite.<p>Have not tried Grok before and didn't have much confidence, but it did great. Exactly the sort of complex, detailed, nuanced analysis and multi-step task I would previously only trusted to GPT or Opus.
With each release from the the other major labs, it becomes harder for Google to tell a compelling story about Gemini 3.5.<p>Edit: Gemini 3.5 <i>Pro</i>. Expectations grow with each day it is not released.
Gemini 3.5 Pro hasn't been released yet.
Google wanted to release 3.5 Pro last month but because of the trouble Anthropic got with Fable they might have wanted to wait a bit for the dust to settle I could imagine. And now there is quite some competition. 3.5 Flash for me is a replacement to 3.1 Pro. It's more like a 3.2 Pro. It costs about the same (or more!) than 3.1 Pro, is a little bit smarter in many cases and a little bit faster.
3.5 Pro will be a lot more expensive and I expect it to juuuust be able to hang with Opus 4.8 and GPT-5.5.<p>I wish Google was able to actually push the industry further, either in terms of quality (intelligence) or quantity (price) but they've been playing catch up a lot.<p>They are playing the game a bit differently than all the others. The others have useable IDEs etc. while Google has a boatload of half-assed products.<p>Google better come out with a banger 3.5 Pro because who would have thought that Grok and GLM would be beating them?
Generous free tier, when its not overloaded.<p>Also I find the json schema support invaluable, does anyone else have that too now?
for what it's worth, it's fairly popular among my non-technical coworkers here in Russia. we have unlimited access to all models so it's not about the cost, and they still prefer Gemini over Claude and GPT. I never bothered to ask why, but I assume it's better at communicating in Russian.
Wtf do you mean by story? Performance and price are all people care about
Gemini is so far behind it hurts. It's useful for daily tasks and simple questions, but it codes like a model from late 2024. I can't imagine using it for any serious work.
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xAI > Gooogle & DeepMind<p>I did not have this one on my 2026 bingo card.
Every time I get excited about Grok’s performance on benchmarks and demo videos, I test it myself and end up disappointed.<p>I'll give this one a try with a grain of salt and lowering my levels of expectations
Imagine being excited about any Musk-led company or product in 2026…
My only complaint is that a $40 plan gets you very little usage out of Grok Build. 8 hours for an entire month, that is definitely not worth $40.
Great model, very nice. Opus class performance at Haiku level pricing (or cheaper with the token efficiency). This seems like a GLM-5.2 killer and this is what Sonnet 5 should have been.<p>This is a model I could really see used inside applications, where Opus or Sonnet or GPT-5.5 are too expensive.<p>I would really like to see a strong Deepseek v4-Flash competitor, which ideally is something like Sonnet 4.6 performance at <$0.30 per token. This is missing from main US labs.
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.
So basically since US stopped OpenAI and Anthropic for 4 weeks, it allowed all other AI Labs to almost catch up.<p>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...<p>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?<p>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...
This is the bind of an arms race. Any lab that tries to pump the breaks quickly becomes second rate. Regulatory capture doesn't work either because the technology crosses jurisdictions.
"Almost" is doing a lot of work there; there is no alternative to Fable.
Very hard for me to imagine this getting beyond a low-single-digit market share. I don't understand the strategy of xAI burning money on this.
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?
I think the "secret sauce" is not juicing the benchmarks. Claude models just feel like they are <i>better</i> than the benchmarks suggest, in terms of smarts and creativity, while models from every other company feel <i>worse</i> relative to what you'd think from the benchmarks. Only company to really internalize Goodhart's Law, IMO.
My gut feel is Anthropic is very technical and pedantic which makes their models really technical and pedantic. They're top at code and technical benchmarks but anecdotally I've found OpenAI to be significantly farther ahead for general usage.<p>Opus 4.8 will burn 10k tokens trying to answer something 100% whereas GPT-5.5 will burn 2k getting it 90% which is good enough for many things.<p>Some personal testing on a "help me find that restaurant" prompt <a href="https://gist.github.com/nijave/2873b8b10d8c732e46264237b075507a" rel="nofollow">https://gist.github.com/nijave/2873b8b10d8c732e46264237b0755...</a>
The problem is that the remaining 10% can bite you in bad ways.<p>I was in Cotswolds, UK a couple of months ago. For those of you who don't know, it's a rural region known for its "chocolate-box" villages and honey-colored limestone architecture. Basically, you go from village to village, most commonly via bus, taking in the sights and doing touristy stuff.<p>When planning the trip, my sister used ChatGPT, which helpfully (and relatively quickly) found the bus schedules and times for each hop.<p>Midway through the day, though, we ran into a huge problem: it turns out bus schedules are different on Sundays, and more limited. Which meant we couldn't actually go to our primary destination (the Model Village), and had to cut the trip short.<p>Yes, ChatGPT was quick and pleasant to use, but missed a crucial detail.<p>Afterwards I tried it with Opus and it did not make the same mistake.
