DeepSeek V3.1 Base : The ChatGPT killer is back
How to use DeepSeek V3.1-Base for free?
Some model drops are loud, with fireworks and blog posts and CEOs tweeting. DeepSeek V3.1? It just appeared on Hugging Face like it overslept the announcement. Around August 19–20, someone in a WeChat group quietly posted a link. No big LinkedIn posts. No press briefings. But the model? A beast.
https://medium.com/media/52d94bb48ebd2e4f20a361f76200395d/href
A 685B-parameter base model that doesn’t care for drama, just performance.
Model Context Protocol: Advanced AI Agents for Beginners (Generative AI books)
So what’s under the hood?
First thing that jumps out: the model is massive. 685 billion parameters massive. But before you picture all of that firing at once, here’s the trick, only 37 billion are active at a time, thanks to the Mixture-of-Experts (MoE) setup.
And unlike earlier versions of DeepSeek where you’d get a different model depending on the task (one for chat, one for coding, one for reasoning), this one blends it all together. One model. All jobs. Chatting, coding, step-by-step logic chains, it handles them with the same neural blood.
It also stretches its memory: 128,000 tokens of context. That’s entire novels at once. You could throw a hundred-page technical doc at it, and it wouldn’t blink.
The real tricks: MoE, MLA, MTP, and FP8
If you go at the specs, you’ll notice a few less-hyped-but-crucial features.
- Multi-head Latent Attention (MLA): This one’s not fully explained in public docs, but based on the name, think of it as a way for the model to internally focus across more abstract layers of meaning, not just word to word, but idea to idea.
- Multi-Token Prediction (MTP): Instead of predicting one word at a time like a school kid reading slowly, MTP lets it guess multiple tokens together. Faster. Smarter.
- Precision formats: The training involved F8_E4M3 (a kind of FP8) alongside BF16 and F32. Basically, they shaved down the compute cost using lighter formats without making the model dumb. Training something this big for just $5.6 million, on 2.8 million H800 GPU hours, is criminally efficient.
Performance and Cost: It’s Not Just Big, It’s Cheap
On the Aider benchmark, which is tailored toward evaluating coding assistants, DeepSeek V3.1 scored 71.6% , better than Claude Opus 4 by about 1%. That’s a nice brag. But here’s where it hurts:
DeepSeek did it 68x cheaper. One task costs about $1. Claude Opus 4 costs like it’s selling you gold-plated completions.
And yeah, it’s not just benchmarks. People testing it on real-world code gen, debugging, or even enterprise tasks seem to agree — it’s sharp. Especially for dev workflows where accuracy matters but so does your monthly bill.
What’s New? Tokens That Hint at the Future
Some new tokens were spotted inside: <|search_begin|> and <think>. Not decoration. These look like hints at internal search routines and chain-of-thought reasoning. DeepSeek isn’t just going wider, it’s digging deeper.
Also interesting: they’ve pulled the “R1” label off the online UI. Could be a sign they’re moving to fully hybrid inference, no more flipping between reasoning or coding-specific modes. Just one brain doing everything.
The Open Source Play
The entire base model is MIT-licensed and up on Hugging Face. That’s about as open as it gets , commercial use, remixing, rehosting, whatever. While there’s no official API (yet), a bunch of third-party platforms have already jumped on it.
deepseek-ai/DeepSeek-V3.1-Base · Hugging Face
And it exploded in popularity immediately. Hugging Face trending board, Reddit discussions, Twitter (or X or whatever people call it now), it’s making rounds. Especially in open-source AI circles where Claude and GPT-4 are still locked behind paywalls or eval-only toys.
The R2 Delay: What’s Going On?
There’s a bit of backstory here. DeepSeek was supposed to follow up with a next-gen reasoning model, R2. But things hit a wall. Rumors say training R2 on Huawei’s Ascend AI chips didn’t go well. Overheating? Tooling issues? Not clear. But the delay could explain why V3.1 was fast-tracked
Some Complaints
Despite the benchmarks, some early users aren’t fully sold. A few say it doesn’t feel that much smarter than R1 when it comes to reasoning. Some even claimed the text generation dipped a bit for open-ended tasks. Could just be noise. Could be growing pains. Either way, it’s not flawless.
The Bigger Picture
DeepSeek V3.1 isn’t just a model, it’s a shot across the bow. OpenAI and Anthropic aren’t going to be happy about a near-Opus-level model that’s open, cheap, and gaining traction. And in China’s own AI race, this is a direct challenge to big names like Alibaba’s Qwen.
Recap
- Release: Around August 19–20, 2025, soft-launch style
- Parameters: 685B (but only 37B active per token)
- Context Window: 128,000 tokens
- Architecture: Hybrid MoE with MLA + MTP tricks
- Training Cost: $5.6M (on 2.788M H800 GPU-hours)
- Precision Formats: FP8, BF16, F32
- Benchmarks: Aider score 71.6% — beats Claude Opus 4 by 1%, and 68x cheaper
- License: MIT (open and commercial friendly)
- API: None official, but available via third parties
- Special Tokens: <|search_begin|>, <think> spotted
- Knowledge Cutoff: July 2025
- Feedback: Strong for coding, mixed on reasoning
DeepSeek V3.1 Base : The ChatGPT killer is back was originally published in Data Science in Your Pocket on Medium, where people are continuing the conversation by highlighting and responding to this story.
