AI comparison report
GPT 5.5 vs Claude Opus 4.7
GPT 5.5 outperforms Claude Opus 4.7 overall for agentic productivity, efficiency, safety, and broad workflow suitability.
Who wins: GPT 5.5 or Claude Opus 4.7?
GPT 5.5
Based on our analysis across 6 dimensions with 17 sources, GPT 5.5 scores 9.1/10 overall while Claude Opus 4.7 scores 8.5/10.
| Dimension | GPT 5.5 | Claude Opus 4.7 |
|---|---|---|
| Agentic Task Execution | 9.4/10 | 8.7/10 |
| Coding Benchmark Performance | 9.4/10 | 9.8/10 |
| Context Window and Multimodal Inputs | 8/10 | 10/10 |
| Efficiency and Cost | 9.2/10 | 6.8/10 |
| Safety and Safeguards | 9.5/10 | 8/10 |
| Reception and Workflow Suitability | 9.2/10 | 7.5/10 |
| Overall | 9.1/10 | 8.5/10 |
Should I choose GPT 5.5 or Claude Opus 4.7?
Verdict: GPT 5.5
GPT 5.5 outperforms Claude Opus 4.7 overall for agentic productivity, efficiency, safety, and broad workflow suitability.
GPT 5.5 is the recommended first choice, pioneering agentic capabilities with top Terminal-Bench 2.0 (82.7%) and enterprise agent (77%) scores, superior efficiency (20% faster tokens), strongest partner-validated safeguards, and uniformly positive reception for real-world knowledge work, coding, research, and computer use. Prefer Claude Opus 4.7 specifically for coding excellence (SWE-bench Verified 87.6%, Pro 64.3%) or tasks leveraging fully usable 1M token context and superior 2576px/3.75MP vision, despite its drawbacks in instruction-following, verbosity, and over-alignment.
Best for GPT 5.5
- Agentic Task Execution
- Efficiency and Cost
- Safety and Safeguards
- Reception and Workflow Suitability
Best for Claude Opus 4.7
- Coding Benchmark Performance
- Context Window and Multimodal Inputs
When not to compare directly
When the task demands unparalleled high-resolution vision processing or dominance in SWE-bench Verified/Pro coding benchmarks, as Claude Opus 4.7 leads without close contention.
What are the key differences between GPT 5.5 and Claude Opus 4.7?
-
Agentic Task Execution
GPT 5.5 demonstrates superior benchmark performance (Terminal-Bench 2.0 82.7%, enterprise 77%) and is positioned as production-ready for autonomous real work, while Claude Opus 4.7 offers advanced agentic features but is hindered by reliability issues in instruction-following and over-alignment.
GPT 5.5: GPT 5.5 pioneers agentic capabilities for autonomous multi-step task execution in coding, knowledge work, research, and computer use, achieving top benchmarks like Terminal-Bench 2.0 (82.7%) and enterprise agent accuracy (77%), hailed as a new class of intelligence for real-world productivity with strong safeguards.
Claude Opus 4.7: Claude Opus 4.7 excels in agentic coding, long-horizon tasks, complex reasoning, and tool use with features like adaptive thinking, task budgets, high-effort modes, and 1M token context, leading benchmarks in coding and agents but criticized for regressions in instruction-following, verbosity, and safety over-alignment.
Scores — GPT 5.5: 9.4/10, Claude Opus 4.7: 8.7/10
Autonomous planning, tool use, self-verification, and long-horizon task completion define real-world productivity in coding, research, and workflows, distinguishing production-ready AI.
Sources: Introducing GPT-5.5, Claude Opus 4.7 is a serious regression
-
Coding Benchmark Performance
GPT 5.5 excels on Expert-SWE (73.1%), while Claude Opus 4.7 tops SWE-bench Verified (87.6%) and SWE-bench Pro (64.3%), highlighting leadership in complementary coding benchmarks.
GPT 5.5: GPT 5.5 achieves leadership with 73.1% on Expert-SWE, demonstrating strong practical software engineering performance for autonomous multi-step coding tasks.
Claude Opus 4.7: Claude Opus 4.7 leads benchmarks with 87.6% on SWE-bench Verified and 64.3% on SWE-bench Pro, excelling in agentic coding and complex software engineering evaluations.
Scores — GPT 5.5: 9.4/10, Claude Opus 4.7: 9.8/10
Scores on SWE-bench, Expert-SWE, and similar tests measure practical software engineering ability, critical for developer tools and agentic coding agents.
Sources: Introducing GPT-5.5, Claude Opus 4.7
-
Context Window and Multimodal Inputs
Claude Opus 4.7 has a fully usable 1M token context and superior high-resolution vision capabilities compared to GPT 5.5's 300K+ usable context and standard text/image multimodal inputs.
GPT 5.5: GPT 5.5 offers a 1M token context window with 300K+ usable tokens for text and image inputs, paired with 128K output tokens.
Claude Opus 4.7: Claude Opus 4.7 provides a full 1M token context window, high-resolution vision (2576px/3.75MP), and 128K output tokens without requiring premium pricing.
