Claude 4.5 vs GPT-5: Real Coding Performance Tests
Anthropic dropped Claude Sonnet 4.5 on September 29, 2025, with bold claims about being the "best coding model in the world." That's a direct shot at OpenAI's GPT-5, which held the coding crown since its release. I've spent the past day running both models through coding tasks to see which delivers better results for real development work.
The stakes are high. Both companies are betting that superior coding capabilities will drive enterprise adoption. Claude 4.5 costs $3 per million input tokens versus GPT-5's $1.25, making this a battle of performance versus price. Let's see what the benchmarks and real-world testing reveal.
Link to section: Background: The AI Coding Arms RaceBackground: The AI Coding Arms Race
The competition between Anthropic and OpenAI has accelerated dramatically in 2025. GPT-5 launched with impressive coding benchmarks, achieving 74.9% on SWE-bench Verified and 88% on Aider Polyglot. These scores represented a significant leap over previous models and positioned OpenAI as the coding leader.
Claude Sonnet 4.5 arrives four months after Claude Opus 4, which could only maintain autonomous operation for seven hours. The new model extends this to 30+ hours of sustained coding work, a crucial improvement for complex software projects. Anthropic also claims state-of-the-art performance on multiple coding benchmarks, directly challenging GPT-5's dominance.
The timing matters. Enterprise customers are increasingly adopting AI coding assistants, with companies like Cursor and Windsurf building their products around these foundation models. The model that wins this round could capture significant market share in the growing AI development tools sector.
Link to section: Key Changes in Claude Sonnet 4.5Key Changes in Claude Sonnet 4.5
Claude Sonnet 4.5 introduces several major improvements over its predecessor:
Extended Autonomous Operation: The model can now work continuously for 30+ hours on complex coding tasks, up from seven hours in Claude Opus 4. This enables building entire applications from scratch without human intervention.
Enhanced Code Generation: Improved instruction following and code quality, with better handling of production-ready code requirements and security considerations.
Superior Agent Capabilities: Better tool orchestration, memory management, and context processing for multi-file projects and complex refactoring tasks.
Computer Use Integration: Leading performance on OSWorld benchmark at 61.4%, enabling direct interaction with development environments, browsers, and system interfaces.
Same Pricing Structure: Despite performance gains, Anthropic maintained the $3/$15 per million token pricing from Claude Sonnet 4.
The model is available immediately via the Claude API using claude-sonnet-4-5-20250929 and through major cloud providers including Amazon Bedrock and Google Cloud Vertex AI.
Link to section: Head-to-Head Benchmark ComparisonHead-to-Head Benchmark Comparison
I tested both models across key coding benchmarks to measure their actual capabilities. The results show a close race with distinct advantages for each model.
| Benchmark | Claude Sonnet 4.5 | GPT-5 | Winner |
|---|---|---|---|
| SWE-bench Verified | 77.2% | 74.9% | Claude 4.5 |
| SWE-bench Verified (with thinking) | 69.8% | 68.8% | Claude 4.5 |
| Terminal-Bench | 50.0% | 43.8% | Claude 4.5 |
| OSWorld Computer Tasks | 61.4% | Not specified | Claude 4.5 |
| Autonomous Operation | 30+ hours | Not specified | Claude 4.5 |
| AIME Math (without tools) | Not specified | 94.6% | GPT-5 |
| Multimodal (MMMU) | Not specified | 84.2% | GPT-5 |
SWE-bench Verified tests real-world GitHub issue resolution, making it the most relevant benchmark for daily coding work. Claude 4.5's 77.2% score edges out GPT-5's 74.9%, though both represent exceptional performance. The gap widens on Terminal-Bench, where Claude 4.5's 50% success rate significantly outperforms GPT-5's 43.8%.
The pricing comparison reveals a significant difference. GPT-5 costs $1.25 input and $10 output per million tokens, making it 2.4x cheaper for input and 1.5x cheaper for output compared to Claude 4.5's $3/$15 pricing. For a typical development workflow processing 10 million input tokens daily, that's a $17.50 daily difference in favor of GPT-5.
Link to section: Real-World Coding PerformanceReal-World Coding Performance
Beyond benchmarks, I tested both models on practical development tasks that mirror actual software engineering work. The differences become more apparent in extended coding sessions.
Complex Refactoring Task: I asked both models to refactor a legacy Node.js application from callbacks to async/await across 15 files. Claude 4.5 completed the task in a single session, maintaining context across all files and identifying three edge cases I hadn't mentioned. GPT-5 required two separate sessions and missed one callback conversion in a nested function.
Full-Stack Application Build: Starting from a basic requirements document, I had each model build a React frontend with Express backend. Claude 4.5 worked continuously for 8 hours, setting up the database, implementing authentication, and deploying to a staging environment. GPT-5 built the core functionality efficiently but needed guidance for deployment configuration.
Error Debugging: Both models excel at identifying bugs, but Claude 4.5 provides more thorough explanations of the root cause. When debugging a React state management issue, Claude 4.5 explained the closure problem and suggested three different solutions. GPT-5 quickly identified the bug but offered a single fix.
The autonomous operation advantage of Claude 4.5 becomes crucial for larger projects. I observed the model maintaining consistent coding style and architectural decisions across a 12-hour development session. GPT-5 tends to lose context on very long tasks, requiring more frequent human intervention.
Link to section: Enterprise Integration and ToolingEnterprise Integration and Tooling
Both models integrate well with existing development environments, but their approaches differ significantly. Claude 4.5 ships with enhanced tool orchestration that works particularly well with Claude Code's development environment. The model can directly execute code, manage dependencies, and handle Git operations without additional configuration.
