after using cursor grok & trae.ai for several months , grok curor is highly superior results to trae.ai

Elon's mother recently posted an AI-generated photo of her son's birthday party. The tag indicating such was scrubbed as soon as it was pointed out.

This has been my experience as well. Grok will end tasks almost immediately and claim "Done!". It's definitely the laziest and most "dishonest" of all the models. The others aren't perfect, but I can't use Grok for any serious coding task.

Reset context

it doesn’t work nearly as hard

Until you ask it to start generating horrific imagery and then it's best in class.

I was going to say the reverse - claude has been the less satisfying normalized by benchmark for me in the last year. Both astra and fable have their quirks, but I am 90% codex this year up from 10% last year.

The target market for frontier models is CEOs who want to lay off entire departments of their company. So the long autonomy benchmarks would seem to be sending exactly the right signal.

Their leading benchmark with cost per task shows a tough sell compared to Fable 5.1 Low and doesn't reach the performance of Fable 5.1 Medium.

How representative that is of real world usage, I don't know.

In their benchmark GPT 5.6 Sol performs suspiciously poorly compared to the former models.

No but almost all good ideas can be reduced down to a few sentences if you're good at explaining things. It's a different kind of intelligence than what's commonly called IQ but it's something like that regardless.

Sure the explanation will oversimplify a lot but then you can expand it recursively if needed, you gotta start somewhere.

If you are saying that there are problems more complex in this world than cancer to be solved, I'm not denying you.

Solving the problem right in front of you is easy. Stepping back and asking: is that a problem to be solved, is infinitely harder.

I did not use Claude to write my comment, so I don't know where that is coming from.

If you can't explain it simply, you don't understand it well enough

That's half true. A very smart model should be able make good explanations, which include simple understandable prose. That can should be possible even as its thought process gets more alien.

I'm going to assume it's very difficult to assume what a user actually knows from the very small signal that comes in a prompt.

effective communication is knowing who the audience is. Everyone naturally knows their audience to some extent, except for the "neuro-atypicals".

It is unsurprising that a LLM fails, without coaching, to effectively communicate.

I find people tend to lack nuance in their communication more often - leading to problems and misunderstandings.

If I don't grok an elaborate explanation, I can ask for clarification. If it's explained to me in an overly simplistic or unnuanced way, I'll walk away with a false sense of understanding.

That said, I'm sure we all have very different concentrations of these types of people and problems around us. I've definitely met some engineers who seem to actively try to make their language incomprehensible

They’re training the models on generated slop. I’ve done a comparison on samples from the different versions of Claude and they’re worlds apart. The ancient, supposedly obsolete models speak normal English.

Look up the output-style setting, which is a bit stronger than putting it in the system prompt. The new "concise" setting is better than the default but in practice, Claude is a very stubborn model when it comes to these patterns and they're really hard to eliminate, mostly you can only hope to mitigate.

As for the wording of the prompt, you're pretty on point, I created a custom output style targeting mostly the first two you have there. Some people have wording that demands a certain technical standard or uses fancy words to describe what to avoid, but I haven't seen evidence those work better than asking plainly and I suspect the opposite: LLMs mimic the user to a degree so talking to it in terms of technical specifications and fancy words is an invitation to get them back.

Part of intelligence is knowing your audience and communicating efficiently.

Bingo! And on this axis many SOTA models fail miserably. These things are acting on my behalf under my direction. All the supposed intelligence in the world means fuck-all if nobody can understand it.

And like somebody else said… when meat-based humans talk like Claude does, it almost always means they either don’t understand what they are talking about, or are actively trying to conceal something and are a fraud. Not always, but almost always.

Yeah just today it told me in a snarky way that my CPU (7940HX) doesn't exist and that I must have misread it and it's either 7945HX or 7940HS. Yes, AMD (re-)branding CPU models makes things difficult but I thought we are passed AI models making such egregious mistakes

It's pretty much the biggest complaint of Claude compared to its competitors, so they really should adress it .

