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LLM ComparisonGPT-5.2 CodexGPT-4.1

GPT-5.2 Codex vs GPT-4.1

Compare GPT-5.2 Codex and GPT-4.1. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGPT-5.2 CodexGPT-4.1
ProviderOpenAIOpenAI
Model Typetexttext
Context Window400,000 tokens1,047,576 tokens
Input Cost
$1.75/ 1M tokens
$2.00/ 1M tokens
Output Cost
$14.00/ 1M tokens
$8.00/ 1M tokens

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Strengths & Best Use Cases

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.

GPT-4.1

OpenAI

1. Smartest non-reasoning model

  • Highest intelligence among models without a reasoning step.
  • Great for tasks where speed + accuracy matter without deep chain-of-thought.

2. Excellent instruction following

  • Very strong at structured tasks, formatting, and precise execution.
  • Ideal for productized workflows and deterministic outputs.

3. Reliable tool calling

  • Works smoothly with Web Search, File Search, Image Generation, and Code Interpreter.
  • Supports MCP and advanced tool-enabled API flows.

4. Large 1M-token context window

  • Allows extremely long conversations, large documents, and multi-file use cases.
  • Handles context-heavy tasks without requiring chunking.

5. Low latency (no reasoning step)

  • Faster responses than GPT-5 family when reasoning mode isn't required.
  • More predictable timing for production use.

6. Multimodal input

  • Accepts text + image.
  • Output is text only.

7. Supports fine-tuning

  • Can be fine-tuned for specialized tasks.
  • Also supports distillation for smaller custom models.

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