The Complete Guide to DeepSeek Models: V3, R1, V3.1 and Beyond

The Complete Guide to DeepSeek Models: V3, R1, V4 and Beyond

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The Complete Guide to DeepSeek Models: V3, R1, V4 and Beyond

Understand the differences among DeepSeek-V3, R1, V3.1, V3.2, V4, and distilled models. Learn how to choose the right model and deploy them securely.

Authors

Sherlock Xu

Last Updated

April 24, 2026

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DeepSeek has emerged as a major player in AI, drawing attention not just for its massive 671B models like V3.1 and R1, but also for its suite of distilled versions. As interest in these models grows, so does the confusion about their differences, capabilities, and ideal use cases.

“Which DeepSeek model should I use?”

“What’s the difference between R1, V3 and V3.1?”

“Is R1-Zero better than R1?”

“Do I really need a distilled model?”

“What's new in DeepSeek-V4?”

These questions echo across developer forums, Discord channels, and GitHub discussions. And honestly, the confusion makes sense. DeepSeek’s lineup has expanded rapidly, and without a clear roadmap, it’s easy to get lost in technical jargon and benchmark scores.

In this post, we’ll break down the key differences and help you choose the right model for your needs.

DeepSeek-V3#

Let’s rewind to December 2024 when DeepSeek dropped V3. It's a Mixture-of-Experts (MoE) model with 671 billion parameters and 37 billion activated for each token.

If you’re wondering what Mixture-of-Experts means, it’s actually a cool concept. Essentially, it means the model can activate different parts of itself depending on the task at hand. Instead of using the entire model all the time, it “picks” the right experts for the job. This makes it not just powerful but efficient.

What's perhaps most remarkable about DeepSeek-V3 is the training efficiency. Despite its size, the model required only 2.788 million H800 GPU hours, which translates to around $5.6 million in training costs. To put that in perspective, training GPT-4 is estimated to cost between $50–100 million.

DeepSeek-V3 Base vs. Chat model#

DeepSeek-V3 comes in two versions: a Base and a Chat model.

The Base model is exactly what it sounds like - the foundation. During its pre-training phase, it essentially learns to predict what comes next in massive amounts of text. After creating this Base model, DeepSeek researchers took it through two different post-training regimes to create models with different capabilities (which leads to two other models: DeepSeek-V3 Chat model and R1).

The Chat model (aka DeepSeek-V3, and yes, the naming can be confusing) underwent additional instruction tuning and reinforcement learning from human feedback (RLHF) to make it more helpful, harmless, and honest in conversation. It is highly performant in tasks like coding and math, and even compares favorably to the likes of GPT-4o and Llama 3.1 405B.

For DeepSeek-V3-Base, researchers relied exclusively on plain web pages and e-books for training, without deliberately adding synthetic data. They did notice, however, that some crawled web pages used OpenAI-model-generated answers, which means the base model may have indirectly absorbed knowledge from other models. More importantly, during the pre-training cooldown phase, no synthetic OpenAI model outputs were intentionally included.

You can check their benchmark performance in the evaluation results and training details in the supplementary information in Nature.

Deploying DeepSeek-V3#

Both DeepSeek-V3 Base and Chat models are open-source and commercially usable. You can self-host them to build your own ChatGPT-level application.

DeepSeek-R1#

DeepSeek didn’t stop with V3. Just weeks later, they introduced two new models built on DeepSeek-V3-Base: DeepSeek-R1-Zero and DeepSeek-R1.

DeepSeek-R1-Zero: Learning without supervision#

DeepSeek-R1-Zero was trained using large-scale reinforcement learning (RL) without the usual step of supervised fine-tuning (SFT). In simple terms, it learned reasoning patterns entirely on its own, refining its abilities through trial and error rather than structured instruction.

While the results were remarkable, there were also trade-offs. R1-Zero occasionally struggled with endless repetition, poor readability, and even language mixing.

DeepSeek-R1: A more refined reasoning model#

To smooth out these rough edges, DeepSeek developed DeepSeek-R1 using a more sophisticated multi-stage training pipeline. This included incorporating thousands of "cold-start" data points to fine-tune the V3-Base model before applying reinforcement learning. The result was R1, a model that not only keeps the reasoning power of R1-Zero but significantly improves accuracy, readability, and coherence.

Unlike V3, which is optimized for general tasks, R1 is a true reasoning model. That means it doesn’t just give you an answer; it explains how it got there. Before responding, R1 generates a step-by-step chain of thought, making it especially useful for:

Complex mathematical problem-solving

Coding challenges

Scientific reasoning

Multi-step planning for agent workflows

According to the DeepSeek-R1 paper re-published in Nature and its supplementary information, R1’s training cost was the equivalent of just US$294K primarily on NVIDIA H800 chips. This builds on roughly $6 million spent to develop the underlying V3-Base model. R1 is also thought to be the first major LLM to undergo the peer-review process. This marks a rare moment of transparency in large-scale AI research.

Image Source: DeepSeek-R1 Supplementary Information

Performance-wise, R1 rivals or even surpasses OpenAI o1 (also a reasoning model, but does not fully disclose the thinking tokens as R1) in math, coding, and reasoning benchmarks. This makes it one of the most powerful open-source reasoning model available today.

Deploying DeepSeek-R1#

R1 is the engine behind the DeepSeek chat application, and many developers have begun using it for private deployments.

Deploy DeepSeek-R1Deploy DeepSeek-R1

Keep these tips in mind when using R1:

Avoid system prompts and make sure all instructions are included directly in the user prompt.

For math problems, add a directive like Please reason step by step, and put your final answer within \boxed{}.

