DeepSeek R1 vs OpenAI o1 : Which Reasoning Model Is Better for You?



“Reasoning models” are the new hot thing in AI, and two names keep coming up: DeepSeek R1 and OpenAI o1. Both are built to think step-by-step instead of just autocomplete text, but they’re very different in price, openness, and how you can use them.

This breakdown compares DeepSeek R1 vs o1 on:

  • Reasoning & benchmark performance

  • Speed, context window & output length

  • Cost

  • Openness & deployment options

  • Best use cases for each

1. Quick TL;DR

  • DeepSeek R1 – Open-weight reasoning model, MIT license, tuned heavily with RL for chain-of-thought. Performance is on par with o1 on many math/code benchmarks and beats it on some, while being much cheaper per token and fully self-hostable.

  • OpenAI o1 – Closed, API-only reasoning family from OpenAI. It outperforms R1 on some broad reasoning and general-knowledge evals and integrates tightly with the OpenAI ecosystem (tools, safety, platform), but it’s more expensive and not open-weight.

Think of R1 as the open, cheaper “thinking engine” and o1 as the premium, fully managed reasoning service.

2. Model Overviews

DeepSeek R1 in one paragraph

DeepSeek R1 is a family of open reasoning models trained with a “cold-start + RL” pipeline to boost chain-of-thought quality. The official release notes say R1 achieves performance comparable to OpenAI o1 across math, code and reasoning benchmarks, and DeepSeek released both R1 and distilled variants (Llama/Qwen based) under an MIT license, meaning you can use and commercialize them freely.

OpenAI o1 in one paragraph

OpenAI’s o1 series is a line of models “designed to spend more time thinking before they respond,” improving significantly over GPT-4o on hard reasoning tasks in math, coding, and PhD-level science questions.   o1 is closed-weight and available only through the OpenAI (and Azure) APIs, targeted at teams who want top-tier reasoning quality inside a managed platform.

3. Benchmarks: Who “Thinks” Better?

Different evaluations tell slightly different stories, but the pattern is:

Where DeepSeek R1 shines

Several benchmark reports and DeepSeek’s own materials show:

  • AIME 2024 (math) – R1 scores ~79.8%, slightly ahead of o1-1217 (~79.2%).

  • MATH-500 – R1 leads with 97.3% vs 96.4% for o1-1217.

  • SWE-bench Verified (software engineering) – DeepSeek reports R1 outperforming o1 on this code-fix benchmark.

So for math proofs and software engineering bugs, R1 often has a slight edge.

Where OpenAI o1 pulls ahead

Other comparisons find o1 in front on broader reasoning:

  • A Vellum.ai evaluation found o1 answered 18/27 reasoning questions correctly vs 11/27 for R1 (≈26% accuracy gap in that test).

  • A comparative study notes that o1 achieves the best results across almost all of their filtered benchmarks, significantly improving coding and math vs previous models.

  • One long-form comparison reports o1 slightly ahead on general-knowledge tasks like MMLU and on competitive programming (Codeforces), while R1 is slightly better on some math benchmarks.

Interpretation:

  • For specialized math and certain software tasks, DeepSeek R1 can match or beat o1.

  • For overall reasoning across many domains (math, code, science, GPQA, MMLU), most independent tests still give a small edge to o1.

4. Context Window, Speed & Output Length

A practical difference is how much they can process and how fast.

Context & output

  • Both DeepSeek R1 and o1-preview are often deployed with 128k token input context.

  • However, o1-preview can generate up to ~65k output tokens, while DeepSeek R1 typically caps output at around 8k tokens.

If you’re doing long derivations, extended code output, or huge chain-of-thought traces, o1 has more room to “talk.”

Speed

  • One comparison shows o1 completing tasks faster on average: e.g. o1 finishing a reasoning benchmark in ~77 seconds vs R1 taking longer, and another report notes o1’s higher generation speed (≈144 tokens/s vs ~37 tokens/s for R1 in the same setup).

So if latency really matters (interactive tools, live coding assistants), o1 is generally snappier at similar context sizes.

5. Cost: The Big Differentiator

This is where things become spicy.

  • A widely cited analysis (via Bernstein analysts, reported in multiple articles) estimates that DeepSeek R1’s per-token cost is up to ~96% lower than o1’s internal cost, largely thanks to its Mixture-of-Experts design and cheaper training stack.

  • Another pricing comparison (R1 vs GPT-4o) shows DeepSeek R1 is ~4.6× cheaper per input and output token than GPT-4o via one provider; since o1 is more expensive than GPT-4o, the R1 vs o1 gap is likely even larger.

On top of that:

  • DeepSeek R1 is open weight under MIT, so you can run it on your own GPUs or cloud at raw compute cost.

  • OpenAI o1 is API-only, so you pay whatever OpenAI (or Azure) charges per token.

Bottom line: if you need to run massive volumes of reasoning tokens (agents, pipelines, batch evals), R1 is often dramatically cheaper—especially if you self-host.

6. Openness, Control & Deployment

DeepSeek R1

  • Open-source weights under MIT – free to use, modify, and commercialize.

  • Available:

    • As downloadable checkpoints (Hugging Face, GitHub Models, etc.)

    • Through multiple third-party inference APIs and OSS UIs.

  • You can:

    • Run on-prem or VPC for data-sensitive workloads

    • Fine-tune or distill the model for your domain

    • Inspect its chain-of-thought traces more freely (subject to your own safety policies)

OpenAI o1

  • Closed, proprietary weights – you only access it through:

    • OpenAI API (platform.openai.com)

    • Azure OpenAI Service

  • Pros:

    • Production-grade infra, safety systems, monitoring, and rate-limiting

    • Tight integration with the rest of the OpenAI ecosystem (GPT-4o, o3-mini, tools, vector stores, etc.)

  • You trade control and transparency for convenience and reliability.

7. Which One Should You Choose?

Choose DeepSeek R1 if:

  • You want open weights for:

    • On-prem deployment

    • Regulatory/compliance reasons

    • Advanced customization and fine-tuning

  • Your workload is reasoning-heavy (math, proofs, bug-fixing, complex coding tasks), and you’re okay with slightly slower, but cheap and very capable reasoning.

  • You care a lot about cost at scale and want to minimize per-token spend.

Good fits: internal company copilots, research tools, agentic workflows, SaaS products that want a mostly OSS stack.

Choose OpenAI o1 if:

  • You want best-in-class reasoning on average across many domains, based on most independent tests.

  • You value higher speed, longer outputs, and a polished, hosted API.

  • You’re already tied into the OpenAI ecosystem (GPT-4o, o3-mini, Assistants API, tools) and prefer one vendor and one bill.

Good fits: customer-facing apps, mission-critical agents, enterprise products where you want maximum reliability and support, and you’re okay paying more.

8. Simple Decision Cheat-Sheet

  • Need open-source + low cost + strong math/code reasoning → pick DeepSeek R1.

  • Need top overall reasoning quality + speed + managed API → pick OpenAI o1.

  • Hybrid idea:

    • Use R1 for large-scale, low-stakes reasoning (batch analysis, internal tools).

    • Use o1 only for critical paths where you need maximum reliability or longer outputs.