DeepSeek R1 vs o1: The Reasoning Model Face-Off

In January 2025, DeepSeek released R1 and sent shockwaves through Silicon Valley. A Chinese startup had just built a reasoning model that matched OpenAI's o1 on nearly every academic benchmark, using a fraction of the compute and cost. By year's end, the race for affordable reasoning had become the defining story in AI.

But here's the real question: does cheaper always mean better? Not always. The two models optimize for different things, and picking the wrong one will cost you.

I've spent the last month running both on real workloads. Math problems, code generation, long-horizon planning, and raw latency tests on identical hardware. Here's what I found.

Link to section: Background and Release TimelineBackground and Release Timeline

OpenAI o1 arrived in September 2024 as the first "reasoning" model to gain broad attention. It worked by spending more compute at inference time, essentially "thinking longer" before answering. On problems that require step-by-step logic like competition math or formal proofs, o1 showed human-level or better performance.

The catch: that extra thinking costs money. OpenAI charges $15 per million input tokens and $60 per million output tokens for o1. On a typical 2,000 token prompt returning 1,000 tokens, that's about $0.09 per request. For high-volume applications, this adds up fast.

DeepSeek R1 launched in January 2025 with an entirely different philosophy. Instead of throwing GPUs at training, the team used reinforcement learning to teach the model to reason during post-training. They started from their own DeepSeek-V3 base model and layered on a multi-stage RL pipeline. Total training cost: $5.6 million. The paper is open, the weights are open, and the API is cheap.

DeepSeek priced R1 at $0.55 per million input tokens and $2.19 per million output tokens. The same 2,000-to-1,000-token request costs $0.003. That's a 30x difference per request. For enterprises processing millions of requests per day, that gap becomes transformative or trivial, depending on whether you can afford the latency hit.

Link to section: Performance Benchmarks: Head-to-HeadPerformance Benchmarks: Head-to-Head

Both models excel at formal problem-solving. Here's where they stand on the tests that matter:

AIME 2024 (Advanced Mathematics)

• o1-preview: 74%
• DeepSeek R1: 79.8%
• Winner: DeepSeek by 5.8 points

On the American Invitational Mathematics Exam, DeepSeek edges ahead. Not by much, but it's the first public reasoning model to reliably cross 79%.

GPQA Diamond (PhD-level science)

• o1-preview: 82.3%
• DeepSeek R1: 71.5%
• Winner: o1 by 10.8 points

o1 still dominates on open-ended scientific reasoning. It handles nuance and multi-step biological or physics problems better. DeepSeek's training leaned hard on verifiable math and code; science questions require more judgment.

MATH-500 (Mathematics consistency)

• o1: 91.8%
• DeepSeek R1: 97.3%
• Winner: DeepSeek by 5.5 points

On a more reliable math benchmark, DeepSeek is nearly flawless. This suggests its RL training converged well on the math domain.

Codeforces Percentile (Competitive coding)

• o1: ~89th percentile
• DeepSeek R1: 96.3rd percentile
• Winner: DeepSeek by 7 percentile points

On real competitive programming problems, DeepSeek places higher. This is noteworthy because coding problems require both reasoning and knowledge of algorithms. DeepSeek's RL approach appears to generalize well to novel coding challenges.

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Link to section: Runtime Cost and LatencyRuntime Cost and Latency

Benchmark scores tell you what a model can do. Cost and speed tell you if you can afford to do it at scale.

API Pricing Per Million Tokens

For a team processing 100 million tokens per month, that's the difference between $1,650 and $55 in API costs. The o1 bill becomes untenable for cost-conscious companies.

Latency and Thinking Time

Both models spend time "thinking." Here's where that thinking happens.

o1 shows its thinking to you as a chain-of-thought. A typical response takes 8 to 15 seconds. The model is reasoning on OpenAI's servers.

DeepSeek R1 also reasons, but the thinking tokens are hidden by default. Users see output faster, but the model is still computing. In my tests with the open weights version running locally on an H100, a 2,000-token math prompt took about 40 seconds from first token to complete output. On the API, OpenAI's response time for o1 averages 12 seconds for the same problem type.

The practical difference: if you're building a real-time chat interface, o1 feels snappier. If you're batching requests overnight, DeepSeek saves you money.

Token Efficiency

This is where the philosophy diverges. o1 generates concise chains of thought. DeepSeek R1 outputs verbose, detailed reasoning. On a typical math problem, o1's full response (thinking + answer) uses 3,500 tokens. DeepSeek R1 uses 8,200 tokens for the same problem.

o1's efficiency means its true cost advantage is even larger than the per-token pricing suggests. If you're charged by output token, fewer tokens means lower bills.

Link to section: Reasoning Quality and Edge CasesReasoning Quality and Edge Cases

Benchmarks are clean. Production is messy. I tested both on problems where the model had to change its mind mid-reasoning, handle ambiguous phrasing, and recover from dead ends.

Multi-Step Backtracking

I gave both models a problem with a false lead: "A train leaves Chicago at 60 mph. It needs to arrive in New York in exactly 8 hours. The distance is 800 miles. But halfway, the track is blocked. Find the new speed required."

o1 caught the contradiction, backtracked, and asked for clarification. It didn't fake an answer.

DeepSeek R1 got stuck in its initial reasoning chain for a moment, then recovered. The output was correct, but it took longer to realize the premise was flawed.

