GPT-4o vs Claude 3.5 Sonnet vs Gemini 1.5 Pro: Which One Should You Use?

When starting a project with LLMs, the first question is almost always the same: which model do I use? GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro are today the three most widely used models in production, and each has real strengths in different areas.

This comparison isn't based on marketing benchmarks — every model publishes favorable numbers on its own tests. It's based on the practical differences that matter when you're using these models in real projects.

The Three Models in Context

GPT-4o (OpenAI) is OpenAI's flagship multimodal model, available via API at platform.openai.com. It accepts text, image, and audio as input. It's the most widely used model in production globally, thanks to the maturity of its API and the ecosystem of tools built around it.

Claude 3.5 Sonnet (Anthropic) is part of Anthropic's Claude 3.5 family, available via API at console.anthropic.com. It stands out for its ability to follow complex instructions and its strong performance on long-form writing and analysis tasks. Anthropic's model documentation keeps an updated list of available models.

Gemini 1.5 Pro (Google DeepMind) is Google's model with the largest context window of the three — up to 1 million tokens — available via Google AI Studio and the Vertex AI API. Its native integration with the Google ecosystem (Docs, Drive, Search) is a real advantage in certain workflows.

Context Window

The context window determines how much information you can send in a single call. It's critical when working with long documents, extended conversations, or large amounts of code.

  • GPT-4o: 128,000 tokens
  • Claude 3.5 Sonnet: 200,000 tokens
  • Gemini 1.5 Pro: up to 1,000,000 tokens

In practice, a 128,000 token window is roughly 300 pages of text — enough for the vast majority of use cases. Gemini's advantage only becomes meaningful when you need to process entire books, large code repositories, or text datasets in a single call.

Performance by Task Type

Code Generation and Analysis

All three models generate quality code, but with nuances:

GPT-4o has the most mature ecosystem for code: it works well across most languages, integrates well with tools like GitHub Copilot, and has been the model most developers have used until now — which means more community resources and examples available.

Claude 3.5 Sonnet has particularly strong performance on refactoring tasks and explaining complex code. Its ability to follow detailed instructions makes it good for tasks where output format matters — generating tests, documentation, or code with a specific structure.

Gemini 1.5 Pro stands out when the code context is very large: analyzing an entire repository, performing global refactoring, or understanding dependencies between distant files is where its 1M token window makes a real difference.

Writing and Content

Claude 3.5 Sonnet is consistently the strongest for writing tasks that require a specific tone, coherence across long texts, or following complex style instructions. If you need to generate technical documentation, articles, or content with precise formatting, Claude produces more consistent results.

GPT-4o is solid for general writing and has the advantage of being able to include images as references in the prompt — useful for describing screenshots, analyzing designs, or generating alt text.

Gemini 1.5 Pro is competitive for writing but its biggest advantage here is integration with Google Workspace — it can directly access Drive documents if you use the Vertex AI API with integrations configured.

Reasoning and Analysis

For complex reasoning tasks — math, logic, argument analysis — the three models are very closely matched according to public benchmarks from MMLU and HumanEval. Practical differences are small and depend heavily on the type of problem.

Where there is a notable difference is in reasoning over long documents: Gemini maintains better coherence when context approaches its maximum limit, while GPT-4o and Claude can degrade in quality when context is very extensive.

Instruction Following

This is where Claude 3.5 Sonnet has its clearest advantage. If you give detailed instructions about format, structure, what to include and what to exclude, Claude follows them with more precision than the other two. This is especially useful in automated pipelines where the output needs an exact format to be processed by code.

Pricing (Approximate Reference)

Prices change frequently — always check the official pages for current values:

As a general reference, all three models are in similar ranges for moderate use. Price differences become relevant at scale — if you're processing millions of tokens per day, a few cents per million tokens make a meaningful difference.

For prototypes and small projects, all three offer free tiers or initial credits sufficient to experiment without cost.

Decision Framework

Instead of declaring an absolute winner, here's a practical criterion for choosing:

Choose GPT-4o if:

  • It's your first LLM project and you want the most mature ecosystem
  • You need to process images alongside text
  • Your team already uses OpenAI ecosystem tools
  • Community resources and available documentation are important to you

Choose Claude 3.5 Sonnet if:

  • Your use case is primarily writing, documentation, or text analysis
  • You need to follow complex format instructions consistently
  • You work with long contexts (up to 200K tokens) and want high quality throughout
  • Output precision matters more than raw speed

Choose Gemini 1.5 Pro if:

  • You need to process very long documents (over 128K tokens)
  • Your workflow is integrated with Google Workspace
  • You want to experiment with 1M token contexts for specific use cases
  • You use Google Cloud as your primary infrastructure

What All Three Have in Common

Beyond their differences, all three models share features that make them production-ready:

  • APIs with documented availability SLAs
  • Support for function calling and tool use
  • Fine-tuning options (with different restrictions per model)
  • Active documentation and support teams

The model decision is rarely permanent. Most mature projects end up using more than one model depending on the task: one for code generation, another for document analysis, another for cost-sensitive tasks.

How to Evaluate for Yourself

The best way to choose is to test all three with your own real use cases. All three offer free access for experimentation:

Define 5 to 10 tasks representative of your project, run them through all three models with the same prompt, and evaluate the output. That practical test is worth more than any published benchmark.