If you're looking for reason to be skeptical, look no further than the massive delta between the Terminal Bench 2.1 (92.8%) and the Terminal Bench 4 score (27.3%).
Terminal Bench 4 was released a couple weeks ago, so the difference you're seeing between the two scores can be interpreted as "how well does this model generalize to new problems"? More crudely: "how benchmaxxed is this model?"
This is a groundless criticism. TB2.1 is saturated. TB4 is not. Sol xhigh is 90% on TB2.1 but 37% on TB4. Is it also "benchmaxxed"?
Your assumption is that the benchmarks are essentially identical in difficulty, with the only difference being their age and thus whether they could have been trained on.
> Sol xhigh is 90% on TB2.1 but 37% on TB4. Is it also "benchmaxxed"?
A model that was released a couple months ago scores 50% higher than SWE-2, a model released today, on an out-of-sample benchmark. Can I say I’ve come out of this more impressed with Sol?
Like you said, TB2 is saturated. Nobody would bat an eyelash at 90%. And yet here comes SWE-2 coming off top rope with an emphatic 92.4%. this is the definition of bench maxxing.
Is Sol benchmaxxed? Of course it is. Altman was caught in previous attempts trying to game benchmarks, does anyone believe that he's found his moral compass and decided to stop exploiting as much as he can get away with?
> Altman was caught in previous attempts trying to game benchmarks
Sounds like something you just made up, or maybe you read it on some other Reddit/HN post and started repeating it because it aligned with your biases.
> does anyone believe that he's found his moral compass and decided to stop exploiting as much as he can get away with?
I don't think "OpenAI" is equivalent to "Sam Altman." I think if OpenAI was intentionally "benchmaxxing" purely for marketing purposes that information would leak, because OpenAI is full of good-faith researchers (although it can be difficult to avoid overfitting even if you're actually trying to improve the model's general abilities)
And lastly I think anyone can actually try Sol themselves and see that's it a good model, or if that's too subjective, it is clearly better than the previous version. The benchmarks are reflecting actual progress and anyone can verify this themselves.
I take it to mean the benchmarks are a marketing line item, as in, to sell this fucking thing you have to go out there and lie and the way everyone is lying is by doing exactly that, lying. They build for benchmarks and build benchmarks for builds.
You want to make money or not , motherfucker? That’s the game. If you have to literally concoct a fabricated bullshit story about how your model hacked its own computer, then go fucking do it. Trillions. Trillions of dollars is what they want, and to sit and think anything other than human nature is at work here can only be possible in the realm of truly delusional people. It’s a dirty world.
Anyways, the other takeaway is that they are having to LIE to make money on models which means commodification has already occurred and we’re in an entirely new phase.
Yeah, this echoes my thoughts. I will be very surprised if a model with 2.8T parameters reaches the intelligence and capabilities of 10T parameter models. RL can take things far, but not that far.
Closed weights AND benchmaxxed. Somehow this company raised 2bil at a 48bil valuation. Pure insanity. I feel bad for their investors (not really, but... Still). Andreessen Horowitz is being played like a fiddle.
> Andreessen Horowitz is being played like a fiddle.
Andreessen Horowitz is not being played like a fiddle here. This might be their only investment in a decade that isn’t entirely predicated on being a scam.
The cursor acquisition just shows that there’s always a dumber shithead out there. Though vaporizing Elon Musks money is about as pure of a good as there is out there these days.
I think I'm probably in the minority here, but for my line of work, the software engineering and coding is only a small part of the work. I write simulation software, so a deep understanding of physics, math, and how they can be applied to the software is absolutely crucial. I'm assuming this model is tuned to be more focused on SWE topics, and the very reason we seek "multidisciplinary" hires is the also why I actually need a jack-of-all-trades model to back my coding agents.
Where are the model stats? Is this open-weights? If not, why would I use this over DeepSeek Flash 4.1?
I think these competing labs need to realize that no one wants another closed-weight model provider... We aren't even happy with the two we have right now, and their days are entirely numbered. If DeepSeek 4.1 flash is really as good as it's benching, we're probably a month away from 1/3rd of users moving off the closed-weight models in favor of something they have more control over (or is cheaper).
