about
Llama 4 Scout 17B 16e Instruct is a capable AI model engineered for diverse applications and task types. It delivers strong performance across coding, analysis, writing, and reasoning tasks while maintaining reliability and safety. Built with modern AI principles for production-grade applications.
Licensellama4
Context window(in thousands)128000
Use cases for Llama-4-Scout-Instruct
- Content Creation: Generate high-quality written content and creative outputs.
- Code Generation: Develop, debug, and optimize software code.
- Data Analysis: Analyze information and extract insights from diverse sources.
Quality
Arena Elo1250
MMLU79.6
MT BenchN/A
Llama 4 Scout 17B 16e Instruct has strong performance on complex reasoning and diverse task benchmarks. Designed for production use with reliable, safe behavior. Suitable for enterprise applications requiring both capability and efficiency.
What's Twitter saying?
- Advanced capability: Llama 4 Scout 17B 16e Instruct delivers strong performance across diverse tasks, enabling reliable AI applications. src: x.com
- Production ready: Organizations trust Llama 4 Scout 17B 16e Instruct for mission-critical applications requiring both capability and safety. src: x.com
- Flexible deployment: Llama 4 Scout 17B 16e Instruct integrates seamlessly with Telnyx Inference for scalable, production-grade AI. src: x.com
Explore Our LLM Library
Discover the power and diversity of large language models available with Telnyx. Explore the options below to find the perfect model for your project.
| Organization | Model Name | Tasks | Languages Supported | Context Length | Parameters | Model Tier | License |
|---|---|---|---|---|---|---|---|
| deepseek-ai | DeepSeek-R1-Distill-Qwen-14B | text generation | English | 43,000 | 14.8B | medium | deepseek |
| fixie-ai | ultravox-v0_4_1-llama-3_1-8b | audio text-to-text | Multilingual | 8,000 | 8.7B | small | mit |
| gemma-2b-it | text generation | English | 8,192 | 2.5B | small | gemma | |
| gemma-7b-it | text generation | English | 8,192 | 8.5B | small | gemma | |
| meta-llama | Llama-3.3-70B-Instruct | text generation | Multilingual | 99,000 | 70.6B | large | llama3.3 |
| meta-llama | Llama-Guard-3-1B | safety classification | Multilingual | 128,000 | 1.5B | small | llama3.3 |
| meta-llama | Meta-Llama-3.1-70B-Instruct | text generation | Multilingual | 99,000 | 70.6B | large | llama3.1 |
| meta-llama | Meta-Llama-3.1-8B-Instruct | text generation | Multilingual | 131,072 | 8.0B | small | llama3.1 |
| minimaxai | MiniMax-M2.5 | text generation | English | 2,000,000 | 0 | large | minimaxai |
| mistralai | Mistral-7B-Instruct-v0.1 | text generation | English | 8,192 | 7.2B | small | apache-2.0 |
| mistralai | Mistral-7B-Instruct-v0.2 | text generation | English | 32,768 | 7.2B | small | apache-2.0 |
| mistralai | Mixtral-8x7B-Instruct-v0.1 | text generation | Multilingual | 32,768 | 46.7B | medium | apache-2.0 |
| moonshotai | Kimi-K2.5 | text generation | English | 256,000 | 1.0T | large | modified-mit |
| Qwen | Qwen3-235B-A22B | text generation | English | 32,768 | 235.1B | large | apache-2.0 |
| zai-org | GLM-5 | text generation | English | 202,752 | 753.9B | large | mit |
| anthropic | claude-3-7-sonnet-latest | text generation | Multilingual | 200,000 | 0 | large | anthropic |
| anthropic | claude-haiku-4-5 | text generation | Multilingual | 200,000 | 0 | large | anthropic |
| anthropic | claude-opus-4-6 | text generation | Multilingual | 200,000 | 0 | large | anthropic |
| anthropic | claude-sonnet-4-20250514 | text generation | Multilingual | 200,000 | 0 | large | anthropic |
| gemini-2.0-flash | text generation | Multilingual | 1,048,576 | 0 | large | ||
| gemini-2.5-flash | text generation | Multilingual | 1,048,576 | 0 | large | ||
| gemini-2.5-flash-lite | text generation | Multilingual | 1,048,576 | 0 | large | ||
| groq | gpt-oss-120b | text generation | English | 131,072 | 117.0B | large | groq |
| groq | kimi-k2-instruct | text generation | English | 131,072 | 1.0T | large | groq |
| groq | llama-3.3-70b-versatile | text generation | Multilingual | 131,072 | 70.6B | large | llama3.3 |
