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DeepSeek-v3.1

api-docs.deepseek.com

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Re: DeepSeek-v3.1

#11

Post by YetAnotherNick »

hodgehog11 wrote: post #2

For reference, here is the terminal-bench leaderboard: https://www.tbench.ai/leaderboard Looks like it doesn't get close to GPT-5, Claude 4, or GLM-4.5, but still does reasonably well compared to other open weight models. Benchmarks are rarely the full story though, so time will tell how good it is in practice.

Depends on the agent. Rank 5 and 15 are claude 4 sonnet, and this stands close to 15th.

Re: DeepSeek-v3.1

#12

Post by [deleted] »

yorwba wrote: post #10
seunosewa wrote: post #4

Earlier quoted context omitted.

The DeepSeek R1 in that list is the old model that's been replaced. Update: Understood.

Yes, and 31.3% is given in the announcement as the performance of the new v3.1, which would put it in sixteenth place.

[deleted]

Re: DeepSeek-v3.1

#13

Post by gunalx »

pdimitar wrote: post #8
bigyabai wrote: post #6

Earlier quoted context omitted.

Those Qwen3 2507 models are the local creme-de-la-creme right now. If you've got any sort of GPU and ~32gb of RAM to play with, the A3B one is great for pair-programming tasks.

Do you happen to know if it can be run via an eGPU enclosure with f.ex. RTX 5090 inside, under Linux? I'm considering buying a Linux workstation lately and I want it full AMD. But if I can just plug an NVIDIA card via an eGPU card for self-hosting LLMs then that would be amazing.

You would still need drivers and all the stuff difficult with nvidia in linux with a egpu. (Its not nessecarily terrible just suboptimal) Rather just add the second GPU in the Workstation, or just run the llm in your AMD GPU.

Re: DeepSeek-v3.1

#14

Post by bigyabai »

pdimitar wrote: post #8
bigyabai wrote: post #6

Earlier quoted context omitted.

Those Qwen3 2507 models are the local creme-de-la-creme right now. If you've got any sort of GPU and ~32gb of RAM to play with, the A3B one is great for pair-programming tasks.

Do you happen to know if it can be run via an eGPU enclosure with f.ex. RTX 5090 inside, under Linux? I'm considering buying a Linux workstation lately and I want it full AMD. But if I can just plug an NVIDIA card via an eGPU card for self-hosting LLMs then that would be amazing.

Sure, though you'll be bottlenecked by the interconnect speed if you're tiling between system memory and the dGPU memory. That shouldn't be an issue for the 30B model, but would definitely be an issue for the 480B-sized models.

Re: DeepSeek-v3.1

#15

Post by imachine1980_ »

amrrs wrote: post #9
coliveira wrote: post #7

Earlier quoted context omitted.

My personal experience is that it produces high quality results.

Any example or prompt you use to make this statment?

I remember asking for quotes about the Spanish conquest of South America because I couldn't remember who said a specific thing. The GPT model started hallucinating quotes on the topic, while DeepSeek responded with, "I don't know a quote about that specific topic, but you might mean this other thing." or something like that then cited a real quote in the same topic, after acknowledging that it wasn't able to find the one I had read in an old book. i don't use it for coding, but for things that are more unique i feel is more precise.

Re: DeepSeek-v3.1

#16

Post by pdimitar »

gunalx wrote: post #13
pdimitar wrote: post #8

Earlier quoted context omitted.

Do you happen to know if it can be run via an eGPU enclosure with f.ex. RTX 5090 inside, under Linux? I'm considering buying a Linux workstation lately and I want it full AMD. But if I can just plug an NVIDIA card via an eGPU card for self-hosting LLMs then that would be amazing.

You would still need drivers and all the stuff difficult with nvidia in linux with a egpu. (Its not nessecarily terrible just suboptimal) Rather just add the second GPU in the Workstation, or just run the llm in your AMD GPU.

Oh, we can run LLMs efficiently with AMD GPUs now? Pretty cool, I haven't been following, thank you.

Re: DeepSeek-v3.1

#17

Post by guluarte »

hodgehog11 wrote: post #2

For reference, here is the terminal-bench leaderboard: https://www.tbench.ai/leaderboard Looks like it doesn't get close to GPT-5, Claude 4, or GLM-4.5, but still does reasonably well compared to other open weight models. Benchmarks are rarely the full story though, so time will tell how good it is in practice.

tbh companies like anthopic, openai, create custom agents for specific benchmarks

Re: DeepSeek-v3.1

#18

Post by ivape »

seunosewa wrote: post #3

It's a hybrid reasoning model. It's good with tool calls and doesn't think too much about everything, but it regularly uses outdated tool formats randomly instead of the standard JSON format. I guess the V3 training set has a lot of those.

What formats? I thought the very schema of json is what allows these LLMs to enforce structured outputs at the decoder level? I guess you can do it with any format, but why stray from json?

Re: DeepSeek-v3.1

#19

Post by tomr75 »

bigyabai wrote: post #6
esafak wrote: post #5

It seems behind Qwen3 235B 2507 Reasoning (which I like) and gpt-oss-120B: https://artificialanalysis.ai/models/deepseek-v3-1-reasoning Pricing: https://openrouter.ai/deepseek/deepseek-chat-v3.1

Those Qwen3 2507 models are the local creme-de-la-creme right now. If you've got any sort of GPU and ~32gb of RAM to play with, the A3B one is great for pair-programming tasks.

With qwen code?

Re: DeepSeek-v3.1

#20

Post by decide1000 »

bigyabai wrote: post #6
esafak wrote: post #5

It seems behind Qwen3 235B 2507 Reasoning (which I like) and gpt-oss-120B: https://artificialanalysis.ai/models/deepseek-v3-1-reasoning Pricing: https://openrouter.ai/deepseek/deepseek-chat-v3.1

Those Qwen3 2507 models are the local creme-de-la-creme right now. If you've got any sort of GPU and ~32gb of RAM to play with, the A3B one is great for pair-programming tasks.

I use it on a 24gb gpu Tesla P40. Very happy with the result.
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