The big labs love to release their new model and quantize after the first week. You don't have that problem using dirt cheap API rates on OpenRouter. DS 4.1 flash is also faster than fast mode Astra. OAI's subscription rates are good value, but now these new open-weight models are nearly as cheap on API usage rates. I honestly can't wait for the day we're not beholden to the two big labs anymore. No wonder there's so much fear pumping happening at the moment from Anthropic and their funded NGOs.
This really just exists so cognition can stop spending API tokens with Anthropic or OpenAI.
Basically any successful AI based service will do this because at scale the frontier models are expensive and you’ll have enough data to fine tune your own.
Same reason Harvey is doing models now and basically every other provider
> If DeepSeek 4.1 flash is really as good as it's benching, we're probably a month away from 1/3rd of users moving off the closed-weight models in favor of something they have more control over (or is cheaper).
Only in the world where the incumbents don't react. Eg if they saw lots of users moving away, they'd drop prices or do something else.
1M cached tokens on deepseek is $0.006, the big labs can't sell anywhere close to this, they have funders expecting returns and huge overhead.
btw I've had a ton of fun with the new deepseek today, I was waiting for my OpenAI 5h limit reset and decided to give it some problems for fun, got pretty great results. Tried some harder problems and still got great results. I don't expect it to be Sol class or anything but I really didn't expect it to be anywhere near this good so we'll see where it ends up. And it's really fun throwing crazy amount of tokens at the wall for ~free instead of watching the subscription limits tick closer while your agents churn away.
> we're probably a month away from 1/3rd of users moving off the closed-weight models in favor of something they have more control over (or is cheaper).
DS 4 Flash requires large amounts of memory to run at reasonable quants (I think a system with 160 GB or so). DS 4.1 Flash is even larger, I think around 250 GB.
Any DS versin is dumb when compared (in realworld tasks) to Astra/Opus 5, which means, one would spend thousands of dollars, and still need to rely on cloud services to do jobs that are not trivial.
Cognition, the same company that a few years ago demoed a coding bot purporting to be able to autonomously complete upwork tasks, but upon closer inspection was going off the rails and not even completing what was asked?
As others have mentioned this is post trained from Kimi k3, which is already quite capable, so it can't be that bad, but any claimed improvements in performance should be taken with a grain of salt.
A friend recently pushed me to try out their coding platform, Devin, after I decided to move away from Cursor. I had the same reaction: "What, the con artists from like 2024?" But after some cajoling, I gave it a shot and was pleasantly surprised. I guess they learned their lessons, grew up, and are doing good work now, maybe?
On the one hand I would have expected a completely new model, on the other hand it's an RL-ed K3 go Fable 5 capabilities, which demonstrate that this is probably possible, which is nice.
I pay for it (mostly because they grandfathered me from the old prices)!
I used to use windsurf as my main editor until they changed their pricing model. Now i use it just to burn my weekly tokens on fable/astra if i remember to that on a task and that's it.
SWE 1.6 was great for small tasks. Very fast and good enough. 1.7 was unusable for me. Took more time thinking than GLM 5.2 and seemed to be generally running in circles. I tried it but abandoned it.
As an Econ graduate, pretty cool seeing Pareto in the "AI-bro" zeitgeist. Slightly surreal watching a 1906 welfare economics idea get rediscovered as a plotting convention. The original, if anyone fancies 579 pages of Italian: https://archive.org/details/manualedieconomi00pareuoft. There is an English translation somewhere.
Pareto leaked out of the sociology/econ bubble a long time ago :) Pareto principle, Pareto efficiency, Pareto distribution have been in the pop-sci buzzwords for quite awhile, I probably encountered it first in the 4-Hour Workweek. I don't think you can read a self-help book without the author introducing it as a groundbreaking principle to live your life by.
Pareto frontiers are pretty commonly invoked to describe tradeoffs in computer science and have been for quite a while. I remember the term being used in one of my early algorithms courses to describe the tradeoff between data structures with fast writes, ones with fast reads and ones that tried to balance the two.
IIRC cognition boasted about hiring a lot of competitive programmers and algorithms experts back when they released Devin, so it tracks that they'd use the term.
It's interesting watching people throw about pareto frontiers sort of like how RF nerds approach the shannon limit (in a practical real world sense of the term, like charting possible modulations/data rates on a two way satellite modem's manufacturer datasheet).
Please correct me if I'm wrong, but this appears to require Devin to use? I'm disappointed to see I need to use a bespoke platform to interact with this agent, to the point that I probably won't be trying it.