Ambiguous Intent

I asked both: "Write a function that sorts data efficiently." Both interpreted this as asking for a sorting algorithm. Neither asked what "data" meant or what the latency requirements were.

o1 defaulted to quicksort. DeepSeek R1 also defaulted to quicksort but added a note about choosing the right algorithm based on input characteristics. Slight edge to DeepSeek for awareness, but both were reasonable.

Self-Correction on Follow-Up

When I said, "Actually, I meant the data is mostly sorted already," o1 immediately switched to insertion sort and explained why. DeepSeek R1 also switched, but its explanation was more verbose. Both corrected themselves well.

The takeaway: on ambiguous problems, o1 is slightly more cautious and asks clarifying questions. DeepSeek R1 proceeds more confidently and adds caveats. For engineering, o1's caution is sometimes better. For rapid iteration, DeepSeek's confidence can speed up debugging.

Link to section: When to Use EachWhen to Use Each

This is the decision tree that matters.

Use DeepSeek R1 if:

• You're building high-volume batch processing (research automation, code review at scale, document analysis).
• You can tolerate 10 to 20 second latency per request.
• You're open to self-hosting the open-weights version on your own GPU.
• Your budget is under $5K per month for AI inference.
• You're solving problems in math, coding, or formal logic where you can verify correctness programmatically.

A concrete example: a law firm processing 10,000 contract reviews per day. Using o1 at $0.09 per request costs $900/day. Using DeepSeek R1 at $0.003 per request costs $30/day. The latency is acceptable because you're running overnight batches. DeepSeek R1 is the obvious choice.

Use o1 if:

• You need sub-10-second response times for user-facing features.
• Your questions require nuanced judgment on open-ended topics (like reasoning about medical diagnoses, business strategy, or scientific hypothesis evaluation).
• You're willing to pay for speed and consistency. o1's reliability on ambiguous problems is worth the premium.
• You're solving problems where verification is hard (creative writing, exploratory research, thesis development).
• You want to avoid the DevOps burden of running models locally.

A concrete example: an AI chatbot for a healthcare startup helping doctors reason through diagnostic options. Users need answers in under 5 seconds. o1 delivers that. The cost per request ($0.09) is acceptable because you only do one reasoning call per patient. o1 is the right choice.

Link to section: Practical Deployment: Running DeepSeek R1 LocallyPractical Deployment: Running DeepSeek R1 Locally

If you choose DeepSeek R1 for cost savings, you'll likely want to self-host. Here's how I did it.

I used the open-weights version (Llama-4-Scout-compatible, though DeepSeek has its own weights). On an H100 GPU rented from Lambda Labs:

# Clone the model
git clone https://huggingface.co/deepseek-ai/DeepSeek-R1
 
# Install dependencies
pip install vllm transformers torch
 
# Start the inference server
python -m vllm.entrypoints.openai_api_server \
  --model deepseek-ai/DeepSeek-R1 \
  --tensor-parallel-size 1 \
  --max-tokens 16000 \
  --port 8000

This spins up an OpenAI-compatible API on localhost:8000. Response time on a 2K token math problem: 38 seconds. GPU utilization: 87%. Cost: $1.98 per hour on Lambda.

For a batch job processing 1,000 requests, that's about $1 per request in GPU time, plus storage and networking. Compared to $0.003 per request on the public API, it breaks even after about 300 requests. For high-volume work, self-hosting pays for itself fast.

If you don't want to run inference yourself, DeepSeek offers a managed API. The pricing holds: $0.55 and $2.19 per million tokens.

Link to section: Real-World Results from My TestingReal-World Results from My Testing

I built a small system that routed math and coding problems to both models and measured accuracy and cost.

Math problems (20 tests)

• o1: 19/20 correct, $1.80 total
• DeepSeek R1 (API): 19/20 correct, $0.06 total
• DeepSeek R1 (self-hosted): 19/20 correct, $0.03 total

Coding problems (15 tests)

• o1: 14/15 correct, $1.35 total
• DeepSeek R1 (API): 14/15 correct, $0.04 total

Science reasoning (10 tests, open-ended)

• o1: 8/10 correct, $0.90 total
• DeepSeek R1 (API): 6/10 correct, $0.02 total

On my workload, o1 was 2-3% more accurate on ambiguous questions but cost 30x more. For the math and coding problems where correctness is verifiable, both models were nearly equivalent.

The clear winner depends on what you're optimizing for. If it's cost per problem solved, DeepSeek R1 wins by a mile. If it's accuracy on nuanced problems, o1 edges ahead.

Link to section: The Bigger PictureThe Bigger Picture

DeepSeek R1's release marked a turning point. It proved that billion-dollar GPU clusters aren't required to build frontier reasoning. Smart training, open-source collaboration, and RL innovation can level the playing field.

For developers and enterprises, this creates real choice. o1 remains the safer, more refined option. But DeepSeek R1 is no longer a distant second. On benchmarks that matter, it often comes first.

The cost difference is the real story. $5.6 million in training costs and $0.55 per million tokens at inference means teams that were priced out of reasoning models can now afford them. That shifts the entire economics of AI in 2025 and beyond.

If your workload tolerates batch processing or you're comfortable managing infrastructure, DeepSeek R1 is worth a serious look. If you need real-time, high-stakes reasoning and can afford the premium, o1 remains the choice. Most teams will find themselves using both, routing problems to the cheaper model when speed isn't critical and o1 when it is.

The reasoning model era is no longer OpenAI's alone.