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LLM ComparisonGPT-5.2GPT-4o

GPT-5.2 vs GPT-4o

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

Model Comparison

FeatureGPT-5.2GPT-4o
ProviderOpenAIOpenAI
Model Typetexttext
Context Window400,000 tokens128,000 tokens
Input Cost
$1.75/ 1M tokens
$2.50/ 1M tokens
Output Cost
$14.00/ 1M tokens
$10.00/ 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

OpenAI

1. High-intelligence, general-purpose model

  • Strong reasoning, creativity, summarization, and problem-solving.
  • Great balance of speed, accuracy, and cost.

2. Multimodal input support

  • Accepts text + image inputs for visual reasoning, extraction, or description.
  • Output is text only, making it predictable for production.

3. Excellent for structured and unstructured tasks

  • Performs well on Q&A, writing, analysis, classification, chat, and planning.
  • Supports Structured Outputs, making it suitable for deterministic workflows.

4. Strong tool-use capabilities

  • Supports function calling, API orchestration, and tool-augmented workflows.
  • Integrates well with assistants, batch operations, and automation pipelines.

5. Large context for complex tasks

  • 128K context allows multi-document reasoning, multi-step conversations, and large input payloads.

6. Production-ready reliability

  • Stable outputs, predictable behaviors, and broad modality coverage.
  • Supported across all major API endpoints.

7. Lower latency than o-series reasoning models

  • Faster responses due to no dedicated reasoning step.
  • Ideal for interactive or near-real-time applications.

8. Fine-tuning and distillation supported

  • Enables specialization for domain-specific tasks.
  • Distillation helps create smaller, efficient custom models.

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