Arguably I'd call that the 90%. In my case, answering the restaurant question correctly with "Rishi" in my tests was the sole intent and 90% of the problem. All the models "helpfully" added extra junk about the closure, dates, quotes, etc and many of them got these details wrong--the 10% or extra crap not central to the question.<p>If the central question was "what is the bus schedule on `day`" and the model screws that up, it gets a fail in my book.<p>Also curious if Google Maps gets the timetables correct (assuming it has them).<p>Semi-related, I also discovered that the default web search/fetch tools are pretty primitive and Exa MCP annihilates them. I ended up doing some comparisons with Claude Code comparing built-in server-side to Exa and to a Python MCP that used SearXNG for search and Exa was a clear winner and Python+SearXNG ended up coming out roughly the same after a few cycles of letting Claude optimize the Python code and adjust SearXNG settings. Ultimately it landed on this (making some changes to optimize returning relevant context directly in the search results so the model didn't need an additional web fetch call) <a href="https://gist.github.com/nijave/604c43e3e0fdcd60f5280d3a6b1096e9" rel="nofollow">https://gist.github.com/nijave/604c43e3e0fdcd60f5280d3a6b109...</a>
This likely comes down to how it accessed the bus schedules (i.e. web search tool) and not intelligence.<p>You need to add the actual bus schedule to context somehow (research agent, custom tool or just dump in prompt) and even the simpler modern models will be able to do the planning.
One angle could be their interpretability research? They understand what's going on in LLMs probably much better than anyone else. This must somehow pay off.<p>I think it's not only an alignment/security tool but could perhaps be used for capabilities as well.
I think it's focus? Anthropic seemed to double down early on being more business/prosumer focused. While OAI, Gemini, Grok, etc were also doing various side quests like image generation, Anthropic seemed to only focus on 1 thing, and that seemed to pay off
I think it's the talent, laser focus on single product set and being early so ahead, same with Open AI who are only a sliver behind. Google, XAI are the next level down but they have other concerns.
I think they have a better agent personality which pushes back and isn't sycophantic. It has been awhile since I've used the others but that's where it locked me in and I've stuck with it.
> isn't sycophantic<p>Not sure about that one... But I think the true secret sauce for all these models is how they reason. GPT never outputs how it thinks, which "saves on tokens" but Claude absolutely tells you how it thinks, and there's people who use how it reasons about solving problems to finetune smaller open source models, with surprisingly better output.
Given their pricing, I'd guess their models are just way bigger in parameter count. They've always underperformed in cost-per-performance.<p>They also target a cost-insensitive market (corporate/coding users) compared to Google/OpenAI which support massive amounts of free users.
I think it is a mix of the sibling replies here. I'd add that the company has seemed to find ways to ~do more with less.<p>I have never liked the various nerfs Anthropic has used to balance GPU (slowing down responses, quota variance, model optimizations etc) and it definitely has burned a lot of good-will.<p>But it has seemed that being able to look beyond the short term pitchforks has worked quite well.
From what I have read, their pre-training team is much better than anyone else. For OpenAI, their post-training team is better. And apparently OpenAI has consistently struggled at training a bigger model than GPT 4 level
I’m a VP Eng — the backend team I manage strongly prefers CC and Opus. The Android team I manage strongly prefers Codex and GPT 5. I’m personally not sure that the answer doesn’t just come down to stylistic differences in prompting and ergonomics in the harness. The folks that prefer Codex seem to get better one-shot results, whereas those that prefer CC are doing more iterative prompting. At any rate, I don’t think you should write OpenAI off when it comes to coding.
Someone has to know.<p>Would be nice if an insider would drop some hints so that the open-source space could make some good progress.
Nobody has to actually know the secret of their own success, especially not relative success to equally-secretive near-peers.<p>Same as with rich person autobiographies: even when they tell you what they think it is, they can't see the path not travelled.
>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?<p>It's self-reinforcing: they've got the best coding/research model, which helps them to improve their models better than the competition so they stay ahead.
because in the real-world, it's far better than the rest.
That's why few people use Grok, it's not even close in day to day work.
