GPT-4 vs Claude 3.5 Sonnet: Quick Verdict
Both are frontier models. The right choice depends on your specific workload:
- GPT-4o — Best for multimodal tasks, tool use, and OpenAI ecosystem integrations
- Claude 3.5 Sonnet — Best for long documents, nuanced instruction-following, and coding
Specification Comparison
| Feature | GPT-4o | Claude 3.5 Sonnet | |---------|--------|-------------------| | Context Window | 128K tokens | 200K tokens | | Input Pricing | $5 / 1M tokens | $3 / 1M tokens | | Output Pricing | $15 / 1M tokens | $15 / 1M tokens | | Vision | ✅ Yes | ✅ Yes | | Function Calling | ✅ Yes | ✅ Yes (tools) | | Knowledge Cutoff | Apr 2024 | Apr 2024 | | API Provider | OpenAI | Anthropic |
Coding Performance
Claude 3.5 Sonnet consistently outperforms GPT-4o on coding benchmarks:
- HumanEval: Claude 92% vs GPT-4o 90.2%
- SWE-bench Verified: Claude resolves 49% of real GitHub issues vs GPT-4o's 38%
For agentic coding tasks (multi-file edits, debugging loops), Claude's longer context and better instruction-following give it an edge.
Reasoning & Analysis
GPT-4o has a slight edge on formal reasoning benchmarks (MATH, GPQA), but Claude handles ambiguous, multi-part instructions more reliably in practice. For business documents, legal review, and long-form analysis, Claude is the stronger choice.
Long Document Handling
With a 200K token context (vs GPT-4o's 128K), Claude can ingest:
- Full codebases
- Lengthy legal contracts
- Entire research papers with appendices
Multimodal Capabilities
GPT-4o was purpose-built as a multimodal model. It handles:
- Image analysis
- Audio input/output (native)
- Video frames
Claude 3.5 Sonnet supports image input but lacks native audio capabilities.
When to Choose GPT-4o
- You're deeply integrated with OpenAI's ecosystem (Assistants API, fine-tuning)
- Your task requires audio or complex image generation workflows
- You need the widest plugin/tool ecosystem
When to Choose Claude 3.5 Sonnet
- You're building coding agents or software engineering workflows
- Your inputs are long (contracts, codebases, research)
- You need precise instruction-following with minimal hallucination
- Cost matters — Claude's input pricing is 40% cheaper
Bottom Line
There is no universally "better" model. Run both on your actual data with your actual prompts. Most serious AI teams use both — routing tasks to whichever model performs best for that category.
Related: Prompt Engineering Techniques: A Complete 2025 Guide · Build vs Buy Your AI MVP · How We Ship AI MVPs in 3 Weeks
Need help picking and integrating the right model for your product? See our sprint model → or contact us →
Practical Applications
Understanding these concepts helps teams make better technology decisions. The right choice depends on your specific use case, team expertise, and project timeline.
When evaluating options, consider total cost of ownership, integration complexity, and long-term maintenance. Teams that invest time in proper evaluation upfront save months of rework later.
For startups building AI products, the fastest path to production is working with experienced teams who have shipped similar systems before. A 3-week sprint can validate your approach and deliver a working prototype.
Getting Started
The best way to evaluate any technology is to build with it. Start with a small proof-of-concept that tests your core assumptions, then iterate based on real user feedback.
Need help deciding? Book a 15-min scope call with our team to discuss your specific requirements and get a concrete recommendation.
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