GPT Image 1.5 vs Claude 5 Sonnet

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. Anthropic's most agentic Sonnet yet

• A substantial upgrade over Sonnet 4.6 on reasoning, tool use, coding, and knowledge work - narrows the gap to Opus 4.8 while staying at Sonnet pricing.
• Early partners describe it finishing multi-step jobs end to end where prior Sonnet models used to stall halfway.

2. Clear benchmark gains over Sonnet 4.6

• 63.2% on SWE-bench Pro (vs 58.1%), 80.4% on Terminal-Bench 2.1 (vs 67.0%), 81.2% on OSWorld-Verified (vs 78.5%), and 57.4% on Humanity's Last Exam with tools (vs 46.8%).
• On the GDPval-AA v2 knowledge-work benchmark it edges past Opus 4.8 (1,618 vs 1,615).

3. Near-Opus performance at a fraction of the price

• Priced at $3/M input and $15/M output - 40% cheaper than Opus 4.8's $5/$25 - with introductory pricing of $2/$10 through Aug 31, 2026 during the rollout.
• Supports low, medium, high, and xhigh effort levels, covering a wider cost-performance range than Sonnet 4.6 and matching Opus 4.8 on some tasks at higher effort.

4. Long-context, long-horizon reliability

• Full 1M token context window with up to 128K output tokens.
• Adaptive thinking automatically decides how long to reason; better self-correction, fewer tool errors, and steadier follow-through across long agent sessions in Claude Code and Cowork.

5. Safer and more honest than its predecessor

• Lower rates of hallucination, sycophancy, and misaligned behavior than Sonnet 4.6 on Anthropic's automated safety audits.
• Deliberately limited cyber capability compared with Opus-class models, shipped with cyber safeguards enabled by default.

6. Updated tokenizer

• Uses a new tokenizer (shared with the Opus 4.7/4.8 line) that can map the same text to up to 1.35x more tokens than Sonnet 4.6 - introductory pricing is set so the switch is roughly cost-neutral.