GPT-4o vs O1 Complete Model Comparison Guide

Picking the right OpenAI model can make or break your AI strategy. GPT-4o gives you multimodal capabilities, lightning-fast responses, and reasonable pricing, while O1 shows off PhD-level thinking that solves Math Olympiad problems.

This breakdown offers head-to-head comparisons with cost analysis. You'll get practical frameworks to apply to your specific needs and discover which model—or combination—fits your tech stack.

GPT 4o vs O1 At A Glance

Choosing between OpenAI models means balancing speed, reasoning quality, context size, and cost. Here's how they stack up before we dive into what this means for your specific use case.

Comparing GPT-4o vs O1 Capabilities In More Detail

Let's examine what these specifications mean for your real-world applications by analyzing key differences between these models.

General Functionality

These models have two different design philosophies. GPT-4o slims down the transformer architecture for speed, making it perfect for when you need chat, translation, and media-rich tasks with minimal wait times.

O1 takes the opposite path, expanding its computation to explicitly work through each reasoning step. This thoughtful approach creates better explanations but drastically increases processing time and cost.

Problem-Solving Skills

The benchmark gaps are striking. Direct comparisons show O1 solving 83% of International Math Olympiad qualifiers versus GPT-4o's 13%. The same tests put O1 in Codeforces' top brackets—well above GPT-4o's middle-of-the-pack results. 

When your workflows depend on complex math or intricate code refactoring, you'll find O1's extra processing delivers real accuracy gains.

Output Capacity

Output capacity reveals another important distinction. Both handle 128k-token inputs, but GPT-4o caps output at 4k tokens—fine for your summaries or chat but too small for comprehensive documentation.

O1 shines with high throughput, allowing you to review entire white papers, legal documents, or large codebases without manually breaking them into chunks.

Multimodal Capabilities

Multimodal abilities flip this comparison completely. GPT-4o natively works with text and images—plus audio through a specialized real-time API, perfect when you're building support systems, parsing screenshots, or voice inputs. O1 supports text and images, though no audio.

Speed compounds this difference: GPT-4o responds in under a second, while testing shows O1 may be up to 30x slower on identical prompts.

This figure is based on benchmarks with specific, complex, step-by-step prompts. In routine short prompts, the latency gap may be far smaller, sometimes just 3–10×. Not all real-world tasks will see the full worst-case difference

Cost Considerations

Cost often drives your final decision. At roughly $2.50 per million input tokens, GPT-4o runs six to seven times cheaper than O1's $15 tier. For your ongoing production traffic, this gap quickly overshadows all other expenses.

API Availability

API maturity creates practical constraints worth noting. GPT-4o operates in general availability with established endpoints requiring minimal changes to your implementation.

O1 has been deprecated (o1-preview as of July 28, 2025; o1-mini shutting down October 27, 2025)—check current status and consider moving to o3 or o4-mini for your production use.

The data paints a clear picture: you'll find GPT-4o dominates scenarios needing fast, multimodal responses at scale, while O1 excels when deep reasoning and extensive output matter more than speed or cost for your applications.

GPT-4o Strengths and Enterprise Applications

GPT-4o stands out for real-world production needs with its balance of speed, multimodal capabilities, and cost-effectiveness. This design philosophy shapes how you'll build applications that need quick, versatile responses.

Strengths

• Native multimodal processing: Handles text, images, and audio in a single prompt, eliminating the need for separate OCR or speech-to-text services

• Superior speed: Benchmarks show responses up to 30× faster than O1, critical for user-facing applications

• Cost efficiency: GPT-4o costs ~$2.50 per million input tokens vs. O1's six to seven times higher price

• Broad versatility: Studies rank it high on general language tasks, handling the 80% of everyday work most teams require

Enterprise Use Cases

1. Customer support automation: Your GPT-4o agent processes screenshots, pulls order data, and responds in under a second

2. Content generation: Marketing teams get blog drafts, social captions, and alt text from brand guides and reference images

3. Real-time experiences: Voice assistants and chat widgets benefit from fast first-token time and consistent throughput

4. Document workflows: Process scanned invoices, annotate contracts, and receive instant analysis

GPT-4o works with popular frameworks through OpenAI's mature API, making deployment straightforward via LangChain or serverless patterns. For most enterprise applications, its balance of speed, capability, and price lets you ship today rather than plan for tomorrow.

O1 Strengths and Enterprise Applications

O1 delivers when accuracy matters more than speed for your applications. It uses deep, step-by-step reasoning, working through each logical step instead of rushing to conclusions. This transparency becomes essential when you need to audit decisions or defend them to regulators.

Strengths

• Deep reasoning chains: Financial research methodology showed "large and consistent improvements" in accuracy by detailing each valuation driver

• Academic-level problem solving: Solved 83% of International Math Olympiad qualifier problems vs. GPT-4o's 13%, and beats PhD-level performance on graduate science questions

• Extensive output capacity: Handles up to 128,000 token inputs and generates up to 65,536 token outputs, perfect for comprehensive analyses

• Transparent verification paths: Every calculation appears inline, letting auditors catch errors before critical presentations

Enterprise Use Cases

1. Financial modeling: Risk assessments for leveraged buyouts with full macroeconomic assumptions, covenant calculations, and break point identification

2. Healthcare diagnostics: Traces differential diagnoses step by step, checking drug interactions so clinicians understand both what to prescribe and why

3. Legal research: Processes case law, finds precedents, and explains rulings with transparent citation chains

4. Strategic planning: Models competitive scenarios, market trends, and capital constraints in cohesive narratives

These advantages come with trade-offs—about $15 per million input tokens and $60 per million output tokens (6–7× more than GPT-4o), plus response times up to 30 times longer. Yet when one bad inference costs millions—or lives—this premium often makes sense for high-stakes decisions requiring transparent logic.

How to Evaluate Which Model Works Best for Your Use Case

One-off tests aren't sufficient when comparing models like GPT-4o and O1. As models evolve rapidly, you need a robust evaluation system.

Effective evaluation requires multi-faceted metrics beyond accuracy alone. Response time, cost per call, and user satisfaction significantly impact real-world performance. Build your evaluation on two parallel tracks:

1. Maintain a frozen benchmark set for regression testing

2. Sample live traffic for continuous shadow testing

An observability layer should tag interactions with performance metrics and business KPIs. When models improve in specific areas, your dashboards will highlight these changes, enabling smart routing decisions.

Implement dynamic selection using a lightweight router that examines prompts and selects the most cost-effective model meeting your quality thresholds. For example, route invoice images to GPT-4o, but send complex financial analyses to O1.

Start with simple nightly comparisons on curated prompts, then add shadow testing and performance alerts as you scale. This systematic approach isn't overhead—it's the control system that lets you leverage each model's strengths while maintaining production stability.

Choose the Right Model with Galileo

Galileo's AI observability platform helps you implement the strategic model selection approach described throughout this guide.

• Continuous performance tracking across all metrics that matter—accuracy, latency, cost, and user satisfaction—with automated dashboards

• Shadow testing capabilities that let you compare model outputs side-by-side without disrupting production workflows

• Smart routing rules based on real data, directing simple queries to GPT-4o and complex reasoning tasks to O1 based on your custom thresholds

• Cost optimization insights that identify opportunities to shift traffic between models as your usage patterns and model capabilities evolve

Start with Galileo today and build a dynamic model evaluation system that leverages the best of both GPT-4o and O1.