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LLM ComparisonGPT-5.2Claude 4.7 Opus

GPT-5.2 vs Claude 4.7 Opus

Compare GPT-5.2 and Claude 4.7 Opus. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGPT-5.2Claude 4.7 Opus
ProviderOpenAIAnthropic
Model Typetexttext
Context Window400,000 tokens1,000,000 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 4.7 Opus

Anthropic

1. State-of-the-art software engineering

  • A notable upgrade over Opus 4.6 on the hardest coding tasks, with users reporting they can hand off work that previously required close supervision.
  • Early partners reported double-digit gains on real-world benchmarks - e.g., Cursor saw CursorBench jump from 58% to 70%, and Rakuten-SWE-Bench resolution tripled versus Opus 4.6.
  • Handles complex, long-running tasks with rigor: plans carefully, catches its own logical faults, and verifies its outputs before reporting back.

2. Long-horizon agent reliability

  • Full 1M token context window at standard pricing, with state-of-the-art long-context consistency.
  • Far fewer tool errors, stronger recovery from tool failures, and better follow-through on multi-step workflows - designed for async work like CI/CD, automations, and managing multiple agents in parallel.
  • Stronger file-system-based memory, retaining useful notes across long, multi-session runs.

3. Sharper instruction following and honesty

  • Takes instructions literally and precisely - existing prompts may need re-tuning since earlier models were more lenient.
  • More honest about its own limits: reports missing data instead of fabricating plausible-but-wrong answers, and resists dissonant-data traps that tripped up Opus 4.6.

4. Substantially improved vision and multimodal reasoning

  • Accepts images up to 2,576 px on the long edge (~3.75 MP) - over 3x more than prior Claude models.
  • Unlocks dense-screenshot computer use, complex diagram extraction, and pixel-perfect reference tasks.
  • Stronger document reasoning for enterprise analysis (e.g., 21% fewer errors than Opus 4.6 on Databricks' OfficeQA Pro).

5. Top-tier professional knowledge work

  • State-of-the-art on the Finance Agent evaluation and GDPval-AA, with tighter, more professional finance analyses, models, and presentations.
  • Strong on legal work - e.g., 90.9% on BigLaw Bench at high effort, with better-calibrated reasoning on review tables and ambiguous edits.
  • Noted by design-focused partners as the best model for building dashboards and data-rich interfaces.

6. Modern effort and budget controls

  • Introduces a new xhigh effort level between high and max for finer control over reasoning vs. latency.
  • Task budgets (public beta) let developers guide token spend across long runs.
  • Recommended to start with high or xhigh effort for coding and agentic use cases.