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LLM ComparisonGPT-4oClaude 4 Opus

GPT-4o vs Claude 4 Opus

Compare GPT-4o and Claude 4 Opus. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGPT-4oClaude 4 Opus
ProviderOpenAIAnthropic
Model Typetexttext
Context Window128,000 tokens200,000 tokens
Input Cost
$2.50/ 1M tokens
$15.00/ 1M tokens
Output Cost
$10.00/ 1M tokens
$75.00/ 1M tokens

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Strengths & Best Use Cases

GPT-4o

OpenAI

1. High-intelligence, general-purpose model

  • Strong reasoning, creativity, summarization, and problem-solving.
  • Great balance of speed, accuracy, and cost.

2. Multimodal input support

  • Accepts text + image inputs for visual reasoning, extraction, or description.
  • Output is text only, making it predictable for production.

3. Excellent for structured and unstructured tasks

  • Performs well on Q&A, writing, analysis, classification, chat, and planning.
  • Supports Structured Outputs, making it suitable for deterministic workflows.

4. Strong tool-use capabilities

  • Supports function calling, API orchestration, and tool-augmented workflows.
  • Integrates well with assistants, batch operations, and automation pipelines.

5. Large context for complex tasks

  • 128K context allows multi-document reasoning, multi-step conversations, and large input payloads.

6. Production-ready reliability

  • Stable outputs, predictable behaviors, and broad modality coverage.
  • Supported across all major API endpoints.

7. Lower latency than o-series reasoning models

  • Faster responses due to no dedicated reasoning step.
  • Ideal for interactive or near-real-time applications.

8. Fine-tuning and distillation supported

  • Enables specialization for domain-specific tasks.
  • Distillation helps create smaller, efficient custom models.

Claude 4 Opus

Anthropic
  • Highest capability in the family: described as “our most powerful model yet” by Anthropic.
  • Exceptional at long-running tasks requiring thousands of steps and sustained focus (e.g., continuous codebase work for hours).
  • Excellent performance on benchmarks: e.g., SWE-bench 72.5 % and Terminal-bench 43.2 %.
  • Designed for complex agentic workflows, deep reasoning, tool use, and large context windows.
  • Placed under a higher safety classification (ASL-3) due to its frontier capability and risk profile.