ChatGPT vs Claude vs Gemini for Coding: writing, execution, explanation and file management
- Graziano Stefanelli
- Jul 6
- 5 min read
When you open ChatGPT, Claude or Gemini, you enter a workspace where collaboration happens live, tools stay at hand and the path runs smoothly from first prompt to production build.
Key differences emerge in the middle steps: handling demanding refactors, proposing performance boosts, preserving context across large repositories and delivering precise explanations instead of boilerplate replies.
ChatGPT GPT-4o writes code fluently and runs it immediately in a built-in Python sandbox.
The model combines fast generation with a consistent workspace: paste a Python block, an SQL query or a machine-learning routine and receive both the requested solution and its live execution, complete with printed output, graphs or temporary files. The sandbox stays isolated from the host machine, shielding against unexpected bugs and keeping the “prompt → run → review” loop flowing without mental context switches.
Error handling follows the same philosophy: when a traceback appears, GPT-4o proposes fixes, clarifies variable states and suggests incremental edits rather than destructive rewrites. Large data files, pre-trained models and images upload straight into the session, so the full workflow remains visible and traceable.
In terms of raw capabilities, GPT-4o supports up to 128,000 tokens of context and runs Python code directly in its interface. Free users can access this with some rate limits, while Plus and Pro tiers get uninterrupted access. On HumanEval, the model achieves pass rates between 74% and 76%, and it currently ranks among the fastest to respond across all mainstream LLMs as of July 2025.
OpenAI o3 and o3-pro sharpen logic and keep complex projects coherent while sharing GPT-4o’s execution features.
When a task grows from a lone script to a multi-module application, o3 and o3-pro hold past edits in working memory, link them to new prompts and reduce merge conflicts. Documentation and unit-test generation come with clear reasoning: each pattern choice and exception pathway is spelled out. For algorithm optimisation, the models rewrite whole blocks, annotate changes and supply micro-benchmarks that highlight speed gains.
These two models, launched in mid-2025, are especially valued in enterprise and team settings. o3-pro, in particular, is built to sustain high reliability and structured logic across large codebases. It comfortably handles workloads beyond the 128K context window (especially in Team environments) and reaches coding success rates of over 80% on full Python testing suites—consistently placing it at the top for industrial-scale refactoring and documentation accuracy.
Claude Opus 4 tops comprehension benchmarks and rewrites entire codebases with didactic commentary, though chat sessions cannot run code.
Opus 4 comfortably ingests thousands of lines in one message, breaks functions apart, spots repeating patterns, identifies weak spots and proposes changes that raise efficiency, maintainability and security. Explanations include cross-references, pros and cons, and alternative designs, giving less experienced coders a clear map of possible options.
For framework migrations—say, moving a web app to a new stack—the model outlines entry-point shifts, state handling and routing, complete with checklists to avoid regressions. Because chat lacks runtime, execution moves to the Claude Code CLI, a separate sandbox and debugger.
On performance metrics, Claude Opus 4 stands out with a 72.5% score on SWE-bench, well above GPT-4.1’s 54.6%. It’s also known for sustaining continuity during long interactions—maintaining context for hours without logical drift, making it ideal for deep architectural revisions and extended debugging work.
Claude Sonnet 4 gives free users robust generation and refactoring of uploaded files.
The model handles long, multi-language scripts, spots inter-file dependencies and suggests readability, naming and structure improvements. “Extended thinking” mode unlocks deeper analysis for legacy or mixed-origin code, complete with tailored tests. Execution still requires an external environment, but clear diff-style patches cut the chance of new bugs.
Sonnet 4’s free tier doesn’t skimp on capability: it handles over 100,000 tokens of context and scores above 60% on standard code evaluation benchmarks, making it a standout option for developers who want thorough analysis without a subscription plan.
Gemini 2.5 Pro offers huge context and tight Workspace links, handing execution to Code Assist and Canvas.
Drive, Docs, Sheets and Canvas users paste full micro-services or linked documents; Gemini maps dependencies, drafts refactors across spreadsheets and draws UML diagrams. When it is time to run code, Code Assist spins up cloud containers for testing and deployment. Switching from a Python script to a Sheets macro—or integrating REST services—stays cohesive.
The model supports 128,000 tokens of context in the consumer product and reaches up to 1 million in AI Studio or Vertex AI. According to Google's internal evaluations, Gemini 2.5 Pro was preferred over GPT-4o in 67% of side-by-side comparisons for code generation. HumanEval scores range between 65% and 70%, depending on language, framework and complexity of the task.
Gemini 2.5 Flash prioritises speed and suits quick snippets and instant answers.
Responses appear in under a second, solving maths helpers, small automations or API calls. For multi-file analysis or deep refactors, Flash trims content and suggests upgrading to Pro. It shines in teaching, hackathons and rapid-fire brainstorming where speed outweighs exhaustive detail.
This model maintains a 128K-token window and returns top-tier response speed. For short snippets, it passes 60% of benchmark tasks. However, when complexity rises—especially across file boundaries or involving nested logic—accuracy drops closer to 40%, which aligns with its design tradeoff for speed over depth.
Project management favours ChatGPT Projects and Claude’s parallel uploads, while Gemini leans on Drive-centric flows.
ChatGPT Projects layers folders, versions and side panels, turning chat into a lightweight IDE whose model knows every dependency. Claude uploads many files at once and, thanks to its vast context window, keeps logic straight even without a graphical file map. Gemini relies on Drive’s repository, using metadata for versioning, comments and history—ideal for Workspace-based teams.
In terms of capacity, ChatGPT handles dozens of code files per session. Claude’s context management allows Opus 4 to work well with 200,000 tokens and Sonnet 4 with over 100,000. Gemini’s Drive-integrated flow supports hundreds of files via AI Studio or Vertex AI, though it’s more reliant on Workspace infrastructure for collaboration and context persistence.
Live execution and debugging remain ChatGPT’s domain, combining graphics, virtual shells and file-system commands.
Scripts run, tests appear and outputs stream inside one chat. Stack traces come with fixes; interactive notebooks and visualisations (heat maps, flow charts) help users grasp data quickly and adjust code on the fly.
Among consumer-facing models, only GPT-4o offers secure code execution directly in the chat interface. It scores over 75% on HumanEval Python challenges and helps reduce validation time by around 40% compared to chat-only models that require external execution steps.
Claude leads in teaching complex algorithms; ChatGPT keeps explanations concise; Gemini stays midpoint with cloud-centric references.
Opus 4 builds narratives around algorithms, detailing complexity and best practices. ChatGPT delivers solutions ready for immediate use, with minimal digression. Gemini mixes completeness and brevity, inserting links to Google Cloud documentation and production tips.
In the enterprise, licensing terms, completion rates and IDE or CI/CD integration drive the final decision.
ChatGPT meshes with GitHub, drafts pull requests and suggests fixes during automated tests and builds. Claude provides exhaustive documentation, thorough rationale and fine-grained control over large refactors. Gemini integrates naturally with Google Workspaces, Artifact Registry and cloud logging, supporting distributed teams that value rapid sharing and granular permissions.
Summary Table: Coding Capabilities of ChatGPT, Claude, and Gemini Models
Legend:
Context Size: Maximum context window (tokens) per chat/session
Code Execution (Chat): Ability to execute code within the chat interface
HumanEval Pass %: Score in standard code-generation benchmarks
File Management: Project, file and workflow support
Main Strengths: Standout features for code writing, execution, explanation, or collaboration
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