Claude 4.7 Opus vs Claude 3.5 Sonnet

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.

1. Intelligence & Reasoning

• Outperforms previous Claude models and competitor LLMs across major benchmarks.
• Excels in graduate-level reasoning (GPQA), knowledge tasks (MMLU), and coding (HumanEval).
• Handles nuance, humor, and complex instructions with human-like clarity.

2. Speed & Efficiency

• Runs 2x faster than Claude 3 Opus, making it ideal for real-time and high-volume workflows.
• Cost-effective pricing: $3/M input tokens and $15/M output tokens.
• Supports a 200K token context window, enabling rich, long-form reasoning.

3. Coding Capabilities

• Solves significantly more coding and bug-fix tasks (64% vs Opus's 38% in internal evaluations).
• Can autonomously write, edit, and execute code when tool use is enabled.
• Strong at translating and modernizing legacy codebases.

4. Vision Strength

• Best vision model in the Claude family, surpassing Opus on vision benchmarks.
• Excellent at interpreting charts, graphs, and imperfect images.
• Reliable text extraction from low-quality visuals for retail, logistics, finance, etc.

5. Agentic Workflows

• Highly capable for multi-step task orchestration.
• Performs well as the engine for agents requiring reasoning, planning, and tool-calling abilities.

6. Content Quality

• Produces natural, relatable writing with improved tone, style, and context awareness.
• Strong at long-form content creation and editing.

7. Safety & Reliability

• Rated ASL-2, meeting Anthropic's safety standards.
• Undergoes extensive red-teaming and external evaluation (UK AISI & US AISI).
• Not trained on user data without explicit permission.