But I don't want to use your CLI. I already have my own harnesses and workflows. The friction is too high to "just try out" a new model like this. It would be preferable if I can evaluate it over, say, open router like all the other models and then decide from there if it's worth downloading a bespoke tool chain for only 1 lab's models
I just gave it a try and it doesn't appear to be free, it used up some of my on demand usage. It does say 75% off though. Seems like for Pro subscribers SWE-1.7 is free, maybe SWE-2 is free for them?
If its weights are open, that covers a multitude of other sins. Sufficiently-strong performance on the part of the new model would justify adapting existing tools to work with it.
I'm using Codex, Gemini etc, they all have desktop apps and have a plan, how do i use SWE-2? Thats is a problem they have. I'm not about to switch out my workflow and plans with a shiny LLM that looks benchmaxxed and graph maxxed.
Yeah, I'd expect model performance to be super spiky on SWE work, at least they admit it with the name of the model. It's distilled from an already-distilled model.
Maybe still worth it if their "64% cheaper" figure holds.
I guess I don't. Does post-training from another (larger) model not fall under the umbrella of distillation? I'd imagine it leads to the same spiky-ness issues...?
I presume post training is significantly easier than the distillation/training the top Chinese labs are doing.
I wonder if, similar to the American labs, they'll become stingy with their weights once they start getting immediately undercut by a wave of slightly better derived models.
At work I setup a cloud worker, where i can spin up as many concurrent agents I want, with unlimited fable 5.1 (thanks employer!!).
I now just work from my phone, and speak into the agents as they run. I dont write code and I dont write documents. I work on very complicated distributed systems. I dont open my laptop most days. Its a legacy brick I carry around.
Some of my coworkers are still doing things by hand, and are working long hours to produce 25% of the output (when considering hours worked). I stay quiet with my setup. We are in the end times for this job for the people that can see clearly how to automate their own job
that's why after 1 year of product development of these AI 20x maxxed speed, we reached AGI 'wizards', there's really no difference in output, outstanding bugs no longer get solved and sites still suck, even doing things that were just regular development 20 years ago. Are you sure they aren't only producing 2.5% of your output that you manage just by farting into your phone? Are you sure it's 25% really? Seems way to high, days when I have diarrhoea my AI agents move even faster
It's not you, it's X... but what would you expect of a nepo-baby economy of little swines. This is like the nepo wet-dream on steroids. Incompetence and delulu
SWE-1.5 was surprisingly good when I used it last. I feel like Cognition is one of the solid players that’s flying a bit under the radar while Anthropic and OpenAI race to IPO.
I like Cognition as a company and hope they succeed. Seemingly excellent engineering org.
I used to really like Windsurf. (Now Devin. Kind of? But also now Antigravity.) I still use it as my editor but haven't touched the agent for a while simply due to the rise of Codex.
If you're looking for reason to be skeptical, look no further than the massive delta between the Terminal Bench 2.1 (92.8%) and the Terminal Bench 4 score (27.3%).
Terminal Bench 4 was released a couple weeks ago, so the difference you're seeing between the two scores can be interpreted as "how well does this model generalize to new problems"? More crudely: "how benchmaxxed is this model?"
This is a groundless criticism. TB2.1 is saturated. TB4 is not. Sol xhigh is 90% on TB2.1 but 37% on TB4. Is it also "benchmaxxed"?
Your assumption is that the benchmarks are essentially identical in difficulty, with the only difference being their age and thus whether they could have been trained on.
> Sol xhigh is 90% on TB2.1 but 37% on TB4. Is it also "benchmaxxed"?
A model that was released a couple months ago scores 50% higher than SWE-2, a model released today, on an out-of-sample benchmark. Can I say I’ve come out of this more impressed with Sol?
Like you said, TB2 is saturated. Nobody would bat an eyelash at 90%. And yet here comes SWE-2 coming off top rope with an emphatic 92.4%. this is the definition of bench maxxing.
> Sol xhigh is 90% on TB2.1 but 37% on TB4. Is it also "benchmaxxed"?
Yes.
Fixed benchmarks will be debunked eventually I'd think. Better to use synthetic problems.
Too easy to game the numbers, and too easy to baselessly accuse companies of gaming the numbers, not to mention how you even define that.
> Sol xhigh is 90% on TB2.1 but 37% on TB4. Is it also "benchmaxxed"?
Yes? Just like every single model from every single AI lab.
