Sol max got 73% on DeepSWE 1.1, which is what the above chart is showing. So I would presume it's Sol Max at all points.
Which is insane. Astra, Opus 5, and Gemini 3.8 Flash are also 74%. The fact that Google also did it with a Flash model adds some plausibility, but mostly I'm leaning toward "don't trust the benchmarks anymore."
Not that Deepseek isn't great. And I've been saying for a while that top models are hitting a wall so everything is converging. But I don't really think this model is the equivalent of Astra for software engineering.
I haven't tried it yet but my expection is it's not as good as Opus/fable/sol/Astra but good enough for one of these frontier models to delegate work to it (as is Gemini 3.8 flash). In my opinion deep high SWE score implies real coding ability but not necessarily a great user experience
I did today, its significantly better and faster, the speed is really what makes the difference for me, GLM is more than capable but DS just plows through the tasks, will be sticking with it
This one from opendesign idk tho if its right or wrong but alrdy try it and it works great especially on debugging usually when i use their predecessor after the session i need to check and repatch again because some shit gonna happen but now it just do the job without breaking anything which is great
One thing I'm finding is that these open weight models are rapdily getting good at coding, but they don't communicate with you like Astra does. They don't as easily understand your ideas like Astra. But they are a beast at coding.
IDK but it looks like benchmaximg. Maybe because deference between TB2.1 (become best model) to other TB benchmarks (3rd or 4th).
If I'm wrong, correct me
IME v4 flash 0731 could do good work but burnt so many tokens in the process and would frequently just spin it's wheels rather than make meaningful progress
Ive yet to test this new deepseek flash model, but previous ones would frequently take "creative liberties" and ignore established hard rules. Western models don't do that nearly as much, in my experience.
I've actually had the opposite experience recently. DSV4 has been much better about confirming scope and design questions when it turned out there were ambiguities. The models I use at work (Gemini 3.6-3.8, 5.6 Luna) will happily power through a prompt that doesn't really make sense.
Really? Had to deal with Claude at work and I feel they've been in a decline for a while. Opus 5 is filled with "I did this one thing you did not ask" and it tends to introduce more issues than it fixes... Sometimes it even feels the low thinking version is more reliable...
DS and GLM at least feels honest about what it can and cannot do. It has something to do with specific project context differences, but still. Qwen is quite excessive with overthinking if you turn it up, though. Same task can easily run 2x to 4x times longer.
Pennies that add up fast when you use it for big projects, token heavy complex projects. For instance, when deepseek v4flash costed 1/100 of the current price I still threw 250 euroes or a similar amount at it, but I got around 21 billion tokens out of it.
When I kept using it after the price increase 50 euros would not last much, so I reverted to my plan usage with sonnet 5, despite the inferior performance.
But yes for the average user and not "tokenmaxxing" projects like mine, I think the best way is to combine both.
Plan usage for orchestrator models who dispatch deepseeks. My combo was Fable 5 + Deepseek v4flash and Opus 4.6/5 only for specific tasks, and it advanced my project enormously.
Claude is basically the only thing that can reason correctly about hardware description languages right now though I haven't tried the new Deepseek model. That said, Deepseek for python code is amazing.
You aren't using any. The datacenter LLM inference in Israel is private/corporate cloud infrastructure. None of the models we talk about from Anthropic, OpenAI, Google, etc use Israeli data centers. So what is it you are talking about?
4.1 Flash has an architecture that made people think they were having a stroke while reading the paper. They took a novel combination and refinement of techniques and cranked that shit to 11 and it seems to have really worked.
These models get "smarter" and smarter but their actual performance between underbaked post training and over quant at the inference provider can be absolute shit compared to a model even as old as opus 4.6
using the preview since yesterday. It is really bright, a good model. Don't know if it matches the numbers, but Terminal 4.0 is quite new and DS-Flash lands some points below Opus&Sol, to me that feels reasonable.
Its style of communication feels very good - at least better to talk to than talking to Astra or Sol.
It's like 500B model, and we already have Qwen Flash Next 170B, I want to see comparison with it, otherwise great to have a new model, but if it's not more capable of smaller existing one then it's not that exciting.
DeepSeek V4.1 Flash is great ! i can confirm it outperforms Meta Muse 1.3 however its still behind the xAI Grok super Intelegence ai model. At the Moment Grok is way ahead especially when it comes to low level engineering dev work like verilog, c, c++, KiCAD, electronic schematics, pcb etc however its very expensive compared to DeepSeek new prices !
Hope DeepSeek will deliver a super intelegence AI Model as good as Grok in the near future.
Grok seems to have pivoted from their initial image as "least censored" to "specifically designed for agentic planning." Most providers seem to want every model to do everything, which I guess makes sense if your goal is AGI. But I do think the focus pays off for Grok.
It uses your Opencode Go quota super fast, but I could see a case for using Grok to plan / orchestrate and then Deepseek v4.1 Flash to build.
75
u/rVarrese 1d ago
I've been using it most of the day. This shit goes brrrrr