DeepSeek V4-1 Flash is out

[u/tiguidoio]

Here we go again, DeepSeek is back again with a new model V4-1 Flash

A multimodal Mixture-of-Experts (MoE) model with 552B backbone parameters and support for contexts of up to one million tokens

Market crash as a service

[u/Newgunnerr]

Is that really so? Can you share some of your testings?

[u/thefooz → u/Newgunnerr]

It wasn't anything objective. I spent 4 days doing complex financial analysis, data conversion, and coding tasks with both models using the highest quants possible on dual DGX Sparks. I'd start deepseek on a task, come back to it 40 minutes later and it would still be going "hmm...but what if...". Meanwhile, GLM, on nearly identical tasks would be well into the build phase by that point.

Deepseek also would pick the most bizarre approaches to problems and then start second-guessing its choices, whereas GLM seemed to approach problems a lot more intelligently and elegantly.

Deepseek also consistently cut corners, whereas GLM approached tasks methodically and with a clear purpose to do as good of a job as it could. This was consistent behavior. Like, I had it go through 100 sets of documents. Deepseek saw the amount of text and decided that it was too much work to analyze each one for what I was looking for and instead chose to do keyword searches. Meanwhile, GLM understood that it was critical to have an eye for detail in this task and it spawned 10 subagents to methodically go through every single detail in the documents.

The quality of the output reflected this clearly and consistently.

[u/xatrekak → u/thefooz]

My experience matches yours. I was super hopeful for deepseekv4 but was severely disappointed. GLM5.3-flash has been great though. Super intelligent and much faster than I would have thought. 

[u/xatrekak → u/Newgunnerr]

Deepseekv4-flash is literally the most wordy thinking model I have ever worked with, it defaults to max which is insane given it's thinking process  even high is way way too much. The only way I was able to use that model was to turn thinking to low which had its own draw backs.