GPT-5.6 Sol vs Claude 4.7 Opus

1. Frontier Coding & Agentic Performance

• Scores 88.8% on Terminal-Bench 2.1 and 72.7% on DeepSWE v1.1, with stronger performance across complex terminal workflows and long-horizon engineering.
• Programmatic Tool Calling can coordinate tools, process intermediate results, and adapt workflows with fewer model round trips.

2. Maximum Capability on Demand

• Adds max reasoning effort for difficult tasks that benefit from deeper exploration, checking, and revision.
• Multi-agent ultra coordinates parallel agents for demanding work, reaching 91.9% on Terminal-Bench 2.1 and 92.2% on BrowseComp in OpenAI's evaluations.

3. Strong Computer Use, Design & Knowledge Work

• Scores 62.6% on OSWorld 2.0 and 90.4% on BrowseComp in standard mode.
• Produces more polished interfaces, presentations, documents, and spreadsheets while following reference formats more accurately.

4. Long Context & Broad Tool Support

• Supports a 1.05M-token context window, up to 128K output tokens, and text plus image input.
• Works with web search, file search, image generation, code interpreter, hosted shell, computer use, MCP, and other Responses API tools.

5. Stronger Science, Cybersecurity & Safeguards

• Improves scientific and defensive cybersecurity performance, including 28.7% on GeneBench Pro and 73.5% on ExploitBench.
• Uses layered safeguards, real-time checks, monitoring, and access controls for higher-risk capabilities.

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.