Every single model I've tested that is supposed to be anywhere near Opus, wasn't there. There is some magic sauce in Anthropic models that these benchmarks aren't demonstrating.
It's been suggested elsewhere that the AA-Omniscience benchmark [1] is more representative than the rest. It puts K3.1 about 6 months behind the frontier and the new version of DS 4.1-flash about 12 months. That roughly lines up with my gut feeling as well, at least when it comes to the trickier problems. Still rather impressive, but not quite frontier.
That definitely matches my experience, I have found Astra to be more capable than many other benchmarks show, have been very impressed with Argon for text, and generally like Sol. I find Haiku and Sonnet to be not worth my time as compared to the speed and cheapness of something like DeepSeek.
The only exception on the chart is DeepSeek - it scores exceptionally low there, which doesn't exactly track my experience. It usually does what you tell it to, and it's not exceptionally intelligent, but I do find it quite good at what it does (the bulk of code, scripting, etc). I know a lot of folks like it too ... curious if I'm just not exploring the simpler models enough?
Huh, this is an interesting hypothesis! It would make sense, to a degree. Part of "big model smell" to me is the model being able to pull a huge range of interesting algorithms, architectures, mathematics, and everything else that would come with greater world knowledge and having a much more sort of innate, like, full, well-rounded understanding of it instead of surface-level ICL pattern matching on web results.
Although even this one needs to be taken with a grain of salt. Fable/Opus still act very differently to Astra and Argon imo. But maybe it's just because I am used to these. I wouldn't clearly put them above each other though, although I still prefer anthropics models, but that could be simply out of comfort.
Maybe I need to try it again, but I have never liked the opus models.
I keep having problems with them not doing the thing I ask, (and also failing to call tools correctly in pi. I think they've very much trained to the harness, which is frustrating.).
It really depends on the type of task you're doing and how you're using it, because for the past two month the only reason I've had to use my company's paid Opus plan instead of my local Qwen-3.8-Next was speed. I use Qwen as a back-up plan when my token limit is reached and in my day to day work they have been indiscernible in terms of output.
I agree, but my point wasn't about being able to use a weaker model, it's about benchmarks not being in any way representative of the actual quality/level of the model. Of course for a lot of scenarios Opus is overkill and Qwen/DS are more than enough.
I think also heavily depends on what you're doing.
If you're in a stable codebase with lots of best practices, patterns, controls (linters, analyzers, etc.), examples already established, then any model will do ~the same plus/minus since there's not much novelty.
If you're building ground up, entirely novel, no reference, I think results will vary with lower capability models.
For me, I do a mix, leaving implementation to Haiku/Luna/DeepSeek and research/feedback/critique to Opus/Sol/Astra/Fable
A lot of these models are likely benchmaxxing the existing benchmarks that are out there. Some perhaps even by accident.
I think looking at some more niche benchmarks helps, I created one and it does show Kimi K3.1 Pro as the most intelligent of all open-weight models, though it is still significantly below Opus 5/5.5[1]
I think the secret sauce is about communication. It understands you and you understand it. Just a better coworker to work with. Benchmarks don’t measure that.
Yeah, but if you need to provide less details to Opus to get the same output it means the model is better at deducing your needs, which again implies it's a higher quality model.
Opus 5.5 is flat out amazing, particularly how cheap it is in practice (I'm on a subscription and use Claude Code and I almost never even approach the cap).
I would love a benchmark suite that better reflects real-world coding performance, as having Muse Wonder in the top three makes no sense to me.
I spent the last 4 months on a mix of chatgpt and glm building some prototypes of product ideas I've had for years because claude was in the phase of obnoxious conversation and verbosity.
I took the output and original specs and handed them to Claude today and the level of insight and concise detail in it's criticism and problem solving was fantastic. Dropped the others immediately
Same. Every time I prompt some other new/best/benchmaxxed model, I think to myself: wow, finally, this is amazing. And then I give the same thing to Opus and the bastard is always better. It's annoying to a point.
It's not just 5.5, every iteration of the frontier moving - Opus has had some edge. I'm sure it's not 100% in every scenario, I love interacting with Codex models more, but the quality of the output, in most scenarios that I've been exposed to, has been the best.
Anthropic cooked with Opus 4.5 and 5.5. Their other iterations have usually been nicer to interact with than GPT and more "autonomous", while being dumber.
I know the log scale of that chart is valuable when surveying the full breadth of possibilities, but I would encourage people to make decisions between nearby models based on linear scaling. Linear cost is often more useful for making the actual decision.
Would you please elaborate on "really decent"? Really decent for what use exactly? I want to love it (and use it as a cheaper research model in my harness), but I really cannot get any real development done with it? It stumbles and fumbles and over-complicates, where astra just plows ahead (often in less overall time) with excellent solutions.
I really want to love this model, particularly since it's good to have an open weight model on my team so to speak, but I think I might be holding it wrong.
I can have 2-3 sessions running at the same time for pretty much the entire working day with DSF4.1 where the responses are fast, cost is pretty negligible, and the results are good enough 95% of the time.
On an overall speed/cost/quality comparison, it seems unmatched in my experience these days.
I've been playing with the fast mode on BaseTen - clearly a different cost bracket, but WOW - the speed is phenomenal! A real game changer to be able to stay in flow.
For agentic coding, Qwen 3.8-27B is not far from that, and Qwen 3.8 Next is there. Never used DeepSeek 4.1 flash but I'd be surprised if it wasn't better than this.
If you have well-defined, long running tasks that are relatively disposable but requires constant ML monitoring and overwatch e.g. web crawlers, monitoring prices, logs etc. DS4 Flash is perfect for those.
For tasks like crawling and real time log analysis etc., the costs really add up at scale.
1. https://artificialanalysis.ai/evaluations/omniscience
The only exception on the chart is DeepSeek - it scores exceptionally low there, which doesn't exactly track my experience. It usually does what you tell it to, and it's not exceptionally intelligent, but I do find it quite good at what it does (the bulk of code, scripting, etc). I know a lot of folks like it too ... curious if I'm just not exploring the simpler models enough?
Although even this one needs to be taken with a grain of salt. Fable/Opus still act very differently to Astra and Argon imo. But maybe it's just because I am used to these. I wouldn't clearly put them above each other though, although I still prefer anthropics models, but that could be simply out of comfort.
I keep having problems with them not doing the thing I ask, (and also failing to call tools correctly in pi. I think they've very much trained to the harness, which is frustrating.).
If you're in a stable codebase with lots of best practices, patterns, controls (linters, analyzers, etc.), examples already established, then any model will do ~the same plus/minus since there's not much novelty.
If you're building ground up, entirely novel, no reference, I think results will vary with lower capability models.
For me, I do a mix, leaving implementation to Haiku/Luna/DeepSeek and research/feedback/critique to Opus/Sol/Astra/Fable
I think looking at some more niche benchmarks helps, I created one and it does show Kimi K3.1 Pro as the most intelligent of all open-weight models, though it is still significantly below Opus 5/5.5[1]
1 - https://bench.killswitch-lang.org/
I would love a benchmark suite that better reflects real-world coding performance, as having Muse Wonder in the top three makes no sense to me.
I took the output and original specs and handed them to Claude today and the level of insight and concise detail in it's criticism and problem solving was fantastic. Dropped the others immediately
I really want to love this model, particularly since it's good to have an open weight model on my team so to speak, but I think I might be holding it wrong.
What are you using it for?
On an overall speed/cost/quality comparison, it seems unmatched in my experience these days.
For tasks like crawling and real time log analysis etc., the costs really add up at scale.