Is Sol benchmaxxed? Of course it is. Altman was caught in previous attempts trying to game benchmarks, does anyone believe that he's found his moral compass and decided to stop exploiting as much as he can get away with?
> Altman was caught in previous attempts trying to game benchmarks
Sounds like something you just made up, or maybe you read it on some other Reddit/HN post and started repeating it because it aligned with your biases.
> does anyone believe that he's found his moral compass and decided to stop exploiting as much as he can get away with?
I don't think "OpenAI" is equivalent to "Sam Altman." I think if OpenAI was intentionally "benchmaxxing" purely for marketing purposes that information would leak, because OpenAI is full of good-faith researchers (although it can be difficult to avoid overfitting even if you're actually trying to improve the model's general abilities)
And lastly I think anyone can actually try Sol themselves and see that's it a good model, or if that's too subjective, it is clearly better than the previous version. The benchmarks are reflecting actual progress and anyone can verify this themselves.
Yeah and Astra is much better still
I take it to mean the benchmarks are a marketing line item, as in, to sell this fucking thing you have to go out there and lie and the way everyone is lying is by doing exactly that, lying. They build for benchmarks and build benchmarks for builds.
You want to make money or not , motherfucker? That’s the game. If you have to literally concoct a fabricated bullshit story about how your model hacked its own computer, then go fucking do it. Trillions. Trillions of dollars is what they want, and to sit and think anything other than human nature is at work here can only be possible in the realm of truly delusional people. It’s a dirty world.
Anyways, the other takeaway is that they are having to LIE to make money on models which means commodification has already occurred and we’re in an entirely new phase.
For comparison Qwen 3.8-Flash-Next which runs in under 190GB of RAM locally scores 25.3% on terminalbench 4.0.
Those 27.3% are still in the ballpark of modern models:
- Sonnet 5 - 12.4%
- Luna - 17.3%
- Grok 4.6 - 20.3%
- Sol - 37.3%
- GLM 5.3 - 41.8%
- Opus 5 - 51.8%
This is why I find benchmarks absolutely worthless.
First, almost all models are within spitting distances of eachother.
Second, it never translates to being better for my own workloads.
You just need to make your own benchmarks.
When a benchmark becomes a target, it's no longer a good benchmark...
Yeah, this echoes my thoughts. I will be very surprised if a model with 2.8T parameters reaches the intelligence and capabilities of 10T parameter models. RL can take things far, but not that far.
Closed weights AND benchmaxxed. Somehow this company raised 2bil at a 48bil valuation. Pure insanity. I feel bad for their investors (not really, but... Still). Andreessen Horowitz is being played like a fiddle.
> Andreessen Horowitz is being played like a fiddle.
Andreessen Horowitz is not being played like a fiddle here. This might be their only investment in a decade that isn’t entirely predicated on being a scam.
The Cursor acquisition shows that it’s possible for these valuations to be justified. But Cursor was more successful and bent the truth much less.
While I wouldn’t expect anything good for Cognition’s fate, it’s a much safer bet than Thinking Machines, SSI, and some others.
Though they’ll be in big trouble if the more talented Chinese labs stop letting them repackage their work.
The cursor acquisition just shows that there’s always a dumber shithead out there. Though vaporizing Elon Musks money is about as pure of a good as there is out there these days.
I think I'm probably in the minority here, but for my line of work, the software engineering and coding is only a small part of the work. I write simulation software, so a deep understanding of physics, math, and how they can be applied to the software is absolutely crucial. I'm assuming this model is tuned to be more focused on SWE topics, and the very reason we seek "multidisciplinary" hires is the also why I actually need a jack-of-all-trades model to back my coding agents.
Where are the model stats? Is this open-weights? If not, why would I use this over DeepSeek Flash 4.1?
I think these competing labs need to realize that no one wants another closed-weight model provider... We aren't even happy with the two we have right now, and their days are entirely numbered. If DeepSeek 4.1 flash is really as good as it's benching, we're probably a month away from 1/3rd of users moving off the closed-weight models in favor of something they have more control over (or is cheaper).
The big labs love to release their new model and quantize after the first week. You don't have that problem using dirt cheap API rates on OpenRouter. DS 4.1 flash is also faster than fast mode Astra. OAI's subscription rates are good value, but now these new open-weight models are nearly as cheap on API usage rates. I honestly can't wait for the day we're not beholden to the two big labs anymore. No wonder there's so much fear pumping happening at the moment from Anthropic and their funded NGOs.
