Compare GPT-4.1 Nano and Claude 3.5 Haiku. Find out which model is better for your specific use case and requirements.
| Feature | GPT-4.1 Nano | Claude 3.5 Haiku |
|---|---|---|
| Provider | OpenAI | Anthropic |
| Model Type | text | text |
| Context Window | 1,047,576 tokens | 200,000 tokens |
| Input Cost | $0.10/ 1M tokens | $0.80/ 1M tokens |
| Output Cost | $0.40/ 1M tokens | $4.00/ 1M tokens |
1. Ultra-Fast, Low-Latency Performance
2. Most Cost-Efficient GPT-4.1 Variant
3. Solid Instruction Following
4. Strong Tool Calling Capabilities
5. Basic Multimodal Input
6. Text-Only Output
7. 1M-Token Context Window
8. Ideal Use Cases
9. Broad API Integration
1. Intelligence & Benchmark Performance
2. Coding Strength
Scores 40.6% on SWE-bench Verified, outperforming:
Excellent for engineering assistants, agent coding tasks, and bug fixing.
3. Speed & Latency
4. Tool Use & Instruction Following
5. Best Use Cases
Discover AI prompts that work great with these models. Use them to build powerful AI tools on Appaca.
Create a marketing experimentation framework to test and optimize persona-targeted messaging and offers that highlight your USP and address challenges.
Create clear, measurable learning objectives aligned to standards using Blooms Taxonomy action verbs.
Generate high-converting email subject lines that boost open rates using proven psychological triggers and A/B testing frameworks.
Analyze customer data patterns and convert insights into targeted messaging that emphasizes your USP and addresses persona challenges.
Create a loyalty program that rewards continued engagement and advocacy, reinforcing how your USP supports ongoing persona challenges.
Develop a marketing automation workflow that delivers relevant content by persona challenge while reinforcing your USP throughout the journey.
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