Build AI powered apps for your work

Get started free
LLM ComparisonGPT-5Claude 3 Opus

GPT-5 vs Claude 3 Opus

Compare GPT-5 and Claude 3 Opus. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGPT-5Claude 3 Opus
ProviderOpenAIAnthropic
Model Typetexttext
Context Window400,000 tokens200,000 tokens
Input Cost
$1.25/ 1M tokens
$15.00/ 1M tokens
Output Cost
$10.00/ 1M tokens
$75.00/ 1M tokens

Stop choosing. Use both.

With Appaca you don't have to pick — build apps that are powered by GPT-5, Claude 3 Opus, for your specific use case.

Build your first app free

Strengths & Best Use Cases

GPT-5

OpenAI

1. High reasoning capability

  • Designed for intelligent reasoning across complex domains.
  • Supports reasoning tokens and adjustable reasoning effort.

2. Strong coding and agentic performance

  • Optimized for multi-step coding tasks, tool-use chains, and agent workflows.
  • Handles complex logic, planning, and structured problem solving reliably.

3. Multimodal input

  • Accepts text + image as input.
  • Produces text outputs with strong instruction following.

4. Extensive tool support

  • Works with Web Search, File Search, Image Generation (as a tool), Code Interpreter, MCP, and more.
  • Integrated across Chat Completions, Responses API, Realtime, Assistants, Batch, Embeddings, etc.

Claude 3 Opus

Anthropic

1. Intelligence & Reasoning

  • Highest capability in the Claude 3 family
  • Near-human comprehension and fluency
  • Excels at MMLU, GPQA, GSM8K, advanced reasoning tasks

2. Complex Problem Solving

  • Best for research, strategy, multi-step planning
  • Handles ambiguous, open-ended tasks with ease

3. Vision & Multimodal Capabilities

  • Strong chart/graph understanding
  • Processes documents, technical diagrams, and dense visual data

4. Recall & Long-Context Reasoning

  • Near-perfect recall (>99% on NIAH benchmark)
  • Handles very large documents and multi-file workflows

5. Enterprise-Grade Accuracy

  • Significantly reduced hallucinations
  • High correctness rate for factual queries