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GPT-4.5 preview vs GPT-o3 mini
In the rapidly evolving landscape of artificial intelligence, choosing the right model for specific workflows is critical for both performance and cost-efficiency. This comprehensive guide provides a deep-dive comparison between OpenAI's GPT-4.5 Preview and GPT-o3 mini. While GPT-4.5 represents the pinnacle of broad-spectrum knowledge and creative insight, GPT-o3 mini introduces a specialized approach to reasoning and coding through a "private chain of thought."
To see how these models compare to other industry leaders, explore our previous analysis: ChatGPT 4o vs. Gemini 1.5.
Technical Specifications & Performance Metrics
Understanding the hardware-level limitations and capacities is the first step in model selection. Below is a detailed breakdown of their technical configurations as of early 2025.
| Specification | GPT-4.5 Preview | GPT-o3 Mini |
|---|---|---|
| Input Context Window | 128K | 200K |
| Maximum Output Tokens | 16K | 100K |
| Processing Speed (TPS) | 37.0 | 167.3 |
| Knowledge Cutoff | October 2023 | October 2023 |
| Release Date | Feb 27, 2025 | Jan 30, 2025 |
💡 Key Insight: GPT-o3 mini is built for high-throughput applications, offering nearly 4.5x faster output generation and a significantly larger context capacity for handling massive datasets.
Standardized Benchmark Performance
Data derived from official release notes and independent open benchmarks reveals a clear divergence in capabilities between "General Knowledge" and "Logical Reasoning."
| Benchmark Category | GPT-4.5 Preview | GPT-o3 Mini |
|---|---|---|
| MMLU (Undergrad Knowledge) | 85.1 | 81.1 |
| GPQA (Graduate Reasoning) | 71.4 | 79.7 |
| MATH (AIME '24) | 36.7 | 87.3 |
| SWE-Bench Verified (Coding) | 38.0 | 61.0 |
Hands-On Testing: Reasoning, Math, and Code
To go beyond numbers, we conducted practical evaluations. These tests monitor "efficiency vs. accuracy" using AIML API token consumption as a cost metric.
1. Verbal Reasoning & Logic
Scenario: Analyzing Medieval manuscripts and the influence of Aristotle's Poetics.
Solved the nuance of "demand and interest" effortlessly.
Tokens: 24,740
Initially struggled at "Low" reasoning, required "Medium" effort to solve.
Tokens: 136,395
2. Mathematical Geometry
Task: Calculating the radius of a smaller tangent semicircle within a larger quadrant.
Provided a beautiful radical explanation but failed the final calculation.
Tokens: 423,833
Used its chain-of-thought to arrive at the correct fractional answer (14/3).
Tokens: 25,179
3. Algorithmic Coding
Task: "Substring with Concatenation of All Words" (Sliding Window Algorithm).
In this test, GPT-4.5 Preview demonstrated its dominance in coding architecture, achieving a 5/5 score for efficiency and clean logic. While GPT-o3 mini solved the core problem, its code was less optimized for large-scale string processing.
API Cost Comparison (Per 1k Tokens)
| Token Type | GPT-4.5 Preview | GPT-o3 Mini |
|---|---|---|
| Input Price | $0.07875 | $0.001155 |
| Output Price | $0.15750 | $0.004620 |
*Pricing based on AIML API standard rates as of 2025.
Final Verdict: Which Model Should You Use?
Choose GPT-4.5 Preview If:
- You need advanced creative writing or nuanced tone.
- You are performing high-level software architecture.
- The task requires a vast "common sense" knowledge base.
- Human-like intuition is more important than raw mathematical speed.
Choose GPT-o3 Mini If:
- You are solving complex math or logic puzzles.
- Speed and latency are critical for your application.
- You are working on a budget (it is significantly cheaper).
- You need a massive context window for long documents (up to 200K).
Frequently Asked Questions
Generally, yes. Due to its "reasoning chain" architecture, GPT-o3 mini excels at the multi-step logical verification required for math, whereas GPT-4.5 may prioritize conversational fluency over computational accuracy.
GPT-o3 mini uses "hidden" reasoning tokens to process thoughts. Depending on the "reasoning effort" setting (Low, Medium, High), it may consume more tokens to ensure accuracy on difficult problems.
Yes, platforms like AIML API allow you to switch between these models dynamically. This is often the best strategy—using GPT-o3 mini for logic/math and GPT-4.5 for creative synthesis.
While both share an October 2023 cutoff, GPT-4.5 has a "wider" parameter base, meaning it typically recalls obscure facts or literary references more reliably than the "mini" reasoning models.
Would you like me to help you integrate these models into your specific Python or JavaScript application?