💰 Unit Cost Winner GPT-4o Mini $0.15/1M in · $0.60/1M out
⚡ Latency & Speed Winner GPT-4o Mini 220ms TTFT · 140 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: GPT-4o Mini or OpenAI o3?

GPT-4o Mini is 92.5% cheaper for input tokens ($0.15 vs. $2.00 per 1M tokens) and $0.60 vs. $8.00 for output tokens (13.3x cost advantage). In terms of operational performance, GPT-4o Mini delivers lower response latency at 220ms TTFT (930ms faster than OpenAI o3) with 140 vs. 82 tok/s throughput. OpenAI o3 leads coding evaluations at 71.7% SWE-bench Verified vs. 41.0% for GPT-4o Mini and 87.7% vs. 40.2% 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 GPT-4o Mini OpenAI o3 Net Delta / Advantage
Input Cost / 1M Tokens $0.15 $2.00 GPT-4o Mini is 13.3x cheaper
Output Cost / 1M Tokens $0.60 $8.00 GPT-4o Mini (92.5% lower)
Prompt Cache Read / 1M $0.075 $0.500 GPT-4o Mini is 6.7x cheaper
Prompt Cache Write / 1M $0.15 $2.00 GPT-4o Mini is 13.3x cheaper
Context Window (Max Output) 131k (16k out) 205k (100k out) OpenAI o3 (1.6x larger context)
Avg TTFT Response Latency 220ms 1150ms GPT-4o Mini (930ms faster)
Generation Speed (Throughput) 140 tok/s 82 tok/s GPT-4o Mini (+58 tok/s faster)
SWE-bench Verified (Coding) 41% 71.7% OpenAI o3 (+30.7% lead)
GPQA Diamond (PhD STEM) 40.2% 87.7% OpenAI o3 (+47.5% lead)
MMLU-Pro (Multi-Discipline) 68% 89.8% OpenAI o3 (+21.8% lead)
MATH-500 (Quantitative Logic) 70.2% 98.6% OpenAI o3 (+28.4% lead)
Inference Value Score (IVR) 67.2 / 100 60.6 / 100 GPT-4o Mini (+6.6 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

GPT-4o Mini $5.81

$0.15/1M in · $0.60/1M out

Prompt Cache: $0.075/1M read · $0.15/1M 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
$65.44 / mo
(91.8% lower cost with GPT-4o Mini)
Open Full Matchup Analysis →
A

When to Choose GPT-4o Mini

Choose GPT-4o Mini (OpenAI) for high-volume classification, lightweight RAG chatbots, and structured JSON extraction — offering a 131k context window, 220ms TTFT, and $0.15/1M input pricing ($0.075/1M 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: GPT-4o Mini or OpenAI o3?

GPT-4o Mini is cheaper at $0.15/1M input tokens ($0.60/1M 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: GPT-4o Mini or OpenAI o3?

GPT-4o Mini delivers faster first-token latency at 220ms TTFT (140 tok/s throughput) compared to 1150ms TTFT (82 tok/s) for OpenAI o3.

How do GPT-4o Mini and OpenAI o3 compare on SWE-bench coding and GPQA Diamond reasoning?

On SWE-bench Verified software engineering, GPT-4o Mini scores 41% versus 71.7% for OpenAI o3. On GPQA Diamond graduate-level STEM reasoning, GPT-4o Mini scores 40.2% versus 87.7% for OpenAI o3 (and 70.2% vs 98.6% on MATH-500).

When should you choose GPT-4o Mini?

Choose GPT-4o Mini (OpenAI) for high-volume classification, lightweight RAG chatbots, and structured JSON extraction — offering a 131k context window, 220ms TTFT, and $0.15/1M input pricing ($0.075/1M 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