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LLM ComparisonGPT-5.1Claude 4.6 Sonnet

GPT-5.1 vs Claude 4.6 Sonnet

Compare GPT-5.1 and Claude 4.6 Sonnet. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGPT-5.1Claude 4.6 Sonnet
ProviderOpenAIAnthropic
Model Typetexttext
Context Window400,000 tokens1,000,000 tokens
Input Cost
$1.25/ 1M tokens
$3.00/ 1M tokens
Output Cost
$10.00/ 1M tokens
$15.00/ 1M tokens

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Strengths & Best Use Cases

GPT-5.1

OpenAI

1. Configurable Reasoning for Agentic Tasks

  • Built to excel in autonomous or semi-autonomous coding workflows, with adjustable reasoning effort for planning, refactoring and debugging.

2. Fast Multi-Modal Input with Large Output

  • Accepts both text and image inputs while producing text outputs.
  • Offers up to 128 k output tokens, allowing long responses and code generation across multiple files.

3. Large Context & Knowledge Cut-Off

  • 400 k token context window supports processing large codebases or documents.
  • Knowledge cut-off of Sep 30 2024 ensures familiarity with recent tools and frameworks.

4. Reasoning Token Support

  • Provides explicit support for reasoning tokens, enabling developers to fine-tune the balance between reasoning depth and speed.

Claude 4.6 Sonnet

Anthropic

1. Most capable Sonnet model yet

  • Anthropic describes Sonnet 4.6 as its most capable Sonnet model.
  • It is a full upgrade across coding, computer use, long-context reasoning, agent planning, knowledge work, and design.

2. Stronger coding and professional task performance at Sonnet pricing

  • Pricing remains at $3/M input and $15/M output, matching Sonnet 4.5.
  • Anthropic says early-access developers strongly preferred it to Sonnet 4.5, and often even to Opus 4.5 for practical work.

3. Long-context, agent-friendly reasoning

  • Supports up to a 1M token context window in beta.
  • Anthropic reports better consistency, fewer false claims of success, fewer hallucinations, and more reliable follow-through on multi-step tasks.

4. Modern API controls for adaptive work

  • Supports adaptive thinking and the effort parameter for balancing speed, cost, and depth.
  • Gains dynamic filtering for web search and web fetch, helping agent workflows keep only relevant information in context.