When DeepSeek released R1 in January 2025, it was the first open-weights reasoning model to match OpenAI o1’s benchmark performance on mathematics and coding — and it arrived at a fraction of the training cost, with weights anyone could download and run locally. The comparison between DeepSeek R1 and OpenAI o1 is significant not just as a model comparison but as a signal about the state of AI development: frontier reasoning capability is no longer exclusive to well-funded American labs. This guide covers where each model genuinely excels, where they fall short, and how to choose between them for practical use.
What Both Models Do
DeepSeek R1 and OpenAI o1 are both reasoning models — they use extended chain-of-thought processing to think through problems before producing a final answer. Unlike standard language models that generate responses token by token with no explicit reasoning step, both models spend compute at inference time working through a problem, checking their reasoning, and correcting errors before committing to an answer. This “thinking before speaking” approach is why both models significantly outperform their non-reasoning counterparts on mathematics, science, and coding tasks that require multi-step logical inference rather than pattern recall.
The Benchmark Picture
On the benchmarks that matter most for reasoning models, DeepSeek R1 and OpenAI o1 are closely matched. On AIME 2024 (competition mathematics), R1 scores around 79% and o1 around 74% at the time of their comparison — R1 slightly ahead. On MATH-500 (a broad mathematics benchmark), both score above 97%, essentially tied. On Codeforces competitive programming, R1 scores around 96th percentile and o1 around 89th percentile — R1 ahead. On MMLU (broad knowledge), they are similar. On GPQA Diamond (PhD-level science), o1 leads. The overall picture is competitive parity, with R1 slightly stronger on pure mathematical reasoning and o1 slightly stronger on broad scientific knowledge.
These benchmarks were measured at the time of R1’s release in early 2025. Both models have since been superseded by newer versions — OpenAI’s o3 and o4-mini, and DeepSeek’s own R2 (released in 2026). For current state-of-the-art reasoning, those newer models are the relevant comparison. The R1 vs o1 comparison remains instructive as a case study in open-weights vs proprietary development, and R1 remains widely used because it is free to run locally.
The Open Weights Advantage
The most practically important difference between R1 and o1 is not performance but access. OpenAI o1 is available only through the API — you pay per token, your data passes through OpenAI’s servers, and you cannot inspect or modify the model. DeepSeek R1 is open weights: you can download the full model (or distilled versions), run it on your own hardware, and use it in any way you want with no usage fees and no data leaving your infrastructure. This is transformative for use cases where privacy matters (medical, legal, financial data), where cost at scale is a constraint, or where offline or air-gapped operation is required. The 70B version of R1 runs well on a machine with two A100 GPUs; the distilled 14B and 32B versions run on a single GPU with quantisation.
Cost Comparison
For API usage, DeepSeek R1 is dramatically cheaper than o1. DeepSeek’s API prices R1 at roughly $0.55 per million input tokens and $2.19 per million output tokens — compared to o1’s approximately $15 per million input tokens and $60 per million output tokens at peak pricing. That is roughly a 10–27x cost difference for equivalent reasoning capability on most tasks. For high-volume applications — running hundreds of thousands of queries — the cost difference translates directly to whether a product is viable. Many teams building on reasoning models have switched from o1 to DeepSeek R1 API purely on cost grounds, using the proprietary o-series only for tasks where the quality gap justifies the premium.
Figure 1 — DeepSeek R1 vs OpenAI o1: key comparison
How DeepSeek R1 Was Trained
DeepSeek’s technical report revealed that R1’s reasoning capability was developed using reinforcement learning from verifiable rewards — a training approach that bypasses supervised fine-tuning on human-generated chain-of-thought examples. Instead, R1 learned to reason by being rewarded for correct answers on problems with verifiable solutions (mathematics, code that compiles and passes tests). This approach is notable because it suggests reasoning capability can emerge from reward signals on correct outcomes without requiring labelled reasoning traces as training data. The model developed extended chain-of-thought reasoning, self-correction, and verification behaviours spontaneously through this process. The training cost was reported as approximately $5.6 million — dramatically lower than the estimated training costs for comparable OpenAI models, which sparked significant discussion about the efficiency of the American AI development approach.
DeepSeek R1 Distilled Models
Alongside the 671B MoE base model, DeepSeek released distilled versions trained to replicate R1’s reasoning capability at smaller parameter counts: R1-Distill-Qwen-1.5B, 7B, 14B, and R1-Distill-Llama-70B. These are remarkable because they retain much of R1’s reasoning capability at sizes that run on consumer hardware. The distillation process works by training smaller models (built on Qwen-2.5 and Llama-3 base architectures) on R1’s extended reasoning outputs — the smaller model learns to replicate the reasoning behaviour without needing the full 671B parameter capacity. The 7B distilled model already outperforms GPT-3.5-turbo on most reasoning benchmarks; the 14B and 32B versions are competitive with earlier GPT-4 class models on many tasks. R1-Distill-14B running on a single RTX 4090 with 4-bit quantisation performs comparably to OpenAI o1-mini on many reasoning benchmarks — free, private, and offline. The distillation approach transfers the reasoning behaviour from the large model to smaller architectures by training on R1’s chain-of-thought outputs, making the knowledge in the large model accessible without the compute cost of running it.
Thinking Visibility
One important UX difference: DeepSeek R1 shows its full chain-of-thought reasoning by default (the “thinking” tokens are visible in the model’s output), while OpenAI o1 hides the internal reasoning and shows only the final answer. For developers and researchers, seeing R1’s reasoning process is valuable — it makes the model’s logic transparent, which helps debug incorrect answers and understand where the reasoning went wrong. OpenAI has since added more reasoning visibility to newer models, but R1’s full thinking trace remains a distinctive feature that many practitioners find useful for understanding and validating the model’s behaviour on their specific use cases.
Which to Choose
For most use cases that need reasoning capability, start with DeepSeek R1 (via API or self-hosted) unless you have a specific reason to use o1. The performance is comparable on most tasks, the cost is dramatically lower, and the open weights give you options — local deployment, fine-tuning, quantisation, and no dependency on a single vendor’s pricing or availability. Use OpenAI o1 (or its successors o3 and o4-mini) when you need the convenience of OpenAI’s API infrastructure, when you need the absolute best performance on hard science tasks where o1 leads slightly, or when you are already deeply integrated with the OpenAI ecosystem and the integration cost of switching is higher than the cost difference. For privacy-sensitive use cases or high-volume applications, R1’s open weights and dramatically lower API cost make it the clear choice.
The DeepSeek R1 vs OpenAI o1 comparison ultimately matters most as a proof of concept: open-weights models can reach frontier reasoning capability, and the cost of developing them is lower than the proprietary development narrative suggested. Whether you use R1 directly or not, its existence changes the competitive landscape in ways that benefit everyone building with AI — it drives down proprietary API prices, expands access to reasoning capability for researchers and smaller teams, and demonstrates that the knowledge concentration advantage of well-funded labs is narrower than it appeared.
The broader competitive landscape has moved on since R1 and o1 — DeepSeek has since released R2, and OpenAI has released o3 and o4-mini — but the January 2025 moment when R1 matched o1 marked a permanent shift in how the industry thinks about open vs proprietary AI development. The gap between what you can run privately on your own hardware and what requires a proprietary API narrowed dramatically and has continued to narrow since.