This really just exists so cognition can stop spending API tokens with Anthropic or OpenAI.
Basically any successful AI based service will do this because at scale the frontier models are expensive and you’ll have enough data to fine tune your own.
Same reason Harvey is doing models now and basically every other provider
> If DeepSeek 4.1 flash is really as good as it's benching, we're probably a month away from 1/3rd of users moving off the closed-weight models in favor of something they have more control over (or is cheaper).
Only in the world where the incumbents don't react. Eg if they saw lots of users moving away, they'd drop prices or do something else.
1M cached tokens on deepseek is $0.006, the big labs can't sell anywhere close to this, they have funders expecting returns and huge overhead.
btw I've had a ton of fun with the new deepseek today, I was waiting for my OpenAI 5h limit reset and decided to give it some problems for fun, got pretty great results. Tried some harder problems and still got great results. I don't expect it to be Sol class or anything but I really didn't expect it to be anywhere near this good so we'll see where it ends up. And it's really fun throwing crazy amount of tokens at the wall for ~free instead of watching the subscription limits tick closer while your agents churn away.
> we're probably a month away from 1/3rd of users moving off the closed-weight models in favor of something they have more control over (or is cheaper).
DS 4 Flash requires large amounts of memory to run at reasonable quants (I think a system with 160 GB or so). DS 4.1 Flash is even larger, I think around 250 GB.
Any DS versin is dumb when compared (in realworld tasks) to Astra/Opus 5, which means, one would spend thousands of dollars, and still need to rely on cloud services to do jobs that are not trivial.
Cognition, the same company that a few years ago demoed a coding bot purporting to be able to autonomously complete upwork tasks, but upon closer inspection was going off the rails and not even completing what was asked?
https://www.youtube.com/watch?v=tNmgmwEtoWE
As others have mentioned this is post trained from Kimi k3, which is already quite capable, so it can't be that bad, but any claimed improvements in performance should be taken with a grain of salt.
A friend recently pushed me to try out their coding platform, Devin, after I decided to move away from Cursor. I had the same reaction: "What, the con artists from like 2024?" But after some cajoling, I gave it a shot and was pleasantly surprised. I guess they learned their lessons, grew up, and are doing good work now, maybe?
Well the models did get better but yeah their early product was godawful
> SWE-2 is post-trained from Kimi K3
On the one hand I would have expected a completely new model, on the other hand it's an RL-ed K3 go Fable 5 capabilities, which demonstrate that this is probably possible, which is nice.
Not sure it matters when devin is the most consistently shit product I've used. And yes, I tried again, they wasted the money on the billboards.
I have not met a single person/company that uses Devin… does anyone here actually use it?
I pay for it (mostly because they grandfathered me from the old prices)!
I used to use windsurf as my main editor until they changed their pricing model. Now i use it just to burn my weekly tokens on fable/astra if i remember to that on a task and that's it.
Seems like benchmaxing? For example for Terminal-Bench 4 it doesn't have great results. And why not show other benchmarks?
Probably, FrontierCode is made by Cognition itself. The model also seems worse in every way than DeepSeek v4.1 Flash, launched today.
Also the submitter's account is very new which makes me suspicious of self-promotion.
SWE 1.6 was great for small tasks. Very fast and good enough. 1.7 was unusable for me. Took more time thinking than GLM 5.2 and seemed to be generally running in circles. I tried it but abandoned it.
Looking forward to 2 -- maybe it'll be usable
Wonder if this was the model that drove factoring the rsa-260
The write-up from yesterday was by somebody from cognition using Devin to translate existing cpu sieving methods to gpu and to optimize the gpu sieve.
As an Econ graduate, pretty cool seeing Pareto in the "AI-bro" zeitgeist. Slightly surreal watching a 1906 welfare economics idea get rediscovered as a plotting convention. The original, if anyone fancies 579 pages of Italian: https://archive.org/details/manualedieconomi00pareuoft. There is an English translation somewhere.
Pareto leaked out of the sociology/econ bubble a long time ago :) Pareto principle, Pareto efficiency, Pareto distribution have been in the pop-sci buzzwords for quite awhile, I probably encountered it first in the 4-Hour Workweek. I don't think you can read a self-help book without the author introducing it as a groundbreaking principle to live your life by.