Same. The issue with Anthropics models is that (speaking regarding code generation) they REFUSE any kind of comment override instructions. I've tried everything and no matter what, after a few turns, they resort to generating the same overtly verbose junk. Bun's codebase is littered with them See

   // `HANDLE` is an opaque kernel handle (kernel32 validates and returns 0/FALSE
   // on a non-console handle); every out-param is `&mut T` to a `#[repr(C)]` POD,
   // ABI-identical to the Win32 `LP*` pointer (thin non-null). The reference type
   // encodes the only pointer-validity precondition, so `safe fn` discharges the
   // link-time proof. (`bun_windows_sys::kernel32` declares these with `*mut`;
   // redeclared locally so the legacy-conhost cursor path below is plain calls.)

or

   // Progress's terminal handle is the canonical `output::File` (vtable-backed
   // stderr/File from `OutputSinkVTable`). The duplicate `ProgressTerminalVTable`
   // from B-0 round 1 is removed; tty/ansi/winsize route through    the new
   // `OutputSinkVTable` slots so `bun_core` stays T0 (no `bun_sys` dep).

from src/bun_core/Progress.rs

yes

I would if they let me bring the subscription I have to the harness of my choice.

Agreed. It's very capable for something carrying the "flash" label, super fast, and very clear to read.

It's coding is still meh. Greenfield applets all day, but don't throw it in the jungle.

It's best for brain storming, rabbit holes, and image recognition.

Let the big models do the heavy lifting for now.

I remember the Gemini 2.5 Pro series was the best at text, but failed half of it's tool calls. They really redeemed themselves with the 3.5 Flash series, it's great. I love how fast it is. Now if only they could offer a subscription service for it...

it's definitely not bigger. smaller if anything looking at how much faster it is

Anthropic has probably RL’d the system prompt into nothing because of their fear of the user being able to control the model. If it listened to you about the slop language, it might listen to you if you asked it to help you with no-no tasks.

It does work, you however have to put it into every single prompt in which you didn't want a rubbish response

Literally every one, even 1-2 prompts later it starts to go back

For me it works at first but Claude models forgets it after some prompts, despite only using like 100k tokens.

This fixed claude! Thanks!

I think this is more a meme than anything else, for a couple reasons:

First, after a while it's just as grating as Claudeish. Second, my hunch is that it constricts the actual thinking of the LLM, like the same way that Newspeak does in 1984. It shrinks the range of thought that can be expressed if used as an input.

I think the real way to do it is to have another Claude entirely deal with the user as a liaison, but to keep the thinking in whatever format it came in.

Latent space reasoning, if you think about it, is exactly this to a crazy degree: why even formulate a thought as words if you can just keep it as matmuls until the user needs it? And then, if the user needs it, have it always specifically formulated for the user by another LLM rather than constrict its range of thought? Anyway, that's my take.

Using a variety of models feels similar to the benefit of having a team of individuals from different backgrounds.

Agreed, Claude has a "Claude Design" tool but doesn't publish any frontend brenchmarks. Maybe the industry will develop one.

Do you have any connection to Portugal? I imagine if you have Portuguese in any of your prompts that might bleed into your user profile which becomes a part of every prompt. Alternatively it might use browser language settings

My favorite part of the new Groks has been how they speak in plain english.

I don't know if it's the plain english or what, but I really like Grok for legal research (as opposed to code). It's got a noticeable edge in getting to the point compared to Opus 5.

I've found grok 4.6 speaks heavily in Claudish. It especially likes using verbs as nouns.

Looking at AA and Vals, your theory seems to check out.

this is exactly it.

The chart is cursorbench though and they asked about the "deceptive graph"

They can benchmark it because you can use an openai api key with cursor. Astra is just not included in the cursor plan.

The point of Cursor Bench is to show how models perform in Cursor. If 99% of their users won't be able to access a model unless they go out of their way to include setup an API key for it (which would be insanely expensive with Astra), why would they include it in the benchmark?

