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LLM ComparisonGPT-5.2GPT-4o mini

GPT-5.2 vs GPT-4o mini

Compare GPT-5.2 and GPT-4o mini. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGPT-5.2GPT-4o mini
ProviderOpenAIOpenAI
Model Typetexttext
Context Window400,000 tokens128,000 tokens
Input Cost
$1.75/ 1M tokens
$0.15/ 1M tokens
Output Cost
$14.00/ 1M tokens
$0.60/ 1M tokens

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Strengths & Best Use Cases

GPT-5.2

OpenAI

1. Advanced Reasoning for Diverse Domains

  • Built to tackle coding and agentic workflows across multiple industries, with configurable reasoning support.

2. Multi-Modal & Long-Form Capabilities

  • Handles both text and image inputs, producing text output.
  • Allows up to 128 k output tokens for lengthy responses.

3. Large Context & Updated Knowledge

  • 400 k token context window accommodates extensive codebases or documents.
  • Knowledge cut-off of Aug 31 2025 keeps it current with recent developments.

GPT-4o mini

OpenAI

1. Fast, cost-efficient performance

  • Designed for low-latency, high-throughput workloads.
  • Ideal for production systems where speed and budget matter more than deep reasoning power.

2. Great for focused NLP tasks

  • Excels at classification, tagging, entity extraction, rewriting, paraphrasing, and SEO tasks.
  • Strong at translation and keyword generation due to efficient language understanding.

3. Multimodal input capable (text + image)

  • Accepts images for lightweight visual analysis, categorization, or extraction.
  • Outputs text only, ensuring deterministic and easily integrated responses.

4. Supports advanced developer features

  • Structured Outputs for predictable schemas.
  • Function calling for building tool-augmented agents.
  • Fully compatible with Batch API for large-scale processing.

5. Easy to fine-tune

  • One of the best OpenAI models for domain-specific fine-tuning.
  • Allows organizations to compress larger models' behavior (like GPT-4o) into a smaller footprint.

6. Suitable for distillation workflows

  • Can approximate GPT-4o or GPT-5 outputs using distillation, dramatically reducing cost.
  • Enables scalable deployment for high-volume applications.

7. Large context window for its size

  • 128K context supports multi-step tasks, multi-document inputs, and long-running conversations.
  • Useful for agents that need memory across extended sessions.

8. Reliable for commercial production

  • Stable, predictable, and low-variance outputs make it ideal for automation and enterprise stacks.
  • Works well in synchronous or asynchronous pipelines.

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