GPT-4o Mini vs. Claude 3.5 Haiku: A Real-World Performance Comparison
This report analyzes the performance of GPT-4o Mini (OpenAI) and Claude 3.5 Haiku (Anthropic) based on a live test conducted on December 22, 2024, using a dedicated comparison tool.
https://www.llmkit.cc/model-comparison/claude-3-5-haiku-20241022-vs-gpt-4o-mini
📊 Comparison Summary
The test utilized the prompt: "Hello, how are you?".
| Metric | GPT-4o Mini (MegaLLM) | Claude 3.5 Haiku (Anthropic) | Winner |
|---|---|---|---|
| Response Time | 1,925ms | 1,327ms | 🏆 Claude 3.5 Haiku |
| Cost per Request | $0.0000315 | $0.00024 | 🏆 GPT-4o Mini |
| Tokens Used | 42 | 40 | Tie |
| Context Window | 128K | 200K | 🏆 Claude 3.5 Haiku |
🚀 Speed & Performance
Claude 3.5 Haiku is the clear winner for performance-critical applications.
- It delivered a response in 1,327ms, making it approximately 31% faster than GPT-4o Mini.
- This low latency makes it ideal for real-time applications and chat interfaces where user experience is a top priority.
💰 Cost Efficiency
GPT-4o Mini is the budget-friendly leader, being significantly more affordable for high-volume tasks.
- It was 87% cheaper per request in our test ($0.0000315 vs $0.00024).
- Input pricing is 85% lower ($0.00015/1K tokens), and output pricing is 88% lower ($0.0006/1K tokens) than Claude 3.5 Haiku.
- For a high-volume service (10 million requests), GPT-4o Mini would cost roughly $315, whereas Claude 3.5 Haiku would reach $2,400.
🧠 Capabilities & Context
- Claude 3.5 Haiku offers a larger 200K context window, making it superior for processing long documents or complex analysis.
- GPT-4o Mini provides a 128K context window but includes additional features like vision capabilities and function calling support.
🎯 Final Verdict
Choose GPT-4o Mini if:
- ✅ Cost is the primary concern (ideal for high-volume automation).
- ✅ You need vision capabilities or robust function calling.
- ✅ A 128K context window meets your project requirements.
Choose Claude 3.5 Haiku if:
- ✅ Speed is critical for a snappy user experience.
- ✅ You need a larger 200K context window for massive documents.
- ✅ You prioritize response quality and nuance over raw operational cost.
Results generated using LLMKit comparison tool.
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