Model Comparison
Pick two models and compare pricing, capabilities, and recommendations side by side.
Radar Comparison
Value for Money
Benchmark score per dollar spent (higher = better value). Combines MMLU, reasoning, and code benchmarks against total API cost.
| Feature | GPT-5.5 OpenAI | Claude 4 Sonnet Anthropic |
|---|---|---|
| Provider | OpenAI | Anthropic |
| Tier | flagship | flagship |
| Input | $5.00 | $3.00 |
| Output | $30.00 | $15.00 |
| Cached | $0.500 | $0.300 |
| Batch Input | $2.50 | $1.50 |
| Context | 1M | 200K |
| Max Output | 128,000 | 16,384 |
| Output Speed | ~85 tok/s | ~70 tok/s |
| Rate Limit | 5,000 RPM | 4,000 RPM |
| Multimodal | Image Input | Image Input |
| Vision | ||
| Function Calling | ||
| Fine-tuning | ||
| JSON Mode | ||
| Knowledge Cutoff | 2025-06 | 2025-03 |
| Free Tier | ||
| MMLU | 90.0% | 88.0% |
| SWE-bench Verified | 82.7% | 63.8% |
| Terminal-Bench | 82.7% | — |
| GeneBench | 28.5% | — |
| HumanEval | — | 91.0% |
| MATH | — | 72.5% |
API Code Examples
from openai import OpenAI
client = OpenAI(api_key="YOUR_API_KEY")
response = client.chat.completions.create(
model="gpt-5.5",
messages=[{"role": "user", "content": "Hello!"}],
max_tokens=1024,
)
print(response.choices[0].message.content)import anthropic
client = anthropic.Anthropic(api_key="YOUR_API_KEY")
message = client.messages.create(
model="claude-4-sonnet",
max_tokens=1024,
messages=[{"role": "user", "content": "Hello!"}],
)
print(message.content[0].text)Use-Case Recommendations
AI-powered analysis across 8 real-world scenarios — see which model fits your needs best.
Agentic Coding
Code generation, debugging, refactoring with tool use and function calling.
Content Generation
Articles, marketing copy, translations, and long-form writing.
Data Analysis & Reasoning
Math, logic, scientific analysis, and complex multi-step reasoning.
Real-time / Low-latency
Chatbots, live support, streaming responses where speed matters most.
Long Document Processing
Processing large codebases, legal documents, research papers, and books.
Multimodal Applications
Image understanding, document OCR, audio transcription, visual Q&A.
Budget-Conscious Production
High-volume API usage where cost per token is the primary concern.
Enterprise & Reliability
Production workloads needing structured output, fine-tuning, and high rate limits.
GPT-5.5
Pros
- State-of-the-art agentic coding (82.7% Terminal-Bench)
- 1M token context window
- Matches GPT-5.4 latency with fewer tokens
- Strong scientific research capability
Cons
- 2x more expensive than GPT-5.4
- No fine-tuning support yet
- Output costs $30/M tokens
When to use: Best for complex coding agents, computer use, and professional workloads where accuracy matters most.
Claude 4 Sonnet
Pros
- Top-tier writing quality
- 5x cheaper than Opus
- Excellent coding with 200K context
Cons
- Lower max output than Opus
- No fine-tuning
- Slower than GPT-4o mini
When to use: Best balance of quality and cost for writing, coding, and analysis production apps.