Pareto frontiers are pretty commonly invoked to describe tradeoffs in computer science and have been for quite a while. I remember the term being used in one of my early algorithms courses to describe the tradeoff between data structures with fast writes, ones with fast reads and ones that tried to balance the two.
IIRC cognition boasted about hiring a lot of competitive programmers and algorithms experts back when they released Devin, so it tracks that they'd use the term.
It's interesting watching people throw about pareto frontiers sort of like how RF nerds approach the shannon limit (in a practical real world sense of the term, like charting possible modulations/data rates on a two way satellite modem's manufacturer datasheet).
Please correct me if I'm wrong, but this appears to require Devin to use? I'm disappointed to see I need to use a bespoke platform to interact with this agent, to the point that I probably won't be trying it.
SWE-2 is free to use for users like yourself for the next month, and almost all usage should be supported via our CLI (https://docs.devin.ai/cli)
:)
Disclaimer: I work at Cognition, although was not involved in SWE-2
But I don't want to use your CLI. I already have my own harnesses and workflows. The friction is too high to "just try out" a new model like this. It would be preferable if I can evaluate it over, say, open router like all the other models and then decide from there if it's worth downloading a bespoke tool chain for only 1 lab's models
I just gave it a try and it doesn't appear to be free, it used up some of my on demand usage. It does say 75% off though. Seems like for Pro subscribers SWE-1.7 is free, maybe SWE-2 is free for them?
Your own CLI? Not even a /v1/chat/completions API? Is your business model based on pretending LLMs are not an interchangeable commodity already?
If its weights are open, that covers a multitude of other sins. Sufficiently-strong performance on the part of the new model would justify adapting existing tools to work with it.
I'm using Codex, Gemini etc, they all have desktop apps and have a plan, how do i use SWE-2? Thats is a problem they have. I'm not about to switch out my workflow and plans with a shiny LLM that looks benchmaxxed and graph maxxed.
Well written and good diagrams. No idea the verity of the TMBB (trust me bro benchmarks) but it was pleasing to look at
"SWE-2 is post-trained from Kimi K3"
Yeah, I'd expect model performance to be super spiky on SWE work, at least they admit it with the name of the model. It's distilled from an already-distilled model.
Maybe still worth it if their "64% cheaper" figure holds.
I don't think you know what distill means
I guess I don't. Does post-training from another (larger) model not fall under the umbrella of distillation? I'd imagine it leads to the same spiky-ness issues...?
With the Devin subscription even at the 20$ plan, they offered unlimited SWE 1.7 usage. Wondering if they do the same for SWE 2.
SWE-2 is free for all subscribers on the CLI to try out for the next month :)
What about the gui/windsurf app? Same as cli?
I presume post training is significantly easier than the distillation/training the top Chinese labs are doing.
I wonder if, similar to the American labs, they'll become stingy with their weights once they start getting immediately undercut by a wave of slightly better derived models.
At work I setup a cloud worker, where i can spin up as many concurrent agents I want, with unlimited fable 5.1 (thanks employer!!).
I now just work from my phone, and speak into the agents as they run. I dont write code and I dont write documents. I work on very complicated distributed systems. I dont open my laptop most days. Its a legacy brick I carry around.
Some of my coworkers are still doing things by hand, and are working long hours to produce 25% of the output (when considering hours worked). I stay quiet with my setup. We are in the end times for this job for the people that can see clearly how to automate their own job
that's why after 1 year of product development of these AI 20x maxxed speed, we reached AGI 'wizards', there's really no difference in output, outstanding bugs no longer get solved and sites still suck, even doing things that were just regular development 20 years ago. Are you sure they aren't only producing 2.5% of your output that you manage just by farting into your phone? Are you sure it's 25% really? Seems way to high, days when I have diarrhoea my AI agents move even faster
please keep thinking this so i can relax with my automated job
It's not you, it's X... but what would you expect of a nepo-baby economy of little swines. This is like the nepo wet-dream on steroids. Incompetence and delulu
SWE-1.5 was surprisingly good when I used it last. I feel like Cognition is one of the solid players that’s flying a bit under the radar while Anthropic and OpenAI race to IPO.
I like Cognition as a company and hope they succeed. Seemingly excellent engineering org.
I used to really like Windsurf. (Now Devin. Kind of? But also now Antigravity.) I still use it as my editor but haven't touched the agent for a while simply due to the rise of Codex.