💰 Unit Cost Winner DeepSeek R1 $0.55/1M in · $2.19/1M out
⚡ Latency & Speed Winner OpenAI o3 1150ms TTFT · 82 tok/s
💻 Coding (SWE) Winner OpenAI o3 71.7% SWE-bench · 95.2% HumanEval
🧠 STEM Reasoning Winner OpenAI o3 87.7% GPQA · 98.6% MATH-500

Which model is better: DeepSeek R1 or OpenAI o3?

DeepSeek R1 is 72.5% cheaper for input tokens ($0.55 vs. $2.00 per 1M tokens) and $2.19 vs. $8.00 for output tokens (3.6x cost advantage). In terms of operational performance, OpenAI o3 delivers lower response latency at 1150ms TTFT (300ms faster than DeepSeek R1) with 82 vs. 58 tok/s throughput. OpenAI o3 leads coding evaluations at 71.7% SWE-bench Verified vs. 49.2% for DeepSeek R1 and 87.7% vs. 71.5% on GPQA Diamond.
Verified daily via automated API latency tests and official documentation.

Verified Performance & Unit Economics Delta (12 Dimensions)

Daily synchronized metrics across token pricing, prompt cache economics, latency, throughput, and 5 capability benchmarks.

Metric / Dimension DeepSeek R1 OpenAI o3 Net Delta / Advantage
Input Cost / 1M Tokens $0.55 $2.00 DeepSeek R1 is 3.6x cheaper
Output Cost / 1M Tokens $2.19 $8.00 DeepSeek R1 (72.6% lower)
Prompt Cache Read / 1M $0.140 $0.500 DeepSeek R1 is 3.6x cheaper
Prompt Cache Write / 1M $0.55 $2.00 DeepSeek R1 is 3.6x cheaper
Context Window (Max Output) 131k (33k out) 205k (100k out) OpenAI o3 (1.6x larger context)
Avg TTFT Response Latency 1450ms 1150ms OpenAI o3 (300ms faster)
Generation Speed (Throughput) 58 tok/s 82 tok/s OpenAI o3 (+24 tok/s faster)
SWE-bench Verified (Coding) 49.2% 71.7% OpenAI o3 (+22.5% lead)
GPQA Diamond (PhD STEM) 71.5% 87.7% OpenAI o3 (+16.2% lead)
MMLU-Pro (Multi-Discipline) 84% 89.8% OpenAI o3 (+5.8% lead)
MATH-500 (Quantitative Logic) 97.3% 98.6% OpenAI o3 (+1.3% lead)
Inference Value Score (IVR) 64.8 / 100 60.6 / 100 DeepSeek R1 (+4.2 pts)

Interactive Monthly Token Economics & ROI Forecaster

Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.

25.0M Tokens
500K 50M 250M 500M+
5.0M Tokens
100K 25M 75M 150M+
50% Cached
0% (Uncached) 25% 50% (Typical Agent) 95% Max

Estimated Monthly Spend

DeepSeek R1 $19.58

$0.55/1M in · $2.19/1M out

Prompt Cache: $0.140/1M read · $0.55/1M write

OpenAI o3 $71.25

$2.00/1M in · $8.00/1M out

Prompt Cache: $0.500/1M read · $2.00/1M write

Projected Monthly Cost Reduction
$51.68 / mo
(72.5% lower cost with DeepSeek R1)
Open Full Matchup Analysis →
A

When to Choose DeepSeek R1

Choose DeepSeek R1 (DeepSeek) for open-weight chain-of-thought reasoning, synthetic data distillation, and complex mathematical verification — offering a 131k context window, 1450ms TTFT, and $0.55/1M input pricing ($0.140/1M cached reads).

B

When to Choose OpenAI o3

Choose OpenAI o3 (OpenAI) for algorithmic synthesis, mathematical verification, PhD-level scientific research, and multi-step tool planning — offering a 204k context window, 1150ms TTFT, and $2.00/1M input pricing ($0.500/1M cached reads).

Related Questions & Decision Factors

Which is cheaper: DeepSeek R1 or OpenAI o3?

DeepSeek R1 is cheaper at $0.55/1M input tokens ($2.19/1M output) compared to $2.00/1M input ($8.00/1M output) for OpenAI o3.

Which model has lower TTFT latency and higher tok/s throughput: DeepSeek R1 or OpenAI o3?

OpenAI o3 delivers faster first-token latency at 1150ms TTFT (82 tok/s throughput) compared to 1450ms TTFT (58 tok/s) for DeepSeek R1.

How do DeepSeek R1 and OpenAI o3 compare on SWE-bench coding and GPQA Diamond reasoning?

On SWE-bench Verified software engineering, DeepSeek R1 scores 49.2% versus 71.7% for OpenAI o3. On GPQA Diamond graduate-level STEM reasoning, DeepSeek R1 scores 71.5% versus 87.7% for OpenAI o3 (and 97.3% vs 98.6% on MATH-500).

When should you choose DeepSeek R1?

Choose DeepSeek R1 (DeepSeek) for open-weight chain-of-thought reasoning, synthetic data distillation, and complex mathematical verification — offering a 131k context window, 1450ms TTFT, and $0.55/1M input pricing ($0.140/1M cached reads).

When should you choose OpenAI o3?

Choose OpenAI o3 (OpenAI) for algorithmic synthesis, mathematical verification, PhD-level scientific research, and multi-step tool planning — offering a 204k context window, 1150ms TTFT, and $2.00/1M input pricing ($0.500/1M cached reads).

Explore High-Priority Related Model Matchups