Which OpenAI Model Is Best for Coding? A Developer’s Guide to GPT-4, GPT-4o, and Codex

Written By:
Founder & CTO
July 3, 2025

As the software engineering landscape becomes increasingly intertwined with artificial intelligence, one of the most important decisions a developer can make in 2025 is selecting the right large language model for code generation, debugging, documentation, and automation. OpenAI currently offers three distinct models relevant to coding workflows: Codex, GPT-4, and GPT-4o.

This comprehensive technical guide dives deep into the architecture, strengths, limitations, and practical applications of each model. Whether you are building internal tools, integrating LLMs into your IDE, or prototyping AI-powered coding assistants, this comparison will help you choose the right model based on performance, latency, cost efficiency, and real-world use case alignment.

A Brief History of OpenAI's Code Models

The trajectory from Codex to GPT-4o reflects the rapid maturity of LLMs in code-focused use cases. Codex was OpenAI’s first targeted attempt at a code-specialized model. While revolutionary for its time, it was fundamentally a fine-tuned GPT-3 variant. GPT-4 brought large context capabilities and much stronger reasoning. GPT-4o, released in 2024, is an omni-capable model that delivers near GPT-4-level accuracy, higher throughput, and lower latency, while also supporting multimodal capabilities.

Model Overview and Feature Comparison

FeatureCodexGPT-4GPT-4oBase ModelGPT-3 fine-tunedGPT-4GPT-4 (omni architecture)Context Length~4K tokens8K to 128K128K tokensTraining FocusCode-centricGeneral-purpose with coding supportMultimodal with strong code capabilitiesSpeedModerate latencySlower at longer contextFastest among allCost EfficiencyAverageHighHigh performance at lower costAvailabilityDeprecatedAPI and ProAPI, Playground, and ChatGPT free

Codex: Legacy Model Built on GPT-3

Codex was trained on a substantial dataset of publicly available code from GitHub, Stack Overflow, and other programming sources. It specializes in language-to-code translation and simple code generation tasks, especially in Python.

Strengths of Codex

Codex remains surprisingly competent at:

  • Generating small Python scripts with minimal logic
  • Translating simple natural language queries into code snippets
  • Supporting repetitive or boilerplate-heavy programming tasks
  • Function-level code synthesis

The model also powered the first generation of GitHub Copilot, which was widely adopted for autocomplete and line-by-line suggestions.

Limitations of Codex

Codex has a relatively small context window, typically around 4K tokens. This limits its ability to operate on larger codebases or reason about multiple modules. The architectural limitations of GPT-3 also manifest as:

  • Lack of consistency in syntax across different languages
  • Poor handling of ambiguous instructions or vague comments
  • Limited reasoning capability for architectural or system-level decisions
  • Higher error rate in tasks involving recursion, state management, or nested dependencies
Developer Recommendation

Codex may still be functional for quick one-off tasks or legacy support, but it is no longer recommended for production-grade development. Its performance is surpassed in every metric by GPT-4 and GPT-4o. If your toolchain or plugin still relies on Codex, it is worth upgrading to the newer models for accuracy and speed.

GPT-4: Precision and Contextual Reasoning

GPT-4 represents a significant leap in reasoning ability, token context, and multi-language support. Although it is not solely focused on code, it demonstrates high competency in programming, architecture generation, and cross-domain integration.

Advanced Code Understanding

GPT-4 offers:

  • High accuracy in complex function implementation
  • Understanding of multiple paradigms such as functional, object-oriented, and reactive programming
  • Generation of type-safe code across statically typed languages such as TypeScript, Rust, and Go
  • Effective handling of generics, interfaces, and middleware layers
Long Context Window

With up to 128K tokens available in the Turbo variant, GPT-4 can:

  • Reason over multiple files in a project
  • Summarize large codebases and generate documentation
  • Maintain coherence across multiple modules and dependencies
  • Perform high-level refactoring and suggest architectural improvements
Strong Use Case Coverage

GPT-4 excels in:

  • Writing test suites, especially for edge cases
  • Integrating third-party APIs with well-structured error handling
  • Debugging obscure runtime issues using language context
  • Providing step-by-step rationales for why certain design choices should be made
Limitations and Trade-Offs

The primary drawback of GPT-4 is speed. Due to its computational weight, response times may be slower, especially for large payloads or multi-turn interactions. Additionally, it comes at a higher token cost when deployed at scale.

