LLM ComparisonGPT-5.2Claude 4.5 Opus

GPT-5.2 vs Claude 4.5 Opus

Compare GPT-5.2 and Claude 4.5 Opus. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGPT-5.2Claude 4.5 Opus
ProviderOpenAIAnthropic
Model Typetexttext
Context Window400,000 tokens200,000 tokens
Input Cost
$1.75/ 1M tokens
$5.00/ 1M tokens
Output Cost
$14.00/ 1M tokens
$25.00/ 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.

Claude 4.5 Opus

Anthropic

1. Maximum capability with more practical pricing

  • Anthropic introduced Opus 4.5 as its most intelligent model, combining maximum capability with practical performance.
  • It was positioned as the best model in the world for coding, agents, and computer use at launch, with pricing reduced to $5/M input and $25/M output.

2. Step-change gains for coding and advanced agent work

  • Anthropic describes Opus 4.5 as state-of-the-art on real-world software engineering tests.
  • It also improved everyday knowledge-work tasks like deep research, slides, and spreadsheets while staying strong on long-horizon agent workflows.

3. Better control over reasoning depth

  • Opus 4.5 introduced the effort parameter, letting developers trade off response thoroughness against token efficiency.
  • This made it easier to use one flagship model across both high-depth analysis and more cost-sensitive production workloads.

4. Stronger computer use and continuity

  • Added enhanced computer use with a zoom action for inspecting detailed screen regions.
  • Preserves prior thinking blocks across turns, helping the model maintain reasoning continuity in extended multi-step tasks.

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