Are the DeepSeek V3 weights open and can I run it locally?

Yes, the weights are publicly available on Hugging Face under the deepseek-ai/DeepSeek-V3 repository. Be aware the full model is 685B parameters, so running it locally demands substantial GPU infrastructure.

What does DeepSeek V3 cost and what is its context window?

Via API it is priced at $0.32 per million input tokens and $0.89 per million output tokens, with a 163,840-token context window.

How does DeepSeek V3 differ from DeepSeek R1?

V3 is a general-purpose instruction-following model; R1 is DeepSeek's dedicated reasoning model that uses long chain-of-thought. V3 had some of R1's reasoning patterns distilled into it during post-training, giving it stronger reasoning than earlier V-series models without the verbose CoT output style of R1.

DeepSeek V3

3886°

#llm #deepseek #coding #reasoning #writing #local

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DeepSeek | 163,840 token context | $0.26/M input, $1.03/M output. DeepSeek-V3 is the latest model from the DeepSeek team, building upon the instruction following and coding abilities of the previous versions. Pre-trained on nearly 15 trillion tokens, the reported evaluations reveal that the model outperforms other open-source models and rivals leading closed-source models. For model details, please visit the DeepSeek-V3 repoOpens in new tab for more information, or see the launch announcementOpens in new tab.

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Captain's notes

Written by Captain, the house editor

Notes from 2026-10-13

DeepSeek V3 is a 671B Mixture-of-Experts model from DeepSeek, with only 37B parameters active per token, making it far cheaper to run than its total size suggests. It was trained on 14.8 trillion tokens and had reasoning patterns distilled from DeepSeek R1, lifting its performance noticeably above most open-weight competitors. At under a dollar per million output tokens via API, it suits developers and researchers who want frontier-class output without frontier-class bills.

Known for

Strong coding and instruction-following, refined across multiple DeepSeek generations
Reasoning ability boosted by distillation from DeepSeek R1's chain-of-thought
671B total parameters, only 37B active per token via MoE architecture
Open weights available on Hugging Face for self-hosting
Unusually low API price for a model at this performance tier

Watch out for

685B weights on Hugging Face: self-hosting requires serious hardware, not a consumer GPU setup.
DeepSeek has not published a standard open-source licence; check the repo terms before commercial use.
Context window via the API is 163,840 tokens; the model card lists 128K for the downloaded weights.

Best for

Prompts to try

Refactor a messy function

Here is a Python function that works but is hard to read and has no error handling. Refactor it for clarity, add type hints, handle the obvious failure cases, and explain each change you made. def proc(d, k, v): d[k] = v return d[k] * 2 if d[k] > 10 else d[k]

Reason through a logic puzzle

Five people each own a different pet and drink a different drink. Alice does not own the cat. The person who drinks tea owns the dog. Bob drinks coffee. Carol owns the fish. The person who drinks water is next to the person who owns the snake. Dave drinks juice. Who owns the snake, and how did you work it out?

Critique a technical argument

A colleague argues: 'We should always use a NoSQL database for new projects because SQL databases don't scale.' Write a structured critique of this claim. Identify what is correct, what is wrong, what context is missing, and what questions you would ask before choosing a database.

Compare with

DeepSeek V4.1 Flash
Open-weights model
1,048,576 token context · $0.04/M input, $0.68/M output
DeepSeek V4 Flash 0731
Open-weights model
1,048,576 token context · $0.01/M input, $1.28/M output
DeepSeek V4 Flash 0423
Open-weights model
1,048,576 token context · $0.09/M input, $0.18/M output
DeepSeek V4 Pro 0813
Open-weights model
1,048,576 token context · $1.32/M input, $3.96/M output

More text models from deepseek

Questions people ask

Are the DeepSeek V3 weights open and can I run it locally?: Yes, the weights are publicly available on Hugging Face under the deepseek-ai/DeepSeek-V3 repository. Be aware the full model is 685B parameters, so running it locally demands substantial GPU infrastructure.
What does DeepSeek V3 cost and what is its context window?: Via API it is priced at $0.32 per million input tokens and $0.89 per million output tokens, with a 163,840-token context window.
How does DeepSeek V3 differ from DeepSeek R1?: V3 is a general-purpose instruction-following model; R1 is DeepSeek's dedicated reasoning model that uses long chain-of-thought. V3 had some of R1's reasoning patterns distilled into it during post-training, giving it stronger reasoning than earlier V-series models without the verbose CoT output style of R1.

Captain's verdict
The most cost-effective way to get near-frontier text quality today, assuming you can live with DeepSeek's licence terms.

Model card on Hugging Face

Captain writes these notes from the model's own description and model card. Prices and context sizes are the provider's at the time of writing and can change.

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Typical model: The middle of the catalog: half the models with a figure grew faster over the period, half slower.

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