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.
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 It's good with tool calls and doesn't think too much about everything, but it regularly uses outdated tool formats randomly instead of the standard JSON format.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.
For local runs, I made some GGUFs! You need around RAM + VRAM >= 250GB for good perf for dynamic 2bit (2bit MoE, 6-8bit rest) - can also do SSD offloading but it'll be slow.
Was that document almost exclusively written with LLMs? I looked at it last night (~8 hours ago) and it was riddled with mistakes, most egregious was that the "Run with Ollama" section had instructions for how to install Ollama, but then the shell commands were actually running llama.cpp, a mistake probably no human would make.
Do you have any plans on disclosing how much of these docs are written by humans vs not?
Regardless, thanks for the continued release of quants and weights :)
It’d also be great if you guys could do a fine tune to run on an 8x80G A/H100. These H200/B200 configs are harder to come by (and much more expensive).
DeepSeek is bad for hallucinations in my experience. I wouldn't trust its output for anything serious without heavy grounding. It's great for fantastical fiction though. It also excels at giving characters "agency".
The next cheapest and capable model is GLM 4.5 at $0.6 per million tokens in and $2.2 per million tokens out. Glad to see DeepSeek is still be the value king.
But I am sti disappointed with the price increase.
Sad to see the off peak discount go. I was able to crank tokens like crazy and not have it cost anything. That said the pricing is still very very good so I can't complain too much.
My experience is that gpt-oss doesn't know much about obscure topics, so if you're using it for anything except puzzles or coding in popular languages, it won't do well as the bigger models.
It's knowledge seems to be lacking even compared to gpt3.
just saw this on Chinese internet - deepseek officially mentioned that v3.1 is trained using UE8M0 FP8 as that is the FP8 to be supported by the next gen Chinese AI chip. so basically -
some Chinese next gen AI chips is coming, deepseek is working with them to get its flagship model trained using such domestic chips.
interesting time ahead! just imagine what it could do to NVIDIA share price when deepseek releases a SOTA new model trained without using NVIDIA chips.
not sure if its just chat.deepseek.com but one strange thing I've noticed is that now it replies to like 90% of your questions with "Of course.", even when it doesnt fit the prompt at all. maybe it's the backend injecting it to be more obedient? but you can tell it `don't begin the reply to this with "of" ending "course"` and it will listen. it's very strange
Some people on reddit (very reliable source I know) are saying it was trained on a lot of Gemini and I can see that. for example it does that annoying thing gemini does now where when you use slang or really any informal terms it puts them in quotes in its reply
It’s a very smart move for DeepSeek to put out an Anthropic-compatible API, similar to Kimi-k2, GLM4.5 (Puzzled as to why Qwen didn’t do this). You can set up a simple function in your .zhsrc to run Claude-Code with these models:
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[ 2.4 ms ] story [ 54.2 ms ] threadhttps://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.
Pricing: https://openrouter.ai/deepseek/deepseek-chat-v3.1
./llama.cpp/llama-cli -hf unsloth/DeepSeek-V3.1-GGUF:UD-Q2_K_XL -ngl 99 --jinja -ot ".ffn_.*_exps.=CPU"
More details on running + optimal params here: https://docs.unsloth.ai/basics/deepseek-v3.1
Was that document almost exclusively written with LLMs? I looked at it last night (~8 hours ago) and it was riddled with mistakes, most egregious was that the "Run with Ollama" section had instructions for how to install Ollama, but then the shell commands were actually running llama.cpp, a mistake probably no human would make.
Do you have any plans on disclosing how much of these docs are written by humans vs not?
Regardless, thanks for the continued release of quants and weights :)
But in the docs I see things like
Wouldn't this explain that? (Didn't look too deep)https://brokk.ai/power-ranking?version=openround-2025-08-20&...
$0.56 per million tokens in — and $1.68 per million tokens out.
But I am sti disappointed with the price increase.
I'll have to see how things go with this model after a week, once the hype has died down.
https://artificialanalysis.ai/models/deepseek-v3-1-reasoning
https://openrouter.ai/openai/gpt-oss-120b and https://openrouter.ai/deepseek/deepseek-chat-v3.1 for the same providers is probably better, although gpt-oss-120b has been around long enough to have more providers, and presumably for hosters to get comfortable with it / optimize hosting of it.
It's knowledge seems to be lacking even compared to gpt3.
No idea how you'd benchmark this though.
Let's hope not, because gpt-oss-120B can be dramatically moronical. I am guessing the MoE contains some very dumb subnets.
Benchmarks can be a starting point, but you really have to see how the results work for you.
some Chinese next gen AI chips is coming, deepseek is working with them to get its flagship model trained using such domestic chips.
interesting time ahead! just imagine what it could do to NVIDIA share price when deepseek releases a SOTA new model trained without using NVIDIA chips.
Some people on reddit (very reliable source I know) are saying it was trained on a lot of Gemini and I can see that. for example it does that annoying thing gemini does now where when you use slang or really any informal terms it puts them in quotes in its reply
https://github.com/pchalasani/claude-code-tools/tree/main?ta...