On February 27, 2025, OpenAI introduced GPT-4.5 as a "research preview," marking it as the latest evolution in their flagship GPT series. Described as their "largest and most knowledgeable model yet," GPT-4.5 promises to refine the art of conversation, reduce errors, and enhance user experience. While it doesn’t aim to dominate reasoning-heavy benchmarks like its o-series counterparts (e.g., o1 and o3-mini), it excels in natural dialogue, factual accuracy, and creative tasks. In this blog, we’ll dive into GPT-4.5’s specifications, benchmark performance, and what it means for users and developers alike. %20(1).jpg)
Internally codenamed "Orion," GPT-4.5 is reportedly OpenAI’s most compute-intensive model to date. While exact numbers on parameters or training data size remain undisclosed, estimates from industry speculation suggest it could involve 5-7 trillion parameters — a significant jump from GPT-4’s rumored 1.7 trillion — paired with a dataset potentially double the size of its predecessor’s. This massive scale translates to a broader knowledge base, sharper accuracy, and a conversational tone that feels distinctly human-like.
1) SimpleQA (Factual Accuracy):
2) MMLU (Multilingual Understanding):
3) MMMU (Multimodal Understanding):
Weaknesses:
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What is GPT-4.5?
GPT-4.5 is the newest installment in OpenAI’s GPT lineage, a series that has redefined natural language processing since ChatGPT’s debut in 2022. Unlike the reasoning-focused o-series models, GPT-4.5 builds on the classic GPT approach of unsupervised learning, scaling up computational power and training data to enhance its "world model" — its understanding of facts, patterns, and human interaction. OpenAI has dubbed it a "non-chain-of-thought model," meaning it doesn’t rely on step-by-step reasoning but instead delivers intuitive, direct responses.Internally codenamed "Orion," GPT-4.5 is reportedly OpenAI’s most compute-intensive model to date. While exact numbers on parameters or training data size remain undisclosed, estimates from industry speculation suggest it could involve 5-7 trillion parameters — a significant jump from GPT-4’s rumored 1.7 trillion — paired with a dataset potentially double the size of its predecessor’s. This massive scale translates to a broader knowledge base, sharper accuracy, and a conversational tone that feels distinctly human-like.
Specifications of GPT-4.5
Though OpenAI hasn’t released a full technical breakdown, we can infer key specifications based on its predecessors and early reports:- Model Size and Compute: GPT-4.5 is OpenAI’s largest model yet, likely requiring an order of magnitude more computational resources than GPT-4. This aligns with posts on X estimating a 10x increase in compute, possibly driven by a combination of increased parameters (e.g., 5x GPT-4’s) and a larger dataset (e.g., 2x GPT-4’s). The result is a denser, more capable neural network.
- Context Window: Like GPT-4 and GPT-4o, GPT-4.5 supports a 128,000-token context window, equivalent to roughly 300 pages of text. This allows it to maintain coherence over long conversations or analyze extensive documents in one go.
- Multimodal Input: GPT-4.5 accepts both text and image inputs, producing text-based outputs. While it doesn’t generate images, audio, or video (unlike some multimodal competitors), its ability to process visual data enhances its utility for tasks like document analysis or captioning.
- Training Approach: Built on unsupervised pre-training, GPT-4.5 leverages vast datasets to improve its intuition and factual recall. It lacks the explicit reasoning mechanisms of the o-series but compensates with scale and refinement.
- API Pricing: Early reports indicate steep costs: $75 per million input tokens and $150 per million output tokens. This makes it 15-20 times pricier than GPT-4o ($2.50/$10) and even outstrips o1 ($15/$60), reflecting its computational demands.
- Access: Released initially to ChatGPT Pro users ($200/month) on February 27, 2025, it rolled out to Plus and Team users the following week, with Enterprise and Edu tiers following after. Features like real-time search and file uploads are supported, though voice mode and video capabilities are absent in this preview.