Cursor never added Astra to its consumer subscription plans. And it's likely exactly because of this announcement. Why would they add support for a model they would have to remove shortly after?

Pulled out from letting them resell Astra access, that's not a limitation on running a benchmark.

I've had really good experiences with Grok 4.6 and grok build. I've been playing around with tscircuit and it can write code with an understanding of spacial reasoning, while also importing cad components from different file formats into tsx, I've been having claude come in and try to error check it and so far claude hasn't found anything to improve in my three projects.

I'm excited for 4.7 although I share skepticism with other users whether 4.7 will be significantly better, since they didn't raise the price.

Nice to see this release cadence increasing and some continued improvement in quality. I am guessing these models are basically still outcomes of the cursor team integrating with the massive amount of compute they now own: I’d imagine we will see significant step up improvements with grok 5 later this year as the team gets more experienced and confident with larger training deployments. Here’s hoping for another competitive frontier model!

It might be more capable, but AA indicates it's a lot less token efficient than Grok 4.6: https://artificialanalysis.ai/agents/coding-agents?agents=co...

I've been experimenting with omp because:

- it allows different models within one session via roles (I only have API, so pay per token)

- it's much more likely (ime) to use the LSP over grep for determining how code fits together

But I agree a 20k+ starting context is way overkill.

I find it's very hard to get information on harnesses people are using. I have to stay model agnostic so I avoid claude, codex, cursor, etc. I've used and tried opencode, which worked well, but obviously lacks the above features.

Does anyone have a resource for following what people are actually being productive with? With so much vibe going on it's hard to separate the wheat from the chaff.

What a crazy thread lol. I really can't tell who is serious and who isn't there.

you are a joke if you think omp is a joke.

Just pi. `pi install` the packages you actually need or ask LLM to write a package for you. Keeping the harness minimal is the point of pi.

Congratulations to the team!

We've only used it through API - but you're right, now API supports xhigh for 4.5-4.7.

I think 4.6 got an xhigh after launch. The benchmarks seem to all have been against 4.6 high.

Your comment is like 6 months to a year late.

There for awhile it seemed like we’d have 3 big competitors but then Grok 4.2 or 4.4 was just diabolical while OAI and Claude continued their significant improvements. Grok was/is so bad that I was convinced musk was gonna shut it down and just fund Anthropic compute once they reached their compute agreement.

( why is the x-axis on the first chart in descending order ? )

Sure, if you're doing easy work. But Grok is a lot more intelligent and can handle harder tasks.

Yes, but with subpar caching and higher cached token pricing, compared to directly using the DeepSeek platform.

One example is Opencode. https://opencode.ai/v2/docs/console/models/

"Privacy# All these models are hosted in the US. Providers follow a zero-retention policy and do not use your data for model training, with the following exceptions:

Big Pickle: During its free period, collected data may be used to improve the model.

DeepSeek V4 Flash Free: During its free period, collected data may be used to improve the model.

MiMo-V2.5 Free: During its free period, collected data may be used to improve the model.

Laguna S 2.1 Free: During its free period, collected data may be used to improve the model.

Ling-3.0-tiny Free: During its free period, collected data may be used to improve the model.

LongCat-2.0 Free: During its free period, collected data may be used to improve the model.

North Mini Code Free: During its free period, collected data may be retained and used to improve the model. Do not submit personal or confidential data. See the provider’s Terms of Use and Privacy Policy.

Nemotron 3 Ultra Free (NVIDIA free endpoints): Trial use only — do not submit personal or confidential data. Your use is logged for security purposes and to improve NVIDIA products and services. The logged session data for improvement purposes is not linked to your identity or any persistent identifier. For more information about data processing practices, see the Privacy Policy. By interacting with this endpoint, you consent to the collection, recording, and use of such information and the NVIDIA API Trial Terms of Service."

openrouter, "together" provider is fastest (165 t/s at the time of writing) and has ZDR and all

https://openrouter.ai/deepseek/deepseek-v4.1-flash?endpoint=...

