---
title: "DeepSeek R1 vs OpenAI o1: Reasoning Model Comparison 2026"
description: "Compare DeepSeek R1 vs OpenAI o1 reasoning models — pricing, benchmarks, chain-of-thought quality, and cost-effectiveness for complex tasks."
keywords: "DeepSeek R1 vs o1, DeepSeek R1 comparison, OpenAI o1 pricing, reasoning AI model, chain of thought AI"
date: "2026-05-07"
slug: "deepseek-r1-vs-o1"
---

# DeepSeek R1 vs OpenAI o1: Reasoning Model Comparison

Reasoning models represent the next frontier in AI. Both DeepSeek R1 and OpenAI o1 use chain-of-thought techniques to tackle complex problems, but they differ significantly in pricing, accessibility, and approach. This comparison helps you choose the right reasoning model for your needs.

## Pricing Comparison

| Model | Input Price | Output Price | Reasoning Tokens | Cached Input |
|-------|-------------|--------------|------------------|-------------|
| DeepSeek R1 | ¥4/M tokens | ¥16/M tokens | Included in output | ¥1/M tokens (75% discount) |
| OpenAI o1 | $15/M tokens | $60/M tokens | Included in output | $7.50/M tokens (50% discount) |
| OpenAI o1-mini | $3/M tokens | $12/M tokens | Included in output | $1.50/M tokens |

### USD Equivalent Pricing

| Model | Input Price (USD) | Output Price (USD) | Relative Cost |
|-------|-------------------|-------------------|---------------|
| DeepSeek R1 | ~$0.56/M | ~$2.22/M | 1x (baseline) |
| OpenAI o1 | $15/M | $60/M | ~27x more expensive |
| OpenAI o1-mini | $3/M | $12/M | ~5.4x more expensive |

DeepSeek R1 is approximately **27x cheaper** than OpenAI o1 and **5.4x cheaper** than o1-mini.

## Benchmark Comparison

| Benchmark | DeepSeek R1 | OpenAI o1 | OpenAI o1-mini |
|-----------|-------------|-----------|----------------|
| MMLU | 90.8% | 92.3% | 88.1% |
| MATH | 79.8% | 96.4% | 90.0% |
| GPQA (Diamond) | 71.5% | 79.2% | 63.6% |
| LiveCodeBench | 65.8% | 72.0% | 62.5% |
| AIME 2024 | 79.8% | 83.6% | 70.0% |
| CodeForces Rating | ~1800 | ~2000 | ~1600 |

OpenAI o1 leads on most reasoning benchmarks, particularly in mathematics. However, DeepSeek R1 remains highly competitive — especially considering it's open-source and 27x cheaper.

## Reasoning Approach Comparison

### DeepSeek R1

- **Explicit chain-of-thought**: Full reasoning traces visible in outputs
- **Reinforcement learning trained**: Uses RL to develop reasoning strategies
- **Cold start → warm start**: Started from DeepSeek V3, then RL fine-tuned
- **Distilled variants**: Smaller distilled versions (1.5B to 70B) available
- **Open source**: Full model weights available under MIT license

### OpenAI o1

- **Hidden chain-of-thought**: Reasoning traces are internal and not exposed
- **Proprietary training**: Training methodology not publicly disclosed
- **Reasoning effort control**: Can set reasoning effort (low/medium/high)
- **Structured outputs**: Supports JSON schema in reasoning mode
- **Closed source**: Available only via OpenAI API

## Cost Analysis: Reasoning Workloads

Reasoning models generate many "thinking tokens" that are billed as output tokens. Here's a real-world cost comparison:

### Scenario: Solving 1000 Math Problems

Assuming average 2K input + 8K reasoning + 2K output tokens per problem:

| Model | Input Tokens | Output Tokens (incl. reasoning) | Total Cost |
|-------|-------------|-------------------------------|-----------|
| DeepSeek R1 | 2M | 10M | ~$23.20 |
| OpenAI o1 | 2M | 10M | ~$630 |
| OpenAI o1-mini | 2M | 10M | ~$126 |

**DeepSeek R1 saves $607 per 1000 math problems compared to OpenAI o1.**

### Scenario: Code Review for 100 PRs

Assuming average 10K input + 5K reasoning + 1K output tokens per PR:

| Model | Total Input | Total Output | Total Cost |
|-------|-----------|-------------|-----------|
| DeepSeek R1 | 1M | 6M | ~$15.30 |
| OpenAI o1 | 1M | 6M | ~$405 |
| OpenAI o1-mini | 1M | 6M | ~$75 |

## DeepSeek R1 Distilled Models

One unique advantage of DeepSeek R1 is the availability of distilled models that capture reasoning behavior in smaller packages:

| Distilled Model | Base Model | MATH Score | Speed | Cost (self-hosted) |
|----------------|-----------|-----------|-------|-------------------|
| DeepSeek-R1-Distill-1.5B | Qwen 1.5B | 28.9% | Very fast | ~$0.05/hr |
| DeepSeek-R1-Distill-7B | Qwen 7B | 46.2% | Fast | ~$0.20/hr |
| DeepSeek-R1-Distill-14B | Qwen 14B | 53.8% | Medium | ~$0.40/hr |
| DeepSeek-R1-Distill-32B | Qwen 32B | 63.2% | Medium | ~$0.80/hr |
| DeepSeek-R1-Distill-70B | Llama 70B | 69.4% | Slow | ~$1.60/hr |

These distilled models are free to self-host and provide reasoning capabilities at various quality/speed trade-offs.

## When to Choose DeepSeek R1

- Cost is a significant concern
- You need transparent reasoning traces (for debugging, education, or compliance)
- You want to self-host for data privacy
- You need distilled models for edge deployment
- You're doing Chinese language reasoning
- Open-source licensing is required

## When to Choose OpenAI o1

- You need the absolute best mathematical reasoning
- Your problems are extremely difficult (competition math, complex proofs)
- Budget is not a concern
- You need structured outputs with reasoning
- You're already in the OpenAI ecosystem
- You want controlled reasoning effort levels

## Hybrid Strategy

Many teams use a hybrid approach:

1. **Start with DeepSeek R1** for all reasoning tasks
2. **Escalate to OpenAI o1** only for problems where DeepSeek R1 fails
3. **Use DeepSeek R1 distilled models** for high-volume, simpler reasoning

This approach typically reduces reasoning costs by 80-95% while maintaining quality on critical tasks.

## Conclusion

DeepSeek R1 has democratized reasoning AI. At 1/27th the cost of OpenAI o1 with open-source weights, it makes chain-of-thought reasoning accessible to everyone. OpenAI o1 still leads on the hardest benchmarks, but for the vast majority of use cases, DeepSeek R1 delivers excellent reasoning at an unbeatable price.

**The practical recommendation**: Start with DeepSeek R1. Only escalate to o1 if you consistently hit quality limits that matter for your application.