Be aware that R1 may sometimes skip its reasoning process (i.e., outputting \n\n). To encourage thorough reasoning, tell the model to start the response with \n in your prompt.

See more recommendations in the DeepSeek-R1 repository.

Note: Compared with R1, DeepSeek-R1-0528 supports system prompts and you don’t need to use to force reasoning output. See details about DeepSeek-R1-0528 below.

DeepSeek-V3 vs. DeepSeek-R1: Which one should you choose?#

DeepSeek-V3 and DeepSeek-R1 are the go-to models for many engineers today, but they serve different purposes. If you’re unsure which model fits your needs, here’s a quick comparison to help you decide:

ItemDeepSeek-V3DeepSeek-R1

Base modelDeepSeek-V3-BaseDeepSeek-V3-Base

TypeGeneral-purpose language modelReasoning model

Response styleDirect answers (e.g., "The answer is 42")Step-by-step reasoning (e.g., "First, calculate X… then Y… so the answer is 42")

Parameters671B (37B activated)671B (37B activated)

ArchitectureMoEMoE

Context length128K128K

LicenseMIT & Model LicenseMIT

Best forContent creation, writing, translation, general Q&AComplex math, coding, research, logical reasoning, agentic workflows

Note that DeepSeek continues to actively update its models. Below are the latest versions of V3 and R1:

DeepSeek-V3-0324#

In March 2025, DeepSeek released a powerful new update: DeepSeek-V3-0324. While it uses the same Base model as DeepSeek-V3, the post-training pipeline has been improved, drawing lessons from the RL technique in DeepSeek-R1. This allows the new model to have better reasoning performance, coding skills and tool-use capabilities. In math and coding evaluations, DeepSeek-V3-0324 even outperforms GPT-4.5.

Deploy DeepSeek-V3-0324Deploy DeepSeek-V3-0324

DeepSeek-R1-0528#

In May 2025, DeepSeek released DeepSeek-R1-0528, a significant upgrade to the original R1 model. While built on the same V3 Base model, this version pushes reasoning and inference capabilities further by leveraging more compute and advanced post-training optimizations.

Deploy DeepSeek-R1-0528Deploy DeepSeek-R1-0528

What’s new in DeepSeek-R1-0528:

Stronger reasoning. R1-0528 shows a significant leap in reasoning quality. The average token usage during reasoning tasks nearly doubled, from 12K to 23K tokens per AIME question. Its overall performance now approaches that of leading models in mathematics, programming, and general logic, including OpenAI o3 and Gemini 2.5 Pro.

Reduced hallucination rate. Hallucination has been cut by 45–50% in tasks such as rewriting, summarization, and reading comprehension.

Function calling improvements. R1-0528 shows solid performance on Tau-Bench with scores of 53.5 (Airline) and 63.9 (Retail). Note that tool use is not currently supported with the thinking mode.

Vibe coding enhancements. In our experiments, R1-0528 generated more coherent and accurate frontend code. However, it’s still unclear how much it actually improved and it needs more experimentation.

Additional updates over the original R1 include:

System prompt support

No need to manually insert to force reasoning behavior.

DeepSeek-V3.1#

In August 2025, DeepSeek released DeepSeek-V3.1, a major update that combines the strengths of V3 and R1 into a single hybrid model. It features a total of 671B parameters (37B activated) and supports context lengths up to 128K.

Key takeaways:

Hybrid thinking mode: V3.1 can switch between “thinking” (chain-of-thought reasoning like R1) and “non-thinking” (direct answers like V3) just by changing the chat template. This means one model can cover both general-purpose and reasoning-heavy use cases.

Extended training: Built on DeepSeek-V3.1-Base, V3.1 went through a expanded long-context training process (630B tokens for the 32K extension phase and 209B tokens for the 128K phase).

Smarter tool calling: Thanks to post-training optimization, V3.1 is much stronger in tool usage and agentic workflows. It outperforms both DeepSeek-V3-0324 and DeepSeek-R1-0528 in code agent and search agent benchmarks.

Image Source: DeepSeek-V3.1 release notes

Faster reasoning: DeepSeek-V3.1-Think achieves quality comparable to DeepSeek-R1-0528, but responds more quickly. Their internal tests show that after chain-of-thought compression training, V3.1-Think reduces output tokens by 20–50% while maintaining almost the same average performance.

Image Source: DeepSeek-V3.1 release notes

DeepSeek-V3.1 vs. DeepSeek-V3-0324 vs. DeepSeek-R1-0528#

Here is a side-by-side comparison:

ItemDeepSeek-V3.1DeepSeek-V3-0324DeepSeek-R1-0528

Base modelV3.1-BaseV3-BaseV3-Base

Parameters671B660B685B

Context length128K128K128K

ModeHybrid: Thinking (CoT) & Non-Thinking (direct)Non-thinking (general-purpose)Thinking (CoT reasoning)

Tool and agent useStrongest among the three; best code & search agent resultsGood, stronger than original V3Improved function calling; search/tool calling not supported under thinking mode

Response styleFlexible — fast direct answers or step-by-step reasoningDirect answersDetailed reasoning chains with higher token usage

Performance highlightsComparable reasoning to R1-0528 but faster; reduced CoT tokens by 20–50%Better coding & math than original V3 and GPT-4.5Strongest step-by-step reasoning; reduced hallucinations

LicenseMITMITMIT

Best forTeams needing both speed & reasoning in one modelGeneral-purpose workloads like content creation and Q&A, with stronger reasoning ability for tasks like coding/mathComplex math, coding, and reasoning tasks which require deep step-by-step logic

In short, DeepSeek-V3.1 is the most versatile DeepSeek model yet. It is capable of acting like V3 when you want fast, direct outputs, or like R1 when you need step-by-step reasoning. If you need both speed an

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