LLM ComparisonGPT-5.3 CodexGPT-5.2 Codex

GPT-5.3 Codex vs GPT-5.2 Codex

Compare GPT-5.3 Codex and GPT-5.2 Codex. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGPT-5.3 CodexGPT-5.2 Codex
ProviderOpenAIOpenAI
Model Typetexttext
Context Window400,000 tokens400,000 tokens
Input Cost
$1.75/ 1M tokens
$1.75/ 1M tokens
Output Cost
$14.00/ 1M tokens
$14.00/ 1M tokens

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Strengths & Best Use Cases

GPT-5.3 Codex

OpenAI

1. Strongest Codex Model for Agentic Engineering

  • OpenAI positions GPT-5.3 Codex as its most capable agentic coding model to date.
  • Built for long-horizon software engineering tasks that require planning, iteration, and reliable code transformation across files.

2. Configurable Reasoning + Multimodal Input

  • Supports configurable reasoning effort from low to xhigh so teams can trade off depth against latency.
  • Accepts both text and image inputs while producing text output.

3. Large Context for Real Codebases

  • 400 k token context window helps it work across larger repositories, implementation plans, and supporting documentation.
  • Allows up to 128 k output tokens for longer code generations, patches, and technical write-ups.

4. Current Knowledge for Modern Dev Workflows

  • Knowledge cut-off of Aug 31 2025 keeps it aligned with newer frameworks, libraries, and tooling.
  • Supports streaming, function calling, and structured outputs for agent-style coding workflows.

GPT-5.2 Codex

OpenAI

1. Optimized for Long-Horizon Coding Tasks

  • OpenAI describes GPT-5.2 Codex as a highly intelligent coding model built for long-horizon, agentic coding work.
  • Well suited to planning, refactoring, debugging, and multi-step implementation flows inside real codebases.

2. Adjustable Reasoning for Coding Work

  • Supports configurable reasoning effort from low to xhigh depending on speed and quality needs.
  • Accepts both text and image inputs while producing text output.

3. Large Context + Long Output

  • 400 k token context window supports broad repository understanding and larger working sets.
  • Allows up to 128 k output tokens for longer patches, code generation, and technical explanations.

4. Up-to-Date Model Snapshot

  • Knowledge cut-off of Aug 31 2025 keeps it current with newer tools and frameworks.
  • Supports streaming, function calling, and structured outputs for tool-driven coding workflows.

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