Context utilization — 256K context with strong recall throughout the window
🟤 Opus 4.7 Strengths
Coding champion — Best-in-class for real-world software engineering, not just toy problems
Agentic reliability — Extended thinking makes multi-step tool chains significantly more reliable
Writing quality — More natural, less formulaic, preferred by humans in blind tests
Instruction following — Remarkably faithful to complex, multi-constraint prompts
Safety/alignment — Fewer hallucinations on factual queries, better calibrated uncertainty
⚠️ Weaknesses to Watch
GPT-5.5: Can be verbose and formulaic in creative tasks. Occasionally over-confident on ambiguous questions. Tool use chains still sometimes break on complex multi-step flows.
Opus 4.7: Slower inference means higher cost at scale. 200K context (vs 256K) can matter for very long documents. Smaller ecosystem — fewer plug-and-play integrations.
Pricing Comparison
Cost per 1M tokens (API pricing, April 2026)
GPT-5.5
Input$8.00
Output$24.00
Cached Input$2.00
Batch (50% off)$4.00 / $12.00
Image InputIncluded
Opus 4.7
Input$15.00
Output$75.00
Cached Input$1.88
Batch (50% off)$7.50 / $37.50
Extended Thinking$15.00 / tok
💡 GPT-5.5 is ~3x cheaper per output token. For high-volume use cases, that adds up fast.
The Bottom Line
GPT-5.5 for speed, math, multimodal, and cost efficiency. Opus 4.7 for coding, agents, writing, and instruction following.
There is no single "best" model in April 2026. The frontier is a Pareto curve — choose based on your use case, latency requirements, and budget. The real winner is the developer who uses both strategically.