GPT Image 1.5 vs Claude 4.1 Opus

1. State-of-the-Art Image Generation

• Produces high-quality, detailed images optimized for realism, style control and prompt fidelity.
• Designed to handle complex visual scenes, compositions and lighting conditions.

2. Natively Multimodal Architecture

• Understands and reasons over both text and images as inputs.
• Ideal for workflows like editing based on reference images, expanding sketches or mockups and visual concept development.

3. Flexible Output Resolutions & Quality Levels

• Supports multiple resolutions including 1024x1024, 1024x1536 and 1536x1024.
• Offers three quality tiers (Low, Medium, High) to balance cost, speed and maximum detail.

4. Multiple Pricing Models

• Pay-per-token for multimodal input: text tokens and image tokens.
• Pay-per-image generation for final output: low, medium and high quality tiers.
• Enables businesses to balance cost and output needs.

5. Broad Use Cases

• Product photography and marketing assets.
• Illustration, concept art and creative ideation.
• UX/UI mockups.
• Style-guided image creation.
• Generating reference images for design or storytelling.

6. Supported Across Major API Endpoints

• Available via Chat Completions, Responses, Realtime, Assistants and Images (generations/edits) endpoints.
• Allows tight integration into automated creative pipelines or user-facing apps.

7. Simplified Model Behavior for Stability

• No streaming, function calling, structured outputs or fine-tuning; focused solely on high-quality image generation.

8. Consistent Results via Snapshots

• Supports snapshots for version locking to ensure long-term reproducibility.

9. Ideal For

• Designers, marketers and creatives.
• Product teams needing image assets.
• App builders integrating image generation workflows.
• Agencies producing visual content at scale.

1. Advanced Coding Performance

• Achieves 74.5% on SWE-bench Verified, improving the Claude family's state-of-the-art coding abilities.

• Stronger at:

  • Multi-file code refactoring
  • Large codebase debugging
  • Pinpointing exact corrections without unnecessary edits

• Outperforms Opus 4 and shows gains comparable to jumps seen in past major releases.

2. Improved Agentic & Research Capabilities

• Better at maintaining detail accuracy in long research tasks.
• Enhanced agentic search and step-by-step problem solving.
• Performs reliably across complex multi-turn reasoning tasks.

3. Validated by Real-World Users

• GitHub: Better multi-file refactoring and code adjustments.
• Rakuten Group: High precision debugging with minimal collateral changes.
• Windsurf: One standard deviation improvement on their junior dev benchmark - similar magnitude to Sonnet 3.7 → Sonnet 4.

4. Hybrid-Reasoning Benchmark Improvements

• Improvements across TAU-bench, GPQA Diamond, MMMLU, MMMU, AIME (with extended thinking).
• Stronger robustness in long-context reasoning tasks.