Refreshing to see model announcements without claiming #1 in some benchmark. The amount of documentation seems very immature [0]. No system card provided - compared to Opus 4.8 which shipped with a 246 page analysis [1].<p>[0] <a href="https://docs.x.ai/developers/models/grok-4.5" rel="nofollow">https://docs.x.ai/developers/models/grok-4.5</a><p>[1] <a href="https://www.anthropic.com/news/claude-opus-4-8" rel="nofollow">https://www.anthropic.com/news/claude-opus-4-8</a>
I think it's the first time ever we don't see the dominant model being surpassed by new released concurent models.<p>Did anthropic found their moat or we hit a Wall?
Tried this for a legal use case and it was excellent, comparable to Opus in quality but much faster. AI is miles behind in law compared to coding: the output was similar to a law student intern. But coherent and directionally correct and beats starting from a blank sheet of paper. Impressed.
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.
I'm guessing that they already have the model ready and the announcement blogposts locked and loaded, and then release them as soon as they see a competitor make the first move, trying to overshadow the first announcement or at least be swept up in the hype just as people start talking about new models again.
I think this one is just a coincidence, bound to happen given the pace of releases<p>For exact timing, probably 10-11am Pacific is just optimal for normal working hours
Maybe it‘s the Nash equilibrium from a timing perspective?<p>Like the reason that close to a McDonals there is usually a Burger King.
The joke is that McDonald's spends hundreds of thousands of dollars to identify new locations - traffic studies, visibility, demographics, nearby traffic generators, site characteristics, drive-thru feasibility, etc. They have one of the most rigorous processes in the industry. Burger King's process is to open a location across the street.
reminds me of SBC's (Seattle's Best Coffee) strategy, which was decidedly not Nash: put a store across the street from every Starbucks.
Competition. You don't want to lose your customers trying out the competitors updated and better product. Release on the same day and they won't be able to compare their new to your old.
But how do they know what day is that? Unless you have already something ready to be announced (and you just hold it until the very last moment, which doesn’t make sense, since you could just announce it asap)
It can also be ”we are done but wanna test it more and tweak it” and then ”oh they launched now. Let’s launch then as well”
All the people who are any good at AI talk to each other. There's no secrets among those who are making 7 figures plus in this field.
"keep refining and testing it until we're really done or somebody else releases"<p>Maybe a little corporate espionage.<p>Probably more keeping an eye on the behavior of the competition and predicting what they might do and adjusting your own schedules.
Do we have any proof that this was made by xAI and isn't some Chinese open model running with modifications?<p>Their inital image generation was a wrapper around Flux.
Even if they did start from an open model base, does (or should) that matter if it performs well?<p>Genuinely asking.
It’d be real funny if this was just GLM 5.2 trained on Cursor data
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.
Thanks for including a section on Token Efficiency (<a href="https://x.ai/news/grok-4-5#faster-than-flash-models" rel="nofollow">https://x.ai/news/grok-4-5#faster-than-flash-models</a>), hope to see this more prominently in all model releases.
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
Because of the of the political stuff, they have a bad reputation I think and are taken less seriously (I feel this way). They have an opportunity imo to break free from that and just not do the gatekeeping / condescension that the other providers are starting, and become more mainstream.
Even without the politics, Elon has shown that he will weaponize his platforms against people/companies he personally doesn't like (e.g. specific bans/demotions to external sites like Substack and Bluesky).<p>Using Grok is therefore a supply chain risk and it's not nearly good enough to offset that risk.
As opposed to what was happening before, on Twitter?
I do just want to focus on the 'even without the politics' asterisk though because sometimes there is a risk people think everyone on x side (x meaning 'a given side', not x.com) is wrong<p>You can claim Elon bought x as some sort of power trip. Fine. Willing to entertain it, I have no dog in the fight. I'm not a member of the Elon fan club. And yet Twitter (under Dorsey though I don't think he was involved) was banning tons of people under guises of 'misinfo' that wasn't misinfo
Americans are 4% of the world's population, and even among those 4% at least half don't give a shit. the rest of us give even less of a shit, we don't have the luxury to be principled.
To be frank, I will <i>never</i> use Grok as long as it's remotely affiliated with or under the influence of Elon Musk or his ilk.
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You can very roughly proxy popularity of close-sourced models through OpenRouter token throughput. Grok has an order of magnitude less OpenRouter usage than Claude, GPT, even Gemini.
They were missing a harness like Claude Code or Codex (terminal). However they recently released Grok Build, which is probably the fasted I've used, in terms of responsiveness, but didn't have a model at Opus 4.7/8 level. The thing is if they add 4.5 to Grok Build and keep improving the harness I think it can compete (cheaper and faster).
