GPT-4.1 vs Claude 4.7 Opus

1. Smartest non-reasoning model

• Highest intelligence among models without a reasoning step.
• Great for tasks where speed + accuracy matter without deep chain-of-thought.

2. Excellent instruction following

• Very strong at structured tasks, formatting, and precise execution.
• Ideal for productized workflows and deterministic outputs.

3. Reliable tool calling

• Works smoothly with Web Search, File Search, Image Generation, and Code Interpreter.
• Supports MCP and advanced tool-enabled API flows.

4. Large 1M-token context window

• Allows extremely long conversations, large documents, and multi-file use cases.
• Handles context-heavy tasks without requiring chunking.

5. Low latency (no reasoning step)

• Faster responses than GPT-5 family when reasoning mode isn't required.
• More predictable timing for production use.

6. Multimodal input

• Accepts text + image.
• Output is text only.

7. Supports fine-tuning

• Can be fine-tuned for specialized tasks.
• Also supports distillation for smaller custom models.

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.