Model comparison
GPT-5.5 Pro vs Claude Sonnet 4.6
Compare GPT-5.5 Pro vs Claude Sonnet 4.6 API pricing: input/output token costs, cache pricing, context windows, workload estimates, and routing fit.
GPT-5.5 Pro
openai · gpt-5.5-pro
- Input
- $30
- Output
- $180
- Context
- 1.1M
Claude Sonnet 4.6
anthropic · claude-sonnet-4-6
- Input
- $3
- Output
- $15
- Context
- 1M
Quick take
Claude Sonnet 4.6 has the lower input price at $3 per 1M input tokens. Claude Sonnet 4.6 is cheaper for the example blended workload below. GPT-5.5 Pro has the larger context window at 1.1M tokens.
Choose GPT-5.5 Pro if...
- GPT-5.5 Pro is safer for long documents, repository analysis, and RAG prompts because it has the larger context window.
- GPT-5.5 Pro gives more room for long generated answers, reports, or code output.
Choose Claude Sonnet 4.6 if...
- Claude Sonnet 4.6 is the better default for cost-sensitive traffic and repeated high-volume calls.
Example workload cost
Estimates use input tokens plus 20% output tokens. They exclude provider discounts, cache hits, and tool/search surcharges.
| Workload | GPT-5.5 Pro | Claude Sonnet 4.6 | Cheaper |
|---|---|---|---|
| 1M input + 200K output | $66.00 | $6.00 | Claude Sonnet 4.6 |
| 10M input + 2M output | $660.00 | $60.00 | Claude Sonnet 4.6 |
| 100M input + 20M output | $6,600.00 | $600.00 | Claude Sonnet 4.6 |
Context, output, and capability fit
GPT-5.5 Pro provides the larger context window. Check max output separately when the task needs long reports, code generation, or full-document rewrites.
- GPT-5.5 Pro max output
- 128K
- Claude Sonnet 4.6 max output
- 64K
- GPT-5.5 Pro features
- prompt caching, function calling, vision
- Claude Sonnet 4.6 features
- prompt caching, function calling, vision
Risk notes for GPT-5.5 Pro
- High output price: cap max tokens for verbose generation workloads.
Risk notes for Claude Sonnet 4.6
- No major capability risk is flagged in this snapshot, but provider pages should still be verified before production routing.
Routing tags
frontierpremiumreasoningcodingagentslong-contextmultimodalcache-friendlyragenterprise-rag