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.
The $2/6 pricing seems to only apply for context under 200K.
Above that (max context is 500K) pricing doubles to $4/12.
https://docs.x.ai/developers/models/grok-4.5
Also, the cache hit pricing is 25% of the input pricing ($2 vs $0.50). Long agentic workflows are dominated by cached input. The US frontier labs typically have this at 10% of the input price, and DeepSeek/Xiaomi etc take it to the extreme 1% range (which is why those are cheap to run in real world agentic loops with dozens of toolcalls per run)
Just to add context (no pun intended), OpenAI also charge differently based on context usage with GPT 5.5 being $5/30 below 200K, and $10/45 above.
Anthropic have a fixed price regardless of context usage.
These per-token pricing schemes aren't directly comparable though since these models all use different numbers of tokens, even for input (Anthropic's recent tokenizer change generates 30% more tokens for exact same input), as well as for reasoning, and context/token usage also varies wildly by harness with Claude Code using 3x the context/tokens of Pi.
I didn't know that, thank you.
Does anyone know why they would charge more for higher context usage (other than they can)?
Think of the entirety of the context (the full thread of conversation, all tool call output, etc.) as one message that's been submitted to the LLM all at once just so it can generate the next token (which is just the next word or even syllable.) Once that token is generated it is appended to the context, and the entire context is once again used to generate the next token. Keep doing that until the entire response is generated. The larger the context, the more stuff the model has to pay attention to as it generates each next token.
To use an analogy: imagine your friend is the author of an unfinished book. They die with 19 chapters written, and on their death bed ask you to write the 20th chapter. Assuming you're up to the task, you can only do this well if you take the time to absorb the entirety of what's been written so far.
This is how LLMs work. Context caching is an optimization on top of this, but it has its limits.
I guess it does increase their cost, or rather your share of their hardware depreciation.
AI serving cost is apparently mostly hardware depreciation rather than operating cost (electricity etc), and if your large context request is occupying VRAM for some fraction of a second then you are paying for the depreciation that occurs in that time!
e. H100 costs $20-40K to buy, with a lifetime of maybe 3 years, and will only consume maybe $2K in electricity if run 24x7 for those 3 years.
Womp. Didn't see this anywhere else.
No longer feels as inexpensive. Will likely just include this in the rolodex of <200k context tasks, like being one of my review agents.
Yeah but depends how you use it - with superpowers and it’s prevalence of splitting things into smaller focused subagents - this could seriously reduce costs …
I wish my company gave me more options than just using Claude to test these things out
Claude Fable and Opus 4.8 1M are by far the best, smartest models. Anything else is a downgrade so you’re not missing anything.
The recent Databricks comparison has GLM 5.2 performing identically to Opus 4.8 on high effort, and some early Twitter reports (e.g. from the OpenCode developers) strongly favor GPT 5.6 Sol over Fable.
As always it depends on what you are using them for, and how you are using them.
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.
Don't they just rent them out to the frontier AI shops? They're not sitting idle.
There are various difficulties with renting GPUs, especially if your setup is very custom.
The competitor would have to port their training systems to your specific network architecture, system design, rdma Vs ethernet vs infiniband Vs nvlink etc.
Getting it running might not be too hard, but getting it running efficiently and making good use of all those flops will require considerable human effort and wall time.
Add that to the fact most frontier labs seem to have a single huge training run - and to my knowledge nobody has figured out how to distribute that training run between data centers effectively.
I'm not saying this as a hypothetical -- I believe xAI is already renting their GPU farm out to frontier labs. Whatever logistical challenges exist, enough of them seem to have been overcome.
Surely rented GPUs would be used for inference, which runs anywhere and needs to scale much larger than a training run.
They do, yes.
So where are these mythical savings coming from? You're saying they have spent more per user therefore can charge each user less or something? I'm not following.
The (optimistic?) take is that xAI is genuinely better at building datacenters at scale than anyone else, and the freedom to use Nat Gas as the primary energy source allows them to have lower marginal costs.
The (pessimistic?) take is that they have loads of idle GPUs and want to get some revenue out of them rather than none. Compare this to OpenAI/Anthropic where every token used by a consumer has to compete with enterprise spenders, and there’s not enough to go around for everyone.
It's also sensible for them to provide a cheap, intelligent model to users if they have capacity, then once they built a user base, tighten the screws. All the other AI providers have done that.
