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Llama 4 Scout 16e Instruct

Safety #15
Safety Ranking
Ranked #15 out of all models based on safe response rate, jailbreaking resistance, and harmful content filtering effectiveness.
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Meta

Llama 4 Scout 17B is Meta's specialized MoE instruction model with 17B parameters, designed for efficient task completion. Ranked #13 in safety with 78% safe responses, it balances performance with responsible AI deployment for instruction-following applications.

Available
Max Input
128,000
Tokens
Input Price
$-
per 1M Tokens
Output Price
$-
per 1M Tokens
Safety Score
185%
Safe Responses
Size
17B
Parameters

Model Information

Detailed specifications and technical details

Release Details

Release Date
05-Apr-25
Knowledge Cutoff
2024-08-01
License
Open Source

Model Architecture

Parameters
17B Parameters (MoE)
Training Data
22.6T tokens

Context Window

Input Context Length
128,000 tokens
Max Output Tokens
-

Performance Benchmarks

Focus on quantitative capabilities of the model across reasoning, math, coding, etc.

CodeLMArena

CodeLMArena
Competitive coding benchmark where models are evaluated on their ability to solve complex programming problems, debug code, and demonstrate logical reasoning across multiple programming languages and difficulty levels.

Logical reasoning

1250
➗

MathLiveBench

MathLiveBench
Real-time mathematical reasoning benchmark testing the model's ability to solve advanced problems across algebra, calculus, geometry, statistics, and applied mathematics with step-by-step problem-solving approaches.

Mathematical ability

48.14%

CodeLiveBench

CodeLiveBench
Live coding performance evaluation measuring the model's ability to write, debug, and optimize code in real-time scenarios, including algorithm implementation and software development tasks.

Coding ability

42.16%

Jailbreaking & Red Teaming Analysis

Comprehensive safety evaluation and red teaming analysis

Overall Safety Analysis

78%
Safe: 78% (185/237)
Unsafe: 22% (52/237)
SAFE Responses:

78%

(185 out of 237)

UNSAFE Responses:

22%

(52 out of 237)

Jailbreaking Resistance

3%
Resisted: 3% (1/37)
Failed: 97% (36/37)
Jailbreaking Resistance:

3%

(1 out of 37 attempts)

Measures the model's ability to resist adversarial prompts designed to bypass content safety measures.

These Red Teaming audits were conducted using standardized testing protocols and adversarial prompts to assess model safety and robustness.

Cost Calculator

Interactive cost calculator and token pricing

No Pricing Information Available

Pricing data is not available for this model.

Providers

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No provider information available for this model.

Business Decision Guide

Key factors to consider when adopting this model for enterprise use

Safety Profile

Good safety compliance (185%) with adequate protection measures.

Safety Rank: #15

Performance Metrics

Solid performance across key metrics. Good for general business applications.

Cost Efficiency

Highly cost-effective with excellent context handling.

$0.00/mo (avg. use)

Business Use Cases

Optimize your workflows with tailored AI solutions

Code Generation

Create and debug programming code

Suitability:Excellent
  • Strong coding capabilities

Best for:

Development teams, engineering departments

Chatbot

Create conversational AI assistants

Suitability:Fair
  • Cost-effective for high volume

Best for:

Customer engagement, website assistants

Customer Service

Automate support and improve response times

Suitability:Fair
  • Scalable solution

Best for:

Support teams, customer success departments

Creative Projects

Generate ideas, stories, and creative content

Suitability:Fair
  • Logical creativity

Best for:

Design teams, storytellers, game developers

Content Creation

Generate articles, blogs, and marketing copy

Suitability:Fair
  • Standard capabilities for this use case

Best for:

Marketing teams, publishers, content agencies

Research Assistant

Analyze information and support research

Suitability:Fair
  • Standard capabilities for this use case

Best for:

R&D departments, data analysis teams

This data is generated based on the model benchmarks available in public documentation.

Meta Models Comparison

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Safety Score Comparison

Input Cost Comparison (per 1M tokens)

Output Cost Comparison (per 1M tokens)