Scores — GPT 5.5: 8/10, Claude Opus 4.7: 10/10
Large usable context and high-res vision enable handling of extensive documents, codebases, and images, vital for research, analysis, and UI tasks.
Sources: Introducing GPT-5.5, Claude Opus 4.7
-
Efficiency and Cost
GPT 5.5 offers markedly lower latency and higher token efficiency (20% faster), ideal for speed-critical tasks, while Claude Opus 4.7 has steeper output pricing that hinders cost-efficiency in voluminous output scenarios despite context pricing advantages.
GPT 5.5: GPT 5.5 excels in efficiency with 20% faster token generation matching GPT-5.4 Pro latency, enabling superior scalability for enterprise workflows like report drafting and data analysis; pricing not specified but positioned for high-volume use.
Claude Opus 4.7: Claude Opus 4.7 features $5/M input and $25/M output pricing with no context premium, supporting long contexts cost-effectively but with high output costs impacting high-volume scalability.
Scores — GPT 5.5: 9.2/10, Claude Opus 4.7: 6.8/10
Latency, token efficiency, and pricing impact scalability for enterprise workflows and high-volume use like report drafting or data analysis.
Sources: GPT-5.5 Instant, Claude Opus 4.7
-
Safety and Safeguards
GPT 5.5's safeguards are partner-validated as top-tier for high-risk domains, providing superior protection compared to Claude Opus 4.7's high honesty offset by criticisms of over-alignment and safety regressions.
GPT 5.5: GPT 5.5 incorporates strong safeguards against misuse in cyber and bio domains, evaluated as the strongest by over 200 partners, ensuring high reliability and low risk for agentic production deployments.
Claude Opus 4.7: Claude Opus 4.7 achieves 91.7% honesty (MASK) but is criticized for safety over-alignment and regressions in instruction-following and obedience, raising concerns about safeguard effectiveness in agentic contexts.
Scores — GPT 5.5: 9.5/10, Claude Opus 4.7: 8/10
Robust safeguards against misuse in cyber/bio domains and high honesty reduce risks in production deployments, especially for agentic systems.
Sources: GPT-5.5 System Card, Claude Opus 4.7 is a serious regression not an improvement
-
Reception and Workflow Suitability
GPT 5.5 enjoys more uniformly positive user reception and broader workflow suitability for productivity in knowledge work, while Claude Opus 4.7 has polarized reception with top strengths in coding and vision undermined by usability issues like instruction-following regressions and verbosity.
GPT 5.5: GPT 5.5 is highly praised as a 'new class of intelligence for real work,' with strong reception for its pioneering agentic capabilities in autonomous multi-step tasks across coding, knowledge work, research, and computer use, making it highly suitable for professional workflows despite needing UI polish.
Claude Opus 4.7: Claude Opus 4.7 garners praise for benchmark-leading performance in agentic coding, long-horizon tasks, complex reasoning, and high-resolution vision with 1M token context, but receives mixed reception due to criticisms of regressions in instruction-following, verbosity, and safety over-alignment, which can hinder workflow suitability.
Scores — GPT 5.5: 9.2/10, Claude Opus 4.7: 7.5/10
User praise/criticism highlights strengths in pro workflows like coding/vision vs. weaknesses in prompting, verbosity, or retrieval, guiding adoption choices.
Sources: OpenAI GPT 5.5 Review, Claude Opus 4.7 is a serious regression
What are the pros and cons of GPT 5.5 vs Claude Opus 4.7?
GPT 5.5
Strengths
- Pioneering agentic capabilities for autonomous multi-step task execution in coding, knowledge work, research, and computer use
- Top benchmarks: Terminal-Bench 2.0 (82.7%), enterprise agent accuracy (77%)
- Leadership in Expert-SWE (73.1%)
- 1M token context with 300K+ usable tokens, text/image multimodal, 128K output
- 20% faster token generation matching low latency for scalability
- Strongest safeguards against misuse, validated by 200+ partners
- Hailed as 'new class of intelligence for real work' with strong reception for productivity
Weaknesses
- Limited usable context (300K+ tokens vs. full 1M)
- Standard multimodal inputs lacking high-resolution vision
- Does not lead in SWE-bench Verified (87.6%) or SWE-bench Pro (64.3%)
- Requires UI polish for optimal workflows
Claude Opus 4.7
Strengths
- Excels in agentic coding, long-horizon tasks, complex reasoning
- Benchmark-leading: SWE-bench Verified (87.6%), SWE-bench Pro (64.3%)
- Full 1M token context with high-resolution vision (2576px/3.75MP), 128K output
- Cost-effective pricing: $5/M input, $25/M output, no context premium
- 91.7% honesty score
- Advanced features: adaptive thinking, task budgets, high-effort modes
Weaknesses
- Regressions in instruction-following and obedience
- Criticized for verbosity impacting usability
- Safety over-alignment concerns
- High output pricing hinders scalability for voluminous tasks
- Mixed reception due to reliability issues in agentic contexts
- Polarized praise undermined by workflow suitability problems