Developer Recommendation

GPT-4 is highly suitable for backend-heavy development, refactoring existing large-scale systems, and for developers looking to build robust pipelines that require stable, accurate outputs. Its performance makes it an excellent model for CI/CD integration, code review automation, and architectural design.

GPT-4o: The Fast, Multimodal Workhorse

GPT-4o (short for omni) is OpenAI’s latest and most capable model, optimized for speed, cost, and flexibility. It not only matches GPT-4 in many coding use cases but also introduces multimodal capabilities, making it an ideal choice for developers building interactive tools and AI-powered IDEs.

Speed and Latency Benefits

GPT-4o drastically reduces average latency. Benchmarks show:

  • Sub-3 second latency on most prompts
  • Stable performance across concurrent requests
  • Near-instant inference for autocompletion and code refactoring

This responsiveness makes GPT-4o ideal for real-time developer tools, code assistants, and pair programming agents.

Code Generation Accuracy

Despite being faster, GPT-4o maintains accuracy close to GPT-4, achieving:

  • High success rates on HumanEval and SWE-bench coding benchmarks
  • Clean syntax, idiomatic usage, and context-awareness across JavaScript, Python, TypeScript, Java, and C++
  • Enhanced ability to handle edge cases, conditional logic, and asynchronous workflows
Multimodal Programming Use Cases

GPT-4o natively supports text, audio, image, and video inputs. Developers can leverage this for:

  • Visual-to-code applications, e.g., converting UI mockups into front-end components
  • Audio-based code generation, useful for accessibility
  • Building multi-agent systems where inputs vary by modality
Cost and Deployment Efficiency

GPT-4o is more cost-efficient compared to GPT-4, offering a favorable performance-to-price ratio. Its efficient architecture makes it easier to integrate into:

  • Custom developer tools and IDEs
  • Prompt pipelines in LangChain, LlamaIndex, or Semantic Kernel
  • Serverless inference APIs or containerized AI services
Developer Recommendation

GPT-4o is the default model for any modern development workflow in 2025. Whether you're integrating LLMs into dev environments or building intelligent tooling for code navigation, GPT-4o offers the best trade-off between speed, accuracy, and cost.

Model Selection Based on Developer Workflows

Choosing the best model ultimately depends on your use case. Here is a decision-oriented guide.

Technical Benchmarks

Using community-curated benchmarks and public dataset analysis, here are technical results based on evaluations conducted over the last six months.

Code Generation Accuracy (HumanEval+)
  • GPT-4: 82.1 percent
  • GPT-4o: 81.7 percent
  • Codex: 28.5 percent
Multilingual Code Support (Code Translation Tests)
  • GPT-4: Excellent (Type-safe, idiomatic)
  • GPT-4o: Very good (Fast and contextually aware)
  • Codex: Limited to Python, JS, basic Java
Latency (Prompt to Completion)
  • Codex: 6 to 8 seconds
  • GPT-4: 10 to 20 seconds
  • GPT-4o: 2 to 3 seconds

Model Ecosystem and Integration

A model’s utility is determined not only by performance but also by its ecosystem compatibility.

IDE Integration
  • Codex: GitHub Copilot legacy, limited customization
  • GPT-4: Accessible via plugins like Cursor, OpenAI API, GoCodeo backend integrations
  • GPT-4o: Native integration into ChatGPT IDEs, supports multi-modal inputs and agent embedding
Agent Frameworks

GPT-4 and GPT-4o are compatible with:

  • OpenAI Functions
  • LangChain agents
  • AutoGen, CrewAI, GoCodeo AI pipelines
  • Retrieval-Augmented Generation for project-level embeddings

Final Thoughts

If you are building with LLMs in 2025, the choice of model can significantly influence development velocity, code quality, and system performance. Codex has served its purpose but is now functionally obsolete. GPT-4 continues to lead in tasks that require architectural depth, long memory, or critical refactoring.

However, GPT-4o is shaping up to be the most balanced model, offering high coding proficiency at superior speed and scalability. Its multimodal nature opens doors to developer experiences not previously feasible, such as real-time visual code generation, voice-driven command execution, and more interactive pair programming workflows.