| groq | llama-4-maverick-17b-128e-instruct | text generation | Multilingual | 1,000,000 | 400.0B | large | llama4 |
| groq | llama-4-scout-17b-16e-instruct | text generation | Multilingual | 128,000 | 109.0B | large | llama4 |
| openai | gpt-3.5-turbo | text generation | Multilingual | 4,096 | 0 | large | openai |
| openai | gpt-4 | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-4-0125-preview | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-4-0314 | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-4-0613 | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-4-1106-preview | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-4-32k-0314 | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-4-turbo-preview | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-4.1 | text generation | Multilingual | 1,047,576 | 0 | large | openai |
| openai | gpt-4.1-mini | text generation | Multilingual | 1,047,576 | 0 | large | openai |
| openai | gpt-4o | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-4o-mini | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-5 | text generation | Multilingual | 400,000 | 0 | large | openai |
| openai | gpt-5-mini | text generation | Multilingual | 400,000 | 0 | large | openai |
| openai | gpt-5.1 | text generation | Multilingual | 400,000 | 0 | large | openai |
| openai | gpt-5.2 | text generation | Multilingual | 400,000 | 0 | large | openai |
| openai | o1-mini | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | o1-preview | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | o3-mini | text generation | Multilingual | 200,000 | 0 | large | openai |
| xai-org | grok-2 | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-2-latest | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-3 | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-3-beta | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-3-fast | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-3-fast-beta | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-3-fast-latest | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-3-latest | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-3-mini | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-3-mini-fast | text generation | Multilingual | 131,072 | 0 | large | xai |
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faqs
What is Llama 4 Scout 17B 16e Instruct?
Llama 4 Scout 17B 16e Instruct is a powerful AI model engineered for customer support and diverse applications. It delivers strong performance on complex tasks while maintaining safety and reliability.
What are the key features of Llama 4 Scout 17B 16e Instruct?
Llama 4 Scout 17B 16e Instruct offers advanced reasoning performance, safety-by-design, and production-ready reliability. It excels at complex reasoning, analysis, and diverse task execution.
Can Llama 4 Scout 17B 16e Instruct be used for enterprise applications?
Yes, Llama 4 Scout 17B 16e Instruct is designed for enterprise scale with strong reasoning depth and safety guarantees. It's trusted by organizations for mission-critical applications.
How does Llama 4 Scout 17B 16e Instruct compare to other models?
Llama 4 Scout 17B 16e Instruct offers superior performance on complex reasoning and diverse tasks. It balances capability with practical efficiency, making it ideal for most production use cases.
Where can I deploy Llama 4 Scout 17B 16e Instruct?
Deploy Llama 4 Scout 17B 16e Instruct on Telnyx Inference for production use cases. Visit the Telnyx Developer Center for integration guides.
What are best practices for using Llama 4 Scout 17B 16e Instruct?
Provide detailed context and clear problem specifications for best results. Use system prompts to guide behavior on specialized tasks. For research applications, include relevant materials.
Is Llama 4 Scout 17B 16e Instruct suitable for my use case?
Llama 4 Scout 17B 16e Instruct is versatile and suitable for coding, analysis, writing, research, and strategic problem-solving. Evaluate its performance on your specific tasks to determine fit.