DeepSeek R1 vs V3, V3 vs V3.1, V3.2 vs V3.1 (Guide)



DeepSeek has turned into a whole family of models instead of just “one big LLM.”
If you’re confused by names like R1, V3, V3.1, V3.2-Exp, you’re not alone.

This guide breaks it down into three clear comparisons:

  • DeepSeek R1 vs V3 – reasoning model vs general MoE model

  • DeepSeek V3 vs V3.1 – same base, but upgraded for agents & “Think” mode

  • DeepSeek V3.2-Exp vs V3.1 – same quality, much more efficient & cheaper

1. DeepSeek R1 vs V3

1.1 What they are designed for

DeepSeek-V3

  • A huge Mixture-of-Experts (MoE) language model with 671B total parameters, but only 37B active per token, so it’s powerful yet efficient.

  • Pre-trained on 14.8T tokens, then SFT + RL for general chat, coding, and knowledge tasks.

  • Aimed at being a top-tier general-purpose LLM that competes with leading closed models on benchmarks like MMLU and GPQA.

DeepSeek-R1

  • A reasoning-first model family: R1 is trained with heavy reinforcement learning to improve chain-of-thought and multi-step reasoning.

  • Released as fully open-source under MIT, along with several distilled smaller models (Qwen/Llama based).

  • Public benchmarks and DeepSeek’s own release note say R1 achieves performance comparable to OpenAI’s o1 on math, coding and reasoning tasks.

Key idea:

  • V3 = big, general MoE LLM for chat, code, knowledge.

  • R1 = dedicated reasoning engine tuned to “think” more deeply.

1.2 When to use R1 vs V3

Use DeepSeek-R1 when:

  • You care most about math proofs, step-by-step logic, bug-hunting, algorithmic reasoning.

  • You’re building agents that need to plan, reflect, and follow long chains of thought.

  • You want an MIT-licensed reasoning model you can distill and fine-tune aggressively.

Use DeepSeek-V3 when:

  • You need a strong general LLM for chat, writing, code, and broad knowledge.

  • You want high quality on standard benchmarks and good performance across many domains (not just math/code).

  • You’re okay with a more “balanced” style instead of maximum chain-of-thought.

You can also combine them:
Use V3 for general chat & routing, and call R1 only for hard reasoning steps.

2. DeepSeek V3 vs V3.1

DeepSeek-V3.1 is basically “V3 upgraded for agents and hybrid thinking.”

2.1 What changes from V3 to V3.1?

From DeepSeek’s official V3.1 release and config notes:

V3 (baseline)

  • 671B-param MoE, 37B active per token.

  • Strong benchmarks, long context, general chat/coding.

V3.1 adds:

  1. Hybrid thinking mode (Think / Non-Think)

    • One model, two behaviors:

      • Think mode → more deliberate reasoning (closer to R1-style).

      • Non-Think mode → faster, lightweight responses.

    • Controlled via chat template / API flags, not a separate model.

  2. Stronger agent & tool use skills

    • Post-training specifically to improve:

      • Tool calling

      • Multi-step agent tasks

      • Following structured protocols

    • Better suited as the “brain” of multi-tool agents than plain V3.

  3. Longer context & continued pretraining

    • V3.1 Base is V3 plus 840B extra tokens for long-context extension.

    • Context window extended to 128K tokens in official deployments.

  4. Tokenizer & template updates

    • New tokenizer config and chat template for more robust multi-turn behavior.

2.2 When to use V3 vs V3.1

Stay on V3 if:

  • You already deployed V3 and don’t need hybrid Think/Non-Think.

  • Your workload is simpler chat/coding with no complex agents.

Upgrade to V3.1 if:

  • You want better tool calling and agent behavior out of the box.

  • You like having both modes:

    • Fast, cheap non-Think for easy queries.

    • Slower Think for hard reasoning.

  • You need 128K context reliably for long documents or codebases.

3. DeepSeek V3.2-Exp vs V3.1

V3.2-Exp is mostly about efficiency, not a huge quality jump.

3.1 What is V3.2-Exp?

According to the official release and early analyses:

  • DeepSeek-V3.2-Exp keeps the same capability level as V3.1-Terminus (a strong V3.1 variant).

  • It introduces DeepSeek Sparse Attention (DSA):

    • Fine-grained sparse attention to reduce compute on long context.

    • Designed to keep almost the same output quality while cutting FLOPs.

3.2 Quality vs efficiency

  • Benchmarks show V3.2-Exp ≈ V3.1-Terminus on most tasks, sometimes slightly better.

  • It significantly reduces compute requirements for long-context use, especially with 128K contexts.

From the API/user side:

  • DeepSeek slashed API prices by more than 50% when V3.2-Exp launched.

    • Input tokens down to about $0.028 / 1M (with caching).

    • Output tokens around $0.42 / 1M—one of the cheapest high-context options available.

3.3 When to move from V3.1 to V3.2-Exp

Stay on V3.1 if:

  • You’re pinned to a specific checkpoint or behavior for compatibility.

  • You can’t change model IDs yet for regulatory or testing reasons.

Switch to V3.2-Exp if:

  • You want the same performance as V3.1-Terminus, but cheaper and faster.

  • Your workloads are long-context heavy (128K tokens often used).

  • API cost and throughput are important for your business.

In almost all new builds, V3.2-Exp is the better default than plain V3.1 because you get similar intelligence at much lower cost.

4. Quick Comparison Summary

DeepSeek R1 vs V3

  • R1 → open reasoning model, RL-optimized for chain-of-thought.

  • V3 → giant general MoE LLM for broad chat/coding/knowledge.

  • Use R1 for hardest reasoning; V3 for general use.

V3 vs V3.1

  • V3.1 = V3 + hybrid Think/Non-Think, better tool use, longer context, extra pretraining.

  • V3.1 is the natural upgrade if you care about agents and long context.

V3.2-Exp vs V3.1

  • Same capability level as strong V3.1, but with sparse attention and big efficiency gains.

  • API prices dropped by >50%, making V3.2-Exp one of the cheapest high-quality, high-context models available.