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DeepSeek-v3.1-Terminus

api-docs.deepseek.com

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Re: DeepSeek-v3.1-Terminus

#11
post #2

> What’s improved? Language consistency: fewer CN/EN mix-ups & no more random chars. It's good that they made this improvement. But is there any advantages at this point using DeepSeek over Qwen?

MIT license that lets you run it on your own hardware and make money off of it.

Qwen3 models (including their 235B and 480B models) use the Apache-2.0 license, so it’s not like that’s a big difference here.

Re: DeepSeek-v3.1-Terminus

#12
post #2

> What’s improved? Language consistency: fewer CN/EN mix-ups & no more random chars. It's good that they made this improvement. But is there any advantages at this point using DeepSeek over Qwen?

They seem fairly competitive with each other. You would have to benchmark them for your specific use case.

Re: DeepSeek-v3.1-Terminus

#13
post #2

> What’s improved? Language consistency: fewer CN/EN mix-ups & no more random chars. It's good that they made this improvement. But is there any advantages at this point using DeepSeek over Qwen?

I wish there was some easy resource to keep up with the latest models. The best I have come up with so far is asking one model to research the others. Realistically I want to know latest versions, best use case, performance (in terms of speed) relative to some baseline, and hardware requirements to run it.

have you tried https://artificialanalysis.ai/

Re: DeepSeek-v3.1-Terminus

#14
I tried V3.1 but it was driving me crazy by ignoring parts of user input, which R1 never did. I had many such instances when e.g. asking about running DeepSeek 671B it instead picked DeepSeek 67B because 671B is too large to exist so I must have made a mistake etc. I concluded that despite being better in benchmarks than R1, it was essentially useless due to this characteristics and I instead started using R1 at OpenRouter. Not sure why deepseek.com removed R1 and left only V3.1 without any ability to switch back, I guess it's cheaper to run.

Re: DeepSeek-v3.1-Terminus

#15
post #13

Earlier quoted context omitted.

I wish there was some easy resource to keep up with the latest models. The best I have come up with so far is asking one model to research the others. Realistically I want to know latest versions, best use case, performance (in terms of speed) relative to some baseline, and hardware requirements to run it.

have you tried https://artificialanalysis.ai/

Dumb collation of benchmarks that the big labs are essentially training on. Livebench.ai is the industry standard - non contaminated, new questions every few months.

Re: DeepSeek-v3.1-Terminus

#16
post #13

Earlier quoted context omitted.

have you tried https://artificialanalysis.ai/

Dumb collation of benchmarks that the big labs are essentially training on. Livebench.ai is the industry standard - non contaminated, new questions every few months.

Thanks! Are the scores in some way linear here? As in, if model A is rated at 25 and model B at 50, does that mean I will have half the mistakes with model B? Get answers that are 2x more accurate? Or is it subjective?

Re: DeepSeek-v3.1-Terminus

#19

sure would be neat if these companies would release models that could run on consumer hardware

So there are two ways to look at this - both hinge on how your define "consumer":

1) We haven't managed to distill models enough to get good enough performance to fit in the typical gaming desktop (say, 7B-24b class models). Even then though - most consumers don't have high end desktops, so even a 3060 class GPU requirement would exclude a lot of people.

2) Nothing is stopping you/anyone from buying 24ish 5090s (a consumer hardware product) to get the required ~600GB-1TB of VRAM to run unquantized deepseek except time/money/know how. Sure, it's unreasonably expensive but it's not like labs are conspiring to prevent people from running these models, it's just expensive for everyone and the common person doesn't have the funding to get into it.

Re: DeepSeek-v3.1-Terminus

#20

sure would be neat if these companies would release models that could run on consumer hardware

I'm using Qwen3Next on my MBP. It uses around 42GB of memory and, according to Aider benchmarks, has similar perf to GPT-4.1

https://huggingface.co/mlx-community/Qwen3-Next-80B-A3B-Inst...

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