Claude 4.7 Opus vs GPT-5 Comparison
Anthropic's flagship model with extended thinking and xhigh effort
OpenAI's flagship, general-purpose leader
Quick Verdict
For code, agentic work, and enterprise use cases, Claude 4.7 Opus wins. For math, creative writing, and general-purpose tasks, GPT-5 wins. In production, the optimal setup is a router pattern that picks per task — yielding roughly 20% better quality and 30% cost savings.
Score Comparison
Detailed Scoring
| Category | Claude 4.7 Opus | GPT-5 |
|---|---|---|
| Performance | 10/10 | 10/10 |
| Ease of Learning | 8/10 | 9/10 |
| Ecosystem | 9/10 | 10/10 |
| Community | 9/10 | 10/10 |
| Job Market | 10/10 | 10/10 |
| Future-Proof | 10/10 | 10/10 |
Pros & Cons
Claude 4.7 Opus
Pros
- SWE-bench Verified score of 72.5% in xhigh mode — the coding leader
- Extended thinking in xhigh mode with 32k reasoning tokens
- Optional 1M-token context window
- Prompt caching delivers up to 90% savings
- Safety and alignment tuned as a secure default for enterprise
- 95% tool-use accuracy
- Native parallel tool use
- Strong, context-aware Turkish performance
Cons
- Priced at $15/1M input and $75/1M output tokens — pricier than OpenAI
- Temperature and top_p are unsupported (a breaking change)
- Prefill is unsupported
- Latency is high (30-60s in xhigh mode)
Best For
GPT-5
Pros
- 94.5% on MATH and 94.5% on AIME 2025 — the math/reasoning leader
- 500k-token context window
- Multimodal: text, image, and video
- Mature tool use and function calling
- Plugin ecosystem plus the Assistants API
- ChatGPT Plus/Pro integration gives it consumer reach
- Reliable structured output (JSON mode)
- Priced at $20/1M input and $80/1M output tokens
Cons
- SWE-bench Verified score of 68.2% — below Claude's
- 91% tool-use accuracy — below Claude's 95%
- Safety tuning is sometimes over-cautious
- Lower API rate limits for enterprise
- Prompt caching isn't as mature as Claude's
Best For
Code Comparison
from anthropic import Anthropic
client = Anthropic()
response = client.messages.create(
model="claude-opus-4-7",
extended_thinking={"enabled": True, "effort_level": "xhigh", "budget_tokens": 20000},
max_tokens=4096,
messages=[{"role": "user", "content": "Refactor this 2000-line Flask app to FastAPI"}]
)from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="gpt-5",
messages=[{"role": "user", "content": "Explain quantum entanglement"}],
reasoning_effort="high"
)Conclusion
For code, agentic work, and enterprise use cases, Claude 4.7 Opus wins. For math, creative writing, and general-purpose tasks, GPT-5 wins. In production, the optimal setup is a router pattern that picks per task — yielding roughly 20% better quality and 30% cost savings.
Get Free ConsultationFrequently Asked Questions
They're close on average across benchmarks. Claude leads on code and agentic tasks, while GPT-5 leads on math and reasoning. "Smarter" is subjective and depends on the task.