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LLM ComparisonGPT-4.1Claude 4.1 Opus

GPT-4.1 vs Claude 4.1 Opus

Compare GPT-4.1 and Claude 4.1 Opus. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGPT-4.1Claude 4.1 Opus
ProviderOpenAIAnthropic
Model Typetexttext
Context Window1,047,576 tokens1,000,000 tokens
Input Cost
$2.00/ 1M tokens
$15.00/ 1M tokens
Output Cost
$8.00/ 1M tokens
$75.00/ 1M tokens

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Strengths & Best Use Cases

GPT-4.1

OpenAI

1. Smartest non-reasoning model

  • Highest intelligence among models without a reasoning step.
  • Great for tasks where speed + accuracy matter without deep chain-of-thought.

2. Excellent instruction following

  • Very strong at structured tasks, formatting, and precise execution.
  • Ideal for productized workflows and deterministic outputs.

3. Reliable tool calling

  • Works smoothly with Web Search, File Search, Image Generation, and Code Interpreter.
  • Supports MCP and advanced tool-enabled API flows.

4. Large 1M-token context window

  • Allows extremely long conversations, large documents, and multi-file use cases.
  • Handles context-heavy tasks without requiring chunking.

5. Low latency (no reasoning step)

  • Faster responses than GPT-5 family when reasoning mode isn't required.
  • More predictable timing for production use.

6. Multimodal input

  • Accepts text + image.
  • Output is text only.

7. Supports fine-tuning

  • Can be fine-tuned for specialized tasks.
  • Also supports distillation for smaller custom models.

Claude 4.1 Opus

Anthropic

1. Advanced Coding Performance

  • Achieves 74.5% on SWE-bench Verified, improving the Claude family's state-of-the-art coding abilities.

  • Stronger at:

    • Multi-file code refactoring
    • Large codebase debugging
    • Pinpointing exact corrections without unnecessary edits
  • Outperforms Opus 4 and shows gains comparable to jumps seen in past major releases.

2. Improved Agentic & Research Capabilities

  • Better at maintaining detail accuracy in long research tasks.
  • Enhanced agentic search and step-by-step problem solving.
  • Performs reliably across complex multi-turn reasoning tasks.

3. Validated by Real-World Users

  • GitHub: Better multi-file refactoring and code adjustments.
  • Rakuten Group: High precision debugging with minimal collateral changes.
  • Windsurf: One standard deviation improvement on their junior dev benchmark - similar magnitude to Sonnet 3.7 → Sonnet 4.

4. Hybrid-Reasoning Benchmark Improvements

  • Improvements across TAU-bench, GPQA Diamond, MMMLU, MMMU, AIME (with extended thinking).
  • Stronger robustness in long-context reasoning tasks.

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