It’s basically a clearance sale, is the theory.
More like they have a less focus on margins and more on cost recovery.
Definitely. They had insanely low rates on TTS up until a month or two ago ($4.20/1M) for example, which they only recently started increasing.
As their models get more competitive I'm sure prices will catch up.
“We lose money on every rack, but we make up for it in volume!” - Elon Musk, probably
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.
xAI had $2.5B in operating losses in the past quarter. What savings are being passed on?
they are renting parts to google for like 1b a month
really dont think they have a lot of idle power
If they've got billions to rent out, they're not using it...
Profitability is never a constraint for Elon companies. He has always been able to be able to extract money from the middle east, government, banks, retail investors (or these same parties through his other companies) whenever they need more.
His net worth is orders of magnitude bigger than the cumulative profits his companies have ever produced (even if you only count the profitable quarters)
It's really easy to do this actually. You just create cars that drive themselves and rockets that land themselves and people start throwing money at you.
I paid for a car that drives itself in 2018 and I haven’t received it. Any updates?
2018 cars get FSD 14 lite this month. Not a great situation but not bad for an 8 year old car.
> Not a great situation but not bad for an 8 year old car.
Not bad to get a product that underdeliver 8 years late ?
When can I get a Tesla that drives itself? About six months?
From your local Tesla dealer. Technically, you do still have to "supervise" it, which basically means making sure the little camera that watches your eyes doesn't catch you with your eyes off the road too much.
My sister-in-law's mother drove one from Florida to the northeast without touching the steering wheel or pedal/brake, right down to the parking at each end.
I've been a humongous fsd sceptic for a while, but had to lay that aside after I went for a test drive (test ride?) in one of these things.
What that technicality means is that you are liable when the car kills someone, not Tesla. Level 3 self-driving is a completely broken idea. People cannot closely supervise a process that never requires their input - when you suddenly are needed you will not be prepared to respond quickly enough. Either you are driving the car, or you aren't. If you are liable, you are driving it.
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No need for theorizing. xai is selling their excess capacity to Anthropic and Google with large markup
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.
You get "Grok Build" (the CLI) that uses the Cursor and/or Grok 4.5 models when you buy SuperGrok, which is like $300/year. I don't know if there is a feature-to-feature comparison by anyone on this, but you can get access to these models with unmetered tokens with SuperGrok.
The model is available through Cursor which has $20, $60 and $200 plans. I assume the $60 version might work better for you?
Will have to give that a try I suppose.
Grok Build sucks compare to composer 2.5. Just use compose 2.5 and you'll have basically unlimited usage on the 40$ 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.
My time is more valuable that I will use a model that doesn’t f** up my code base.
Keep composer away from anything configuration related—it will ruin your day.
give it a structured plan and it it does really well compared to similar priced models. I'd never use it for anything that required heavy reasoning and it's not built for that.
Isn't Composer 2.5 designed to be used from the Cursor harness, and is otherwise not that useful?
I use it from the Cursor harness.
Weird I use it 8 or so hours a day and I haven’t had that issue
I think we need to be explicit about the domains we're applying Composer 2.5 to in these discussions.
I mentioned here (https://news.ycombinator.com/item?id=48766275) how poorly it handles my specific use cases. My coworkers in DevOps and frontend UI swear by its cost-effectiveness, whereas I strongly prefer the reasoning capabilities of Opus 4.8 and Fable 5.
Composer 2.5 seems to be SOTA for Helm charts and React/Vue, but, for my usecases it absolutely struggles spectacularly when tasked with rigid body dynamics or kinematic logic.
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.
The harness is commonly ranked one of the best. what specifically had you hating it?
Yeah, it’s not great—except for debugging. It shines there.
In what way? I spend more of my time managing than hands on lately so I legitimately don’t know.
Not my experience at all. I've been using Cursor hardcore for about two weeks now and Composer 2.5 and it's wonderful. Now with Grok 4.5 I'm quite excited about the possibilities.
Not true. The only issue is cost of frontier models.
Cursors whole moat is their harness. Other people have benched opus and GPT models through Claude code, codex, and cursor, and cursor came out on top everytime because of their harness.
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
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.
