DeepSeek V3 vs DeepSeek R1: Standard vs Reasoning Model Comparison

TLDR

Choose DeepSeek V3 if you need: Faster responses, cheaper costs (2x less on V3, 21x less on V3.2), general-purpose versatility, and excellent AIME 2025 performance (96.0%).

Choose DeepSeek R1 if you need: Chain-of-thought reasoning, superior competitive programming (2029 Codeforces), complex multi-step logic, and transparent reasoning processes.

Budget: DeepSeek V3 ($0.27/$1.10 per million tokens) is 2x cheaper than R1 ($0.55/$2.19). V3.2 ($0.026/$0.39) is even cheaper.

Performance: V3.2 wins on AIME 2025 (96.0% vs 79.8%). R1 wins on competitive programming (2029 vs 51.6th percentile) and MATH-500 (97.3% vs 90.2%).

Overview

DeepSeek released two flagship models within weeks of each other, each optimized for different use cases.

DeepSeek V3, released in December 2024 (with V3.2 in 2025), is a standard Mixture-of-Experts model designed for fast, versatile performance across many domains. It achieved gold medal performance on IMO, CMO, ICPC, and IOI competitions while maintaining remarkably low costs.

DeepSeek R1, released on January 20, 2025, is a reasoning-first model that uses chain-of-thought processing to solve complex problems. Built on the V3 architecture, R1 adds explicit reasoning capabilities at the cost of slower responses.

Both models share the same MoE architecture (671B total parameters, 37B activated), but differ fundamentally in how they approach problems.

Basics: Model Specifications

Pricing: Cost Comparison

For a typical task using 100,000 input tokens and generating 10,000 output tokens:

• DeepSeek V3: $0.038 per request
• DeepSeek V3.2: $0.006 per request
• DeepSeek R1: $0.077 per request

DeepSeek V3.2 offers remarkable value: cutting-edge performance at costs 21x lower than R1 and dramatically lower than any OpenAI or Anthropic model.

Performance: Benchmark Comparison

Mathematical Reasoning

Surprisingly, V3.2 outperforms the reasoning model R1 on AIME 2025 by over 16 percentage points. However, R1 achieves higher scores on MATH-500, demonstrating the value of chain-of-thought for certain problem types.

General Knowledge

R1's reasoning approach gives it an edge in general knowledge, outperforming V3 by 2.3 percentage points.

Coding Performance

This is where reasoning makes the biggest difference. R1's 2,029 Codeforces rating is exceptional, nearly doubling V3's percentile ranking. Chain-of-thought reasoning excels at competitive programming challenges.

Competition Performance

DeepSeek V3 achieved gold medal level performance in 2025:

• International Mathematical Olympiad (IMO)
• Chinese Mathematical Olympiad (CMO)
• International Collegiate Programming Contest (ICPC)
• International Olympiad in Informatics (IOI)

These achievements demonstrate V3's versatility across multiple competition formats without needing explicit reasoning overhead.

Speed & Response Time

DeepSeek V3:

• Fast, standard inference
• No reasoning overhead
• Optimized for low-latency applications
• Better for real-time use cases and user-facing features

DeepSeek R1:

• Slower due to chain-of-thought processing
• Shows visible reasoning steps
• Extra time spent "thinking" before responding
• Better for tasks where accuracy matters more than speed

For applications requiring quick responses (chatbots, autocomplete, real-time suggestions), V3's speed advantage is significant.

Architecture: Same Foundation, Different Approach

Both models use the same Mixture-of-Experts architecture with 256 expert networks per layer, 671B total parameters, and 37B activated per token.

DeepSeek V3 processes inputs directly and generates outputs using standard transformer architecture.

DeepSeek R1 is built on top of V3 but adds chain-of-thought reasoning:

1. Receives input
2. Generates internal reasoning steps (visible to users)
3. Produces final answer based on reasoning

This reasoning layer adds computational overhead but improves accuracy on complex, multi-step problems.

When to Use Each Model

Use DeepSeek V3 when you need:

• Fast responses: Real-time applications, chatbots, autocomplete
• Lower costs: 2x cheaper than R1, or 21x cheaper with V3.2
• General versatility: Strong performance across many domains
• AIME 2025 excellence: 96.0% score, beating R1
• Competition-level math: Gold medal IMO, CMO performance
• No reasoning overhead: Direct answers without visible thinking steps

Use DeepSeek R1 when you need:

• Complex reasoning: Multi-step logic problems requiring explicit reasoning
• Competitive programming: 2,029 Codeforces rating (96.3rd percentile)
• MATH-500 excellence: 97.3% score, beating V3
• Transparent reasoning: See the model's thinking process
• Maximum accuracy: When you can sacrifice speed for correctness
• General knowledge: Higher MMLU score (90.8% vs 88.5%)

The Surprising AIME Result

One of the most interesting findings is that DeepSeek V3.2 (without reasoning) outperforms DeepSeek R1 (with reasoning) on AIME 2025 by 16 percentage points (96.0% vs 79.8%).

This suggests that:

1. Not all math problems benefit from chain-of-thought. Some problems are better solved with direct pattern matching.
2. Model training matters more than architecture. V3.2's training improvements may be more impactful than R1's reasoning layer.
3. Different benchmarks reward different approaches. R1 excels on MATH-500 (97.3%) where multi-step reasoning helps.

The takeaway: reasoning isn't always better, even for mathematics.

Both Models Are Open Source

Unlike comparisons between OpenAI and DeepSeek models, both V3 and R1 are fully open source under the MIT license.

This means you can:

• Self-host either model on your own infrastructure
• Fine-tune for specific domains or use cases
• Modify the model architecture or training approach
• Use commercially without licensing fees
• Compare models directly in your own environment

Orchestrate DeepSeek Models with Miniloop

DeepSeek V3 and R1 aren't competitors. They're complementary models optimized for different tasks within the same workflow.

With Miniloop, you can build AI workflows that intelligently route between DeepSeek models. Use V3 for fast data processing and general tasks, then switch to R1 when you hit complex reasoning problems. Or use R1 for competitive programming challenges while leveraging V3's speed for code generation.

Miniloop lets you:

• Route tasks to the right DeepSeek model based on complexity
• Use V3 for speed-critical steps, R1 for reasoning-critical steps
• Combine DeepSeek's low costs with other models (Claude, GPT-4o)
• A/B test standard vs reasoning approaches on your specific tasks
• Build hybrid workflows that optimize for both speed and accuracy

Stop choosing between fast and smart. Start building multi-model workflows with Miniloop.

Get Started with Miniloop →

Sources

• DeepSeek V3 Release
• DeepSeek V3.2 Release Notes
• DeepSeek V3.2 vs GPT-5 Comparison
• DeepSeek R1 on Hugging Face
• DeepSeek V3 vs R1 Analysis - PromptLayer