I like devcontainers

I use it on fireworks which is US/ZDR and pretty reliable. We run a few hundred million tokens/day through it for dollars. Many are cached, which is super duper cheap.

Not only that all DeepSeek is all garbage.

GLM or Kimi are better for my own personal projects. DS? uhm. it just keeps doing dumb crap

It's probably the most aligned (to a single person) model out there!

The voice is the weird part. The early Grok 4 models had a very distinct presentation unlike anything else out there. Then suddenly it made a big jump in coding ability and started sounding just like every other model.

If you haven't used it, how do you know if it's winning?

I think it's winning on UI for normies (grok bot) and they made some claims about being pareto SOTA (lowest cost per task completed) a while back with 4.6.

I find it to be a perfectly capable model for implementation (there are many in this class--deepseek flash, spark1.3, luna, etc). I find the usage to be very generous w/ supergrok. I find the model to be just fine for 90% of what I want to do, but I use a smarter model to plan complicated things.

After the cursor aquisition it's become a quite capable coding model. If you take cost into account, it's close to the top. OpenAI is maybe still #1, but I'd put Grok at #2 (again, including cost as a factor).

Do other prices vary for >200,000 or just the cached tokens?

$0.40

If the CursorBench 4.0 score diagram is the headline, I read it as "Grok 4.7 xHigh is almost the same as Fable5.1 on low".

Is there a metric for like... time taken when comparing these two? I see score and cost.

If Fable5.1 can knock it out more quickly on low but Grok4.7 might take twice as long to stumble through a problem (and leave behind a bunch of yucky comments or un-needed extra unit tests), are they really comparable?

Or like... the "quality" of the solution? "It works" versus "it's unmaintainable/very messy/hacky".

Good thing they used 5.6 sol instead of Astra for benchmarks, the EEbench one is crazy[1]

[1]https://eebench.org/

Same, Astra is extremely RL fried, and nobody is talking about it. I used Astra for a few days on my personal project, and load times went from less than 3 seconds to almost 30 seconds because it kept using the wrong sync primitives and bad architecture overall.

Not surprising considering Grok 4.7 is a 2T model, so Sol/Opus class, not Astra/Fable class.

No, Astra isn’t better for coding. I’ve switched back to Sol.

Are there good tools for doing context audits? I feel I have no good way to visualize what a new session is getting by default in a given repo without crawling through every potentially included markdown file

Poor fella doesn’t have a seat. Intriguing design where both pedals are on the same side of the frame. Balancing must be a challenge.

What is the default reasoning level?

I tried in Cursor and see a lot of improvements over Grok 4.6 svgs. The AA numbers indicate it's not very token efficient though: https://artificialanalysis.ai/agents/coding-agents?agents=co...

You'll have to include it in the future, or your benchmark won't be relevant.

For now, I doubt anyone would notice your protest if you didn't announce it.

I think you're seeing a big shift around it.... since it's been markedly cheaper and also still easily available from OpenCode, it's getting large enterprise traction.

I am using exactly the same flow.

Astra for deep dive investigations, Sol 5.6 at mid-level for day to day tasks, Grok 4.6 via Cursor for routine and low complexity tasks.

Try using astra as an orchestrator for deepseek 4.1 flash, it seems to work out quite well.

No doubt xAI has seen rapid progress, but it's been several months of them being "just behind" OpenAI and Anthropic. It seems the gap between just behind the frontier and pushing it is a lot wider than most people thought it was a year ago, and that's why a clear third contender in the frontier model space has yet to materialize.

Same reason Coca-Cola only mention Pepsi and Pepsi only mention Coca-Cola. It's an proven way to capture the market. You would rather split the pie in two rather than in 4,12 or 50 right?

Possibly interestingly, I can't take you seriously for having such a superficial approach.

You probably shouldn't cut off your nose to spite your face.

