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Language models: Claude 6 vs. Llama 4

Quick Verdict

Both models offer strong capabilities, with Llama 4 excelling in context window size and hallucination rate, while Claude 6 provides more transparent pricing and strong multilingual support.

Comparison of Language modelsClaude 6 vs. Llama 4

Key features – Side-by-Side

AttributeClaude 6Llama 4
Context Window Size200K tokens (500 pages with paid plan), Enterprise plans may offer 500K with Claude Sonnet 4. Claude Sonnet 4 supports 1 million tokens.Llama 4 Scout: 10 million tokens, Llama 4 Maverick: 1 million tokens
Maximum Token OutputClaude 3.7 Sonnet can generate 128K output tokens.Not available
Factual AccuracyClaude models still make mistakes, get facts wrong, and hallucinate details. OpenAI announced a 45% reduction in errors compared to GPT-4o with web search and an 80% reduction in errors compared to o3 in thinking mode.Trained on over 30 trillion tokens, doubling Llama 3's training data. Improved accuracy and performance through advanced training techniques.
Coding ProficiencyClaude Code, powered by Claude 4 Sonnet, supports 50+ programming languages and helps with code generation, debugging, and refactoring. Supports Python, JavaScript, Java, C++, C#, Ruby, PHP, Go, Swift, Kotlin, and SQL.Understands and generates application code. Llama 4 Maverick is competitive with DeepSeek v3.1 on coding.
Hallucination RateOpenAI GPT-4.5 has the lowest hallucination rate of 15%.Llama 4 Maverick: 4.6%, Llama 4 Scout: 4.7%
API Availability & CostAvailable through Anthropic API, Amazon Bedrock, and Google's Vertex AI. Offers free, Pro, and Max plans. Claude 3 Sonnet is $0.003 per 1K input tokens and $0.015 per 1K output tokens on AWS Bedrock.Generally available, billed per million tokens. As of May 1, 2025, costs range from $0.10 to $0.90 per million tokens. Llama 4 Maverick: $0.27 per 1 million input tokens and $0.85 per 1 million output tokens. Llama 4 Scout: $0.18 per 1 million input tokens and $0.59 per 1 million output tokens.
Multilingual SupportRobust multilingual capabilities with consistent relative performance across languages. Can process input and generate output in most world languages using standard Unicode characters. High accuracy in translating between non-English languages.Supports multiple languages for text, including Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese. Pre-trained on 200 languages. Image understanding is primarily supported in English.

Overall Comparison

Context Window: Llama 4 wins; Hallucination: Llama 4 wins; Pricing: Claude 6 wins; Multilingual: Tie

Pros and Cons

Claude 6

Pros:
  • Excels in extended reasoning
  • Supports 50+ programming languages
  • Robust multilingual capabilities
  • Snappy, high-quality code completions
  • Offers fine-tuning options
  • Includes safety measures and bias mitigation
  • Provides rich documentation and tooling
Cons:
  • Models still make mistakes and hallucinate details

Llama 4

Pros:
  • Improved accuracy and performance compared to previous iterations.
  • Strong coding proficiency (Llama 4 Maverick).
  • Multilingual support with pre-training on 200 languages.
  • Customizable through fine-tuning.
  • Fast inference speeds.
  • Integrated safety measures and bias mitigation.
Cons:
  • Maximum token output information not available.
  • Hallucination rate of 4.6% (Maverick) and 4.7% (Scout).
  • Image understanding primarily supported in English.
  • Information on how the model handles ambiguous or contradictory information in the input context was not found in the provided search results.

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