It’s a fantastic signal processing / engineering app. There are 5 major players and this app isn’t quite as good but it’s in the ballpark. I’d day when I release early next nonth this will be the biggest fully featured vibe coded app I’m aware of.
I’d be curious to hear more about your dev setup and what tips you have for other aspiring vibe app coders.
I currently just use a Mac Mini M4 with cursor. I had 30 years programming before this (in my mid 40s now) so I am not a great person to give vibe coding advice because I have so much actual coding m. I’m pretty sure I couldn’t have done it without knowing how to code because there were some times even the best models would get stuck and I’d have to figure it out myself.
If I had to give advice I’d say to just do it. Work on some project that interests you and go for it
How do you like its design mode?
Suppose eventually that gravy train will disappear, might as well use it then.
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.
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Around Opus 4.7 level would be the same as Sonnet 5 while being cheaper overall.
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 is there.
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.
Yes, Fable tends to only shine when the work to be done is complex and it takes a long time. Other models wedge in different ways.
I do wish the subscription had a separate weekly allocation for rare usage.
I'm not sure if you are aware, but you have to approach prompting Fable slightly differently from a model like Opus.
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".
1: https://platform.claude.com/docs/en/build-with-claude/prompt...
2: You might find some of the example prompts listed here useful https://x.com/trq212/status/2073100352921215386
You mean the parent was holding it wrong?
Oh, come on.
Some things require skill to use most effectively. It's fair enough to consider this a failure if the thing in question is "making a phone call", but when it's something like "getting an AI system to do a good job for you" this is not a reasonable thing to make fun of it for.
It's like...
"I wrote a program, and it segfaulted instead of printing out a list of prime numbers." "Yeah, look, you've got an off-by-one error here." "You mean I'm holding it wrong?"
"I'm trying to play the violin and it's making horrible noises." "You want to change your grip on the bow like this, and be more careful in where you put your fingers on the strings to get the right notes, and there's a whole art to how you adjust the speed and pressure and so forth to make it sound good." "You mean, I'm holding it wrong?"
"I'm managing a team, and one of the people on the team doesn't always do the things I tell her to." "Maybe you should sit down with her and see whether somehow your explanations of what you want aren't getting across, or whether she feels like you aren't treating her with the respect and dignity she deserves, or whether she's bored with the work, or etc. etc. etc." "You mean, I'm holding it wrong?"
Yes. In the second case you're literally holding it wrong. Some things don't work as well when you hold them wrong and it's worth some effort to learn to hold them right.
I hold no particular brief for Anthropic. I don't know whether Fable is really much better than Opus or whether the alleged improvements are all just pareidolia or something. But "getting the most out of this immensely complicated thing that's in some ways kinda like another human being can be tricky" doesn't seem to me like an implausible proposition, and if it's really doing something akin to human-like work[1] then it's not unreasonable if you have to approach working with it in something a bit like the ways you approach working with other people.
[1] If it isn't really doing something akin to human-like work, then why are you bothering with it at all?
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.
Even so, I'm just not that impressed, I felt like I got more done by just using Opus.
Yeah, that's what bit me. Even Anthropic's own documentation seems to indicate that Fable is not all that great as your go to model for tasks. What it seems to excel at is a sort of leadership role because it proactively keeps all the subagents in check.
If you're not explicit in the prompt or haven't configured your environment then the default behavior is to use subagents that match the host.
You can write a skill (many have) to lead it in how to use different models and efforts for subtasks. For searching the code base, for example, I have it use Haiku, which is fast.
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.
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.
But very expensive compared to Deepseek v4 Pro, which performs similarly.
Grok is stuck in a difficult place - not the best model at anything, and not the cheapest either. It's hard to make a case for using it on any dimension, even before you factor in the history (I'm not sure suggesting the company uses the model that refers to itself as "MechaHitler" is the way to a promotion).
It’s the #1 model for creating CSAM
An AI model can't create CSAM unless you're claiming it's managing to hire people to commit crimes in the real world?
The comparison may be better against GPT 5.6 Terra (instead of Sol), which is $2.5/$15.
We don't yet know Terra's results for DeepSWE/TerminalBench though.
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?
Noam Brown (OpenAI) "Implications of Large-Scale Test-Time Compute" https://xcancel.com/i/article/2064210146558136827
until I exceed my Claude Max/$100 sub (have not hit a wall so far), the pricing of these models isn't relevant to me