Llama 4
外部Llama 4 is Meta's cutting-edge family of natively multimodal AI models, powered by mixture-of-experts architecture for seamless text-vision integration and industry-leading 10M token context windows. Models like Scout and Maverick deliver efficient, single-H100 performance, excelling in image reasoning, OCR, grounding, RAG, and summarization. Ideal for developers and enterprises building cost-effective multimodal applications, it offers strong benchmarks but mixed real-world results in coding and creative writing.
説明
Llama 4 is Meta's cutting-edge family of natively multimodal AI models, powered by mixture-of-experts architecture for seamless text-vision integration and industry-leading 10M token context windows. Models like Scout and Maverick deliver efficient, single-H100 performance, excelling in image reasoning, OCR, grounding, RAG, and summarization. Ideal for developers and enterprises building cost-effective multimodal applications, it offers strong benchmarks but mixed real-world results in coding and creative writing.
主な機能
- Natively multimodal via early fusion
- Mixture-of-experts architecture
- Up to 10M token context window
- Expert image grounding
- Advanced reasoning and long-context handling
主な用途
- 1.Vision and OCR tasks
- 2.Image grounding and multimodal reasoning
- 3.Long-context retrieval and RAG
- 4.Document analysis
- 5.Summarization
- 6.Function calling
Llama 4 はあなたに合っていますか？
おすすめの用途
- Developers building RAG or long-context apps
- Enterprises for multimodal tasks like document analysis
向いていない用途
- Users needing strong creative writing or advanced coding
- Europeans or large companies (>700M users) due to licensing restrictions
- Those relying solely on benchmarks for real-world expectations
際立った特徴
- Runs efficiently on single H100 GPU
- Cost-effective inference (~$0.19–$0.49 per 1M tokens)
- 17B active parameters with 128 experts (Maverick)
- Strong benchmarks in image reasoning, coding, multilingual, and long-context tasks
- Downloadable models or Llama API access
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最も高く評価された点
- Excels in vision/OCR, image grounding, long-context retrieval
- Strong multimodal applications, summarization, function calling
- Cost-efficient and hardware-friendly for RAG and coding flows
よくある不満
- Poor real-world coding and creative writing despite benchmarks
- Benchmark controversies (tuned versions used)
- Context performance degrades at longer lengths like 120k tokens
- Verbose, yappy responses disrupting flow
- Rushed release with rough edges and inconsistencies
- Benchmark-reality gap; underperforms peers in practical tests