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Meta: Llama 3.2 3B Instruct Coding Benchmark

Llama 3.2 3B is a 3-billion-parameter multilingual large language model, optimized for advanced natural language processing tasks like dialogue generation, reasoning, and summarization. Designed with the latest transformer architecture, it...

Context