GPT-4.1 Nano vs Claude 4.7 Opus

1. Ultra-Fast, Low-Latency Performance

• The fastest model in the GPT-4.1 family, ideal for real-time interactions and high-throughput applications.
• Designed for scenarios where speed matters more than complex reasoning.

2. Most Cost-Efficient GPT-4.1 Variant

• Lowest price point among GPT-4.1 models.
• Enables large-scale deployments such as support bots, routing systems, and lightweight assistants without high compute costs.

3. Solid Instruction Following

• Consistent and reliable at following clear instructions.
• Well-suited for:
  • Classification
  • Simple reasoning
  • Data extraction
  • Content rewriting
  • Chat-style responses

4. Strong Tool Calling Capabilities

• Built with robust support for:
  • Function calling
  • Structured outputs (e.g., JSON)
  • Lightweight automation tasks
• Works well within multi-step agent workflows that rely on simple tools.

5. Basic Multimodal Input

• Supports text and image input.
• Useful for:
  • Simple visual recognition
  • Alt-text generation
  • Reading graphics or screenshots

6. Text-Only Output

• Produces text only, ensuring:
  • Clean structured outputs
  • High reliability for downstream processing
  • Ease of integration into backend systems

7. 1M-Token Context Window

• Supports up to 1,047,576 tokens, allowing:
  • Long documents
  • Multiple files
  • Large prompt memory
• Reduces or eliminates the need for chunking and retrieval in many simple workflows.

8. Ideal Use Cases

• Customer support bots
• Routing and intent detection
• Simple agents and workflow automation
• Content cleanup and rewriting
• Basic Q&A, summaries, and extraction

9. Broad API Integration

• Available across major API endpoints:
  • Chat Completions
  • Responses
  • Realtime
  • Assistants
  • Fine-tuning
• Supports predicted outputs for reliability and determinism.

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.