GPT Image 1 Mini vs Gemini 2.5 Pro Experimental

Compare GPT Image 1 Mini and Gemini 2.5 Pro Experimental. Find out which one is better for your use case.

Model Comparison

FeatureGPT Image 1 MiniGemini 2.5 Pro Experimental
ProviderOpenAIGoogle
Model Typeimagetext
Context WindowN/A1,048,576 tokens
Input Cost$2.00 / 1M tokens$1.50 / 1M tokens
Output CostN/A$6.00 / 1M tokens

Strengths & Best Use Cases

GPT Image 1 Mini

1. Cost-Efficient Image Generation

  • A budget-friendly version of GPT Image 1 designed for high-volume or cost-sensitive workflows.
  • Offers strong visual generation quality at significantly reduced per-image prices.

2. Natively Multimodal Architecture

  • Accepts both text and image inputs, enabling:
    • Image-to-image transformations
    • Visual editing based on reference photos
    • Enhanced control via mixed inputs
  • Outputs high-quality images aligned with the prompt or reference.

3. Flexible Resolution & Quality Options

  • Supports three quality tiers (Low, Medium, High).
  • Available in multiple resolutions:
    • 1024x1024
    • 1024x1536
    • 1536x1024
  • Allows users to choose between affordability and visual detail.

4. Practical for Real-World Applications Ideal for:

  • Marketing visuals
  • UI/UX mockups
  • Concept art
  • Prototyping & brainstorming
  • Lightweight creative tools within SaaS platforms

5. Broad API Integration Works across all major endpoints:

  • Chat Completions
  • Responses
  • Realtime
  • Assistants
  • Image generation & image edits
  • Batch and embedding pipelines for more complex workflows.

6. Streamlined Feature Set for Simplicity

  • No streaming, function calling, structured output, or fine-tuning.
  • Focused exclusively on reliable, easy-to-use image generation.

7. Snapshot Support for Consistency

  • Supports stable snapshots so developers can lock behavior and ensure reproducible outputs across deployments.

Gemini 2.5 Pro Experimental

1. State-of-the-art reasoning performance

  • #1 on LMArena human preference leaderboard.
  • Excels at advanced reasoning benchmarks like GPQA and AIME 2025.
  • Achieves 18.8% on Humanity's Last Exam (no tools), representing frontier human-level reasoning.

2. New “thinking model” architecture

  • Built with explicit reasoning steps internally before responding.
  • Handles complex, multi-stage logic with higher accuracy and fewer hallucinations.

3. Elite science and mathematics capabilities

  • Leads in math and science tasks across industry benchmarks.
  • High performance without costly inference tricks like majority voting.

4. Exceptional coding abilities

  • Major leap over Gemini 2.0 in coding performance.
  • 63.8% on SWE-Bench Verified with custom agent setup.
  • Strong at code transformation, debugging, and building agentic apps.
  • Capable of generating full applications (e.g., a playable video game) from a single-line prompt.

5. Massive multimodal context

  • Ships with a 1,000,000 token window (2M coming soon).
  • Handles entire documents, datasets, video sequences, audio files, and large codebases.
  • Maintains strong performance even at extreme context lengths.

6. Native multimodality across all inputs

  • Understands and reasons over text, images, audio, video, and code.
  • Designed for real-world, multi-source problem-solving and agent workflows.

7. Consistent high-quality outputs

  • Improved post-training results in more accurate, coherent, and stylistically strong responses.
  • Higher reliability across complex workloads.

8. Early availability for developers

  • Available today in Google AI Studio for experimentation.
  • Coming soon to Vertex AI with higher rate limits and production-ready access.

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