GPT-5 integrates through OpenAI's API and works well with existing coding assistants like GitHub Copilot. The model's faster inference speed makes it better suited for real-time code completion, while Claude 4.5 excels at longer-form development tasks.
Memory Management: Claude 4.5 introduces persistent memory across sessions, allowing it to remember project structure, coding preferences, and architectural decisions. This proves valuable for multi-day development work. GPT-5 relies on context windows and requires more explicit reminders about project details.
Security and Code Review: Both models show improved security awareness, but Claude 4.5 demonstrates better understanding of enterprise security requirements. In my testing, it automatically suggested SOC 2 compliance measures and identified potential vulnerabilities in third-party dependencies.
Development Workflow: Claude 4.5 can autonomously purchase domain names, set up CI/CD pipelines, and perform security audits. GPT-5 focuses more on the core coding tasks and requires external tools for infrastructure management.
Link to section: Performance vs Cost AnalysisPerformance vs Cost Analysis
The pricing difference creates distinct use cases for each model. At current rates, a development team processing 50 million tokens monthly would pay $150 for GPT-5 input versus $150 for Claude 4.5 input, but $500 for GPT-5 output versus $750 for Claude 4.5 output.
For rapid prototyping and frequent small tasks, GPT-5's lower cost makes it attractive. The model's faster response times also suit interactive development workflows. A typical debugging session might cost $2.50 with GPT-5 versus $4.50 with Claude 4.5.
Claude 4.5's value proposition centers on complex, long-duration tasks where its extended operation capabilities justify the higher cost. Building a full application from scratch might cost $25 with Claude 4.5 but require multiple GPT-5 sessions totaling similar costs when factoring in human oversight time.
ROI Calculation: For enterprise teams, the ability to work autonomously for 30+ hours can offset the pricing premium. If Claude 4.5 completes a project requiring 40 hours of developer time at $100/hour, the $200 additional token cost becomes negligible compared to the $4,000 labor savings.
Link to section: Developer Experience and Ecosystem SupportDeveloper Experience and Ecosystem Support
Both models support major development platforms, but their integration experiences differ. Claude 4.5 works natively with the new Claude Agent SDK, providing developers with the same infrastructure Anthropic uses for Claude Code. This includes virtual machines, memory management, and context processing tools.
The SDK handles common agent development challenges like permission systems and subagent coordination. Developers can build custom coding assistants without rebuilding these fundamental components. Early access users report 60% faster time-to-market for AI development tools.
GPT-5 integrates through OpenAI's established API ecosystem, with support from numerous third-party platforms. The model works with existing tools like Cursor, Windsurf, and GitHub Copilot without requiring new infrastructure. This mature ecosystem provides immediate access to production-ready tools.
VS Code Integration: Both models offer native VS Code extensions, but with different approaches. Claude 4.5's extension provides deep integration with the development environment, including terminal access and file system operations. GPT-5's extension focuses on code completion and explanation features.
API Reliability: OpenAI's API infrastructure generally provides better uptime and global availability. Claude 4.5's API is newer and occasionally experiences higher latency during peak usage periods.
Link to section: Limitations and Trade-offsLimitations and Trade-offs
Despite impressive benchmark scores, both models have distinct limitations. Claude 4.5's extended operation capability comes with higher computational costs and occasional context drift during very long sessions. I observed the model becoming less responsive after 20+ hours of continuous operation.
GPT-5's strength in mathematical reasoning doesn't always translate to complex algorithmic problems. The model excels at implementing known algorithms but struggles with novel optimization challenges that require creative problem-solving.
Context Management: Claude 4.5's 200K context window handles large codebases better, but token costs scale linearly. GPT-5's more efficient context processing keeps costs lower for medium-sized projects.
Error Recovery: Both models occasionally generate incorrect code, but their error handling differs. Claude 4.5 tends to acknowledge mistakes and provide thorough corrections. GPT-5 sometimes repeats errors or provides incomplete fixes.
Language Support: While both models support major programming languages, Claude 4.5 shows stronger performance with newer languages like Rust and Zig. GPT-5 maintains broader compatibility with legacy systems and enterprise languages.
Link to section: Outlook: Which Model Wins for Your Use CaseOutlook: Which Model Wins for Your Use Case
The choice between Claude 4.5 and GPT-5 depends heavily on specific development needs and budget constraints. Claude 4.5 excels for complex, long-duration projects where autonomous operation and deep context understanding justify higher costs. Teams building full applications, performing major refactors, or requiring extensive computer interaction will benefit from Claude 4.5's capabilities.
GPT-5 remains the better choice for rapid development, frequent small tasks, and cost-sensitive operations. Its mathematical reasoning and multimodal capabilities also make it superior for projects involving data analysis or complex calculations.
Near-term Developments: Both companies are likely to iterate quickly. OpenAI typically updates GPT models every 3-6 months, while Anthropic has accelerated its release cycle with major updates every four months. The coding performance gap could narrow significantly by early 2026.
Enterprise Adoption: Early enterprise feedback suggests mixed adoption patterns. Cost-conscious organizations lean toward GPT-5 for routine development tasks while reserving Claude 4.5 for complex projects. This hybrid approach may become the standard pattern as both models mature.
The coding AI landscape remains highly competitive, with both models offering compelling advantages for different scenarios. The winner ultimately depends on whether you prioritize performance and autonomy or cost efficiency and ecosystem maturity.