You should try it, it is less sycophantic than other models and is faster and better at most reasoning levels, don't confuse the twitter bots and services also named Grok with the frontier model itself

I am subscribed to ChatGPT, Claude, Kimi and GLM coding plans. 200$ one on GPT and the 20$ ish ones on all others. Recently added Grok and it has somehow bacome my second most used model.

For daily one off questions I prefer it because it is fast enough and I like the way it responds. I also use it for basic research like “find me a battery drill for this and that”.

Kimi and GLM feel extremely coding oriented. I use them for code reviews basically. I hate the way Anthropic models talk. GPT takes too much time and effort for that kind of stuff for some reason.

Grok happened to be a nice middle ground.

Well when I ran out of Grok SuperHeavy subscription ($300) once and tried to use extra credits to cover half a day remaining till reset, $50 in extra credits went in two hours. Based on that, subscription definitely lasts longer; Grok subscription just about covers a week of my work (sometimes a bit extra remains unused, sometimes it runs out half a day to a day early). And as a point of comparison, it lasts for doing same tasks as 2.5-3 weekly limits of Codex on 5.6 Sol did (using xhigh on both Sol and Grok); I needed 3x$200 Codex subscriptions to cover my weekly usage.

By far the worst value subscription of any. I tried Superheavy and got about 5-10% the usage of CC/Codex.

I find I can just about get by with coding every day on a Cursor $60/mth sub with Grok fast mode disabled. Doing pretty heavy coding work/requirements etc, but not much sub agents and no loops.

For me and what I’m doing that’s insanely good value.

I find grok build chews through my SuperGrok sub very quick - but I think that is due to it having the 500k context window which uses more credits. Cursor limits it to 256K (tho I see in today’s update for Grok 4.7 there’s now a toggle for context size).

No, you can add Grox 4.7 high to the chart. 36k vs 66k

Output tokens from Intelligence Index:

- grok 4.6 (xhigh): 97M (for 44 score)

- grok 4.7 (xhigh): 240M (for 46 score)

as someone who is limited by amazon bedrock support at work (no idea why we got stuck with the worst one) - grok is literally the only budget-ish model option, so nice to see it updated, Sol and Opus are just too rich for my blood. Luna is good but so slow at getting things done (tps wise it's fast)

Every Grok release obscures their cache pricing while highlighting their input/output pricing

From their headline comparison:

  Grok: $2/$6 per million
  
  Fable: $10/$50 per million


  What this doesn't say: Grok costs 0.50/M cache read, Fable $0.25/M cache read

Long running agentic workflows are dominated by cache reads.

Just makes Grok sound deceptive, and more importantly, reliant on user's lack of understanding of costs aka predatory (which in turn is more infuriating)

I've been using 4.6 for some one-off game mods/utilities and it has done very well. "I have a very niche keyboard (Moonlander) and I play this very niche space sim, make me a SVG keyboard cheatsheet for it". Told me to grab keymap.c for the keyboard and inputmap.xml for the game's key bindings, churned for a while, then spit out a pretty good first attempt, along with the python script used to generate it. Spent another hour of back and forth to refine the script, and now it generates great diagrams that will adapt as my keyboard firmware and game bindings evolve: https://files.catbox.moe/x0u76x.svg

Excited to try 4.7. I hope they fixed the "it's not X, it's Y" that showed up in 4.6.

This thing is DOA. They compared 4.7 xhigh to 4.6 high to make it look like it improved. The reality is pretty bad: https://x.com/chetaslua/status/2102087511367618942

Looks like they have still problems with caching. Prize is double the other providers for cache hits... which is most of what I do. :/

Absolutely not. I know Elon retweet them a lot when Grok is good. This is how I discover the company.

I like to follow them and look for benchmark for each LLM release.

because boomers will give you free money + tip

musk can fund the space stuff with this

looks like token efficient/verbosity took a big hit.

Output tokens from Intelligence Index:

- grok 4.6 (xhigh): 97M (for 44 score)

- grok 4.7 (xhigh): 240M (for 46 score)

Pacing the frontier, with an aggressive release cadence. Gotta love the US tech industry.