Completely irrelevant, which was expected considering their previous models were vastly outclassed by other models at SWE.<p>This is the first grok model that seems actually pretty competitive at SWE.
Wasn't, which is why they purchased Cursor.
If they were a frontier lab, you'd know.
They had two big substantive flaws on top of the political stuff. Aside from a brief window last summer Grok has been behind the curve for coding, and before the Cursor acquisition they didn’t have a harness. Now they have an Opus tier model and a real harness they have at a minimum the opportunity to undercut the competition on price. And with the 5T and 10T models being trained on Colossus 2 they have the possibility to leap ahead.
No one's made a MechaHitler joke yet?
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What kind of comment is this? Such bad faith and adding nothing to the discussion.
In addition to "nonconsenual porn", it's far superior to almost any (actually any?) open source LLM
1+0 records in and 1+0 records out
They talk about benchmark first places at every release, but in reality from 4.0 onward Grok got worse every release. So bad in fact that they removed the login-free access and rented out colossus.<p>People don't buy it any longer, just like no one bought the fake SpaceX stock recommendations yesterday and everyone just sold.
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 <i>could</i> mean this model is actually pretty close to the SOTA as the benchmarks indicate.<p>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.<p>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.<p>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.
Personally, I wish they had shared some of the galactic code that GROK claims to have generated.
still waiting for a proper gui for grok build<p>terminal is nice but codex desktop app is very useful
What would have been fantastic is if Cursor offered Grok 4.5 in the same usage tier as "Auto + Composer", than provide it as "double usage until July 12" under the API tier (which is what they're doing right now).<p><i>EDIT</i>: After looking at my own usage stats - I stand corrected! It <i>is</i> under the "Auto + Composer" tier - brilliant!
Another subpar model. Why don't they go open weight?
Not available for Europeans yet. :(
I think it should be available through Cursor?<p>EDIT: Tested myself, it's actually NOT available from EU. But with a Swiss VPN it works :)
xAI is under criminal investigation in the EU
Who isn't
I'm not - then again I didn't launch a image generation model advertised as having a spicy mode so that might have something to do with the coincidence.
You <i>think</i> ur not...
Apple, Google (Alphabet), Meta, Tik-tok etc are also, it's the EU shakedown method.
Contrary to what you're implying, that's more of a reflection on the typical US corporation than the EU.
Luckily, basic VPN to US is enough to use it. Just tested it.
They say "EU availability is expected in mid-July". So next week or so.
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Just as a reminder, Musk's actions lead directly to the deaths of thousands of people in the third world: <a href="https://archive.ph/20250629012329/https://www.nytimes.com/2025/06/21/opinion/waste-musk-trump.html" rel="nofollow">https://archive.ph/20250629012329/https://www.nytimes.com/20...</a>, <a href="https://www.propublica.org/article/kenya-trump-usaid-world-food-program-starvation-children-deaths" rel="nofollow">https://www.propublica.org/article/kenya-trump-usaid-world-f...</a><p>Enjoy your chatbot!
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?<p>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.
Twitter was supposed to go bankrupt if you only read news articles from journalists about it. If you looked at Musk's operating track record, you might have had a different opinion.<p>In the transaction announcement (xAI buying twitter) twitter reported $12b in debt on acquisition, roughly the amount originally sourced ($13b), so it apparently made good on its debt covenants during the operating period. I have no idea if it received additional capitalization from Musk to do that or not.<p>That said, the deal was classic Musk - anybody who went on the equity ride with him in Twitter just KILLLED it; xAI was valued at $80bn and twitter at $33bn, so the owners there became 30% owners of xAI. xAI was acquired for $250bn at a SpaceX valuation of $1 trillion, or 20% of the resulting entity, so the twitter stock was 6% of spaceX at about $2 trillion, or $120bn on an equity purchase price basis of $30bn. and that $120bn in value is on really good daily trading volumes; lots of depth.
That was the point of the bailout. Twitter is already a rounding error so no one will notice if it goes to zero.
twitter was "acquired" by xAI which was then "acquired" by SpaceX as part of the IPO strategy, (and part of a strategy of giving the investors on the hook for the twitter acquisition a return). Who knows how it performs, but yeah, now that it's the social media arm of the SpaceX conglomerate, it will likely be around for a long time, especially since it serves the basic function of stroking Musk's ego.
None of them go bankrupt. The whole thing will just get stuffed into a larger Matryoshka egg that IPOs for eleventy trillion dollars in 10 years.
I'd say the prediction is correct, as the acquisition is more or less just a better way to capitalize on the bankruptcy.