Benchmark Performance
OpenAI has shared benchmark results that highlight GPT-4.5’s strengths and limitations. While it doesn’t compete with reasoning models like o3-mini on logic-heavy tasks, it shines in general knowledge, factual accuracy, and conversational quality. Here’s a detailed look:1) SimpleQA (Factual Accuracy):
- GPT-4.5: 62.5%
- GPT-4o: 38.2%
- o1: 47%
- o3-mini: 15%
- Hallucination Rate: 37.1% (vs. 61.8% for GPT-4o, 44% for o1, 80.3% for o3-mini)
- Takeaway: GPT-4.5 leads in straightforward knowledge questions, with a significantly lower tendency to fabricate answers — a major win for reliability.
2) MMLU (Multilingual Understanding):
- GPT-4.5: 85.1%
- GPT-4o: 81.5%
- o3-mini: 81.1%
- Takeaway: A modest but notable improvement, showcasing enhanced performance across diverse languages and subjects.
3) MMMU (Multimodal Understanding):
- GPT-4.5: 74.4%
- GPT-4o: 69.1%
- o3-mini: N/A
- Takeaway: With image input support, GPT-4.5 outperforms GPT-4o in tasks blending text and visuals, like interpreting charts or diagrams.
4) GPQA (Natural Sciences):
- GPT-4.5: 71.4%
- GPT-4o: 53.6%
- o3-mini: 79.7%
- Takeaway: A strong leap over GPT-4o, but it falls short of o3-mini’s reasoning prowess in scientific domains.
5) AIME ’24 (Mathematics):
- GPT-4.5: 36.7%
- GPT-4o: 9.3%
- o3-mini: 87.3%
- Takeaway: While it triples GPT-4o’s score, GPT-4.5 lags far behind o3-mini, underscoring its non-reasoning focus.
6) SWE-Lancer Diamond (Real-World Coding):
- GPT-4.5: 32.6%
- GPT-4o: 23.3%
- o3-mini: 10.8%
- Takeaway: Surprisingly, GPT-4.5 outperforms o3-mini in practical coding tasks, likely due to its broader knowledge base.
7) SWE-Bench Verified (Coding):
- GPT-4.5: 38.0%
- GPT-4o: 30.7%
- o3-mini: 61.0%
- Claude 3.7 Sonnet: 62.3%
- Takeaway: It improves on GPT-4o but can’t match o3-mini or Anthropic’s latest in structured coding.
8) Human Evaluations:
- Preferred over GPT-4o in creative tasks (56.8%), professional queries (63.2%), and everyday questions (57.0%).
- Takeaway: Testers favor GPT-4.5 for its tone, clarity, and emotional resonance.
Strengths and Weaknesses
Strengths:- Conversational Flow: GPT-4.5’s responses feel warm, intuitive, and concise, making it ideal for chatbots, writing assistance, and casual interaction.
- Factual Accuracy: With a hallucination rate of 37.1%, it’s more trustworthy than GPT-4o (61.8%) or o3-mini (80.3%).
- Creativity: It excels in writing, brainstorming, and tasks requiring emotional intelligence, outpacing GPT-4o in human preference tests.
- Multilingual and Multimodal: Improved MMLU and MMMU scores highlight its versatility across languages and input types.
Weaknesses:
- Reasoning Limits: It struggles with complex math, science, and structured problem-solving, where o3-mini reigns supreme.
- Cost: At $75/$150 per million tokens, it’s prohibitively expensive for budget-conscious applications.
- No Multimodal Output: Unlike some competitors, it can’t generate images or audio, limiting its creative scope.
Real-World Applications
GPT-4.5’s design makes it a powerhouse for specific use cases:
- Content Creation: Drafting blog posts, marketing copy, or creative stories with a human-like touch.
- Customer Support: Powering chatbots that empathize and respond naturally to user queries.
- Knowledge Retrieval: Summarizing documents or answering factual questions with higher reliability.
- Multilingual Tasks: Translating or localizing content while preserving context.