GPT-5 Pro vs Claude 4.7 Opus

1. Highest reasoning quality in the GPT-5 family

• Uses significantly more compute to "think harder" before responding.
• Designed for the toughest reasoning tasks where answer quality matters more than speed.
• Produces more precise, reliable, and detailed outputs than standard GPT-5.

2. Advanced multi-turn reasoning via Responses API

• Available only in the Responses API to support:
  • Multi-turn internal model interactions before returning a reply.
  • Advanced control patterns (e.g., background mode for long-running jobs).
• Ideal for complex workflows, deep planning, and multi-step analysis.

3. Configured for maximum effort by default

• Always runs with reasoning.effort: 'high' (no lower-effort mode).
• Prioritizes depth and correctness over latency and cost.

4. Multimodal input

• Accepts text + image as input.
• Outputs text, with strong instruction-following and analysis capabilities.

5. Tooling and ecosystem integration

• Supports Web Search, File Search, and Image Generation (as tools).
• Supports MCP and other Responses API tooling patterns.
• Does not support Code Interpreter and does not support Computer Use, keeping focus on pure reasoning + tools.

1. State-of-the-art software engineering

• A notable upgrade over Opus 4.6 on the hardest coding tasks, with users reporting they can hand off work that previously required close supervision.
• Early partners reported double-digit gains on real-world benchmarks — e.g., Cursor saw CursorBench jump from 58% to 70%, and Rakuten-SWE-Bench resolution tripled versus Opus 4.6.
• Handles complex, long-running tasks with rigor: plans carefully, catches its own logical faults, and verifies its outputs before reporting back.

2. Long-horizon agent reliability

• Full 1M token context window at standard pricing, with state-of-the-art long-context consistency.
• Far fewer tool errors, stronger recovery from tool failures, and better follow-through on multi-step workflows — designed for async work like CI/CD, automations, and managing multiple agents in parallel.
• Stronger file-system-based memory, retaining useful notes across long, multi-session runs.

3. Sharper instruction following and honesty

• Takes instructions literally and precisely — existing prompts may need re-tuning since earlier models were more lenient.
• More honest about its own limits: reports missing data instead of fabricating plausible-but-wrong answers, and resists dissonant-data traps that tripped up Opus 4.6.

4. Substantially improved vision and multimodal reasoning

• Accepts images up to 2,576 px on the long edge (~3.75 MP) — over 3x more than prior Claude models.
• Unlocks dense-screenshot computer use, complex diagram extraction, and pixel-perfect reference tasks.
• Stronger document reasoning for enterprise analysis (e.g., 21% fewer errors than Opus 4.6 on Databricks' OfficeQA Pro).

5. Top-tier professional knowledge work

• State-of-the-art on the Finance Agent evaluation and GDPval-AA, with tighter, more professional finance analyses, models, and presentations.
• Strong on legal work — e.g., 90.9% on BigLaw Bench at high effort, with better-calibrated reasoning on review tables and ambiguous edits.
• Noted by design-focused partners as the best model for building dashboards and data-rich interfaces.

6. Modern effort and budget controls

• Introduces a new xhigh effort level between high and max for finer control over reasoning vs. latency.
• Task budgets (public beta) let developers guide token spend across long runs.
• Recommended to start with high or xhigh effort for coding and agentic use cases.