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LLM ComparisonGPT-5.5GPT-5.2 Codex

GPT-5.5 vs GPT-5.2 Codex

Compare GPT-5.5 and GPT-5.2 Codex. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGPT-5.5GPT-5.2 Codex
ProviderOpenAIOpenAI
Model Typetexttext
Context Window1,000,000 tokens400,000 tokens
Input Cost
$5.00/ 1M tokens
$1.75/ 1M tokens
Output Cost
$30.00/ 1M tokens
$14.00/ 1M tokens

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Strengths & Best Use Cases

GPT-5.5

OpenAI

1. Strongest Agentic Coding Model

  • State-of-the-art on Terminal-Bench 2.0 (82.7%), Expert-SWE (73.1%), and SWE-Bench Pro (58.6%), outperforming GPT-5.4 on complex coding tasks.
  • Holds context across large systems, reasons through ambiguous failures, and carries changes through surrounding codebases with fewer tokens.

2. Higher Intelligence at GPT-5.4 Latency

  • Co-designed, trained, and served on NVIDIA GB200/GB300 NVL72 systems to match GPT-5.4 per-token latency while performing at a significantly higher level.
  • Uses fewer tokens to complete the same tasks, making it more efficient as well as more capable.

3. Powerful for Knowledge Work & Computer Use

  • Scores 84.9% on GDPval (44 occupations) and 78.7% on OSWorld-Verified for autonomous computer operation.
  • Excels at generating documents, spreadsheets, and reports; naturally moves across finding information, using tools, and checking output.

4. Scientific Research Co-Scientist

  • Leading performance on GeneBench, BixBench, and FrontierMath; helped discover a new proof about Ramsey numbers verified in Lean.
  • Strong enough to meaningfully accelerate progress at the frontiers of biomedical and mathematical research.

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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