Both models are priced at $5.00 per 1M input tokens, making this a rare case where the input cost is identical. The key difference is on the output side: Claude Opus 4.7 is $5 cheaper per 1M output tokens ($25 vs $30), a 17% saving that adds up quickly for output-heavy workloads.
Cost Comparison by Use Case
1. Chatbot (500 requests/day, 1500 input + 800 output tokens)
| Model | Input/mo | Output/mo | Total/mo |
|---|---|---|---|
| GPT-5.5 | $112.50 | $360.00 | $472.50 |
| Claude Opus 4.7 | $112.50 | $300.00 | $412.50 |
Winner: Claude Opus 4.7 — saves $60/month (13%) on chatbot workloads.
2. Code Generation (200 requests/day, 2000 input + 1500 output tokens)
| Model | Input/mo | Output/mo | Total/mo |
|---|---|---|---|
| GPT-5.5 | $60.00 | $270.00 | $330.00 |
| Claude Opus 4.7 | $60.00 | $225.00 | $285.00 |
Winner: Claude Opus 4.7 — saves $45/month (14%) on code generation.
3. Document Analysis (100 requests/day, 5000 input + 1000 output tokens)
| Model | Input/mo | Output/mo | Total/mo |
|---|---|---|---|
| GPT-5.5 | $75.00 | $90.00 | $165.00 |
| Claude Opus 4.7 | $75.00 | $75.00 | $150.00 |
Winner: Claude Opus 4.7 — saves $15/month (9%) on document analysis.
Context Window: GPT-5.5's Big Advantage
1M vs 200K tokens
GPT-5.5 supports a 1 million token context window — 5x larger than Claude Opus 4.7's 200K. This matters for:
- Long document processing: Analyze entire codebases, legal contracts, or research papers in a single request
- Multi-turn conversations: Maintain longer conversation history without losing context
- RAG pipelines: Feed more retrieved documents into the context window
If your workload requires processing very long documents, GPT-5.5's 1M context may be worth the extra $5/1M output tokens.
When to Choose GPT-5.5
- You need a 1M context window for long documents or codebases
- You're building on OpenAI's ecosystem (function calling, tool use, Assistants API)
- You need GPT-5.5's specific capabilities (multimodal, real-time data access)
- Your workload is input-heavy (where pricing is identical)
When to Choose Claude Opus 4.7
- Your workload is output-heavy (17% cheaper on output tokens)
- You need Anthropic's safety and alignment approach
- You're building with Claude's extended thinking capabilities
- 200K context is sufficient for your use case
The Hybrid Strategy
For maximum cost efficiency, consider using both models:
- GPT-5.5 for tasks requiring long context (document analysis, codebase review)
- Claude Opus 4.7 for output-heavy tasks (code generation, creative writing, detailed analysis)
- Budget models (GPT-4o mini, Claude Haiku) for simple tasks like classification and Q&A
Cost Optimization Tips
- Use max_tokens: Set output limits to prevent runaway generation
- Cache prompts: Reuse system prompts and common prefixes
- Batch requests: Combine multiple queries into single API calls where possible
- Monitor usage: Track your token consumption to identify optimization opportunities
Calculate your exact costs: Use our free calculator to compare GPT-5.5 and Claude Opus 4.7 for your specific workload.
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