Which model is better: DeepSeek R1 or OpenAI o3-mini?
DeepSeek R1 cuts input token costs by 50.0% compared to OpenAI o3-mini ($0.55 vs. $1.10 per 1M input tokens; $2.19 vs. $4.40/1M output). For autonomous software engineering, OpenAI o3-mini leads on SWE-bench Verified (49.3% vs. 49.2%), while OpenAI o3-mini edges ahead on GPQA Diamond scientific reasoning (79.7%). In real-time API production, OpenAI o3-mini achieves a faster 950ms TTFT latency (98.0 tokens/sec).
Verified daily via automated API latency tests and official documentation.
Zero-Shot RAG Verified
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-mini
Net Delta / Advantage
Input Cost / 1M Tokens
$0.55
$1.10
DeepSeek R1 is 2.0x cheaper
Output Cost / 1M Tokens
$2.19
$4.40
DeepSeek R1 (50.2% lower)
Prompt Cache Read / 1M
$0.140
$0.550
DeepSeek R1 is 3.9x cheaper
Prompt Cache Write / 1M
$0.55
$1.10
DeepSeek R1 is 2.0x cheaper
Context Window (Max Output)
131k (33k out)
205k (100k out)
OpenAI o3-mini (1.6x larger context)
Avg TTFT Response Latency
1450ms
950ms
OpenAI o3-mini (500ms faster)
Generation Speed (Throughput)
58 tok/s
98 tok/s
OpenAI o3-mini (+40 tok/s faster)
SWE-bench Verified (Coding)
49.2%
49.3%
OpenAI o3-mini (+0.1% lead)
GPQA Diamond (PhD STEM)
71.5%
79.7%
OpenAI o3-mini (+8.2% lead)
MMLU-Pro (Multi-Discipline)
84%
83.2%
DeepSeek R1 (+0.8% lead)
MATH-500 (Quantitative Logic)
97.3%
97.9%
OpenAI o3-mini (+0.6% lead)
Inference Value Score (IVR)
64.8 / 100
59.7 / 100
DeepSeek R1 (+5.1 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
DeepSeek R1 is cheaper at $0.55/1M input tokens ($2.19/1M output) compared to $1.10/1M input ($4.40/1M output) for OpenAI o3-mini.
Which model has lower TTFT latency and higher tok/s throughput: DeepSeek R1 or OpenAI o3-mini?
OpenAI o3-mini delivers faster first-token latency at 950ms TTFT (98 tok/s throughput) compared to 1450ms TTFT (58 tok/s) for DeepSeek R1.
How do DeepSeek R1 and OpenAI o3-mini compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, DeepSeek R1 scores 49.2% versus 49.3% for OpenAI o3-mini. On GPQA Diamond graduate-level STEM reasoning, DeepSeek R1 scores 71.5% versus 79.7% for OpenAI o3-mini (and 97.3% vs 97.9% on MATH-500).
When should you choose DeepSeek R1?
Deploy DeepSeek R1 when your architecture requires 49.2% SWE-bench coding accuracy, 71.5% GPQA Diamond reasoning, and 131k context at $0.55/1M input ($0.140/1M cached read).
When should you choose OpenAI o3-mini?
Select OpenAI o3-mini for workloads prioritizing 950ms TTFT latency, 98.0 tokens/sec throughput, and $1.10/1M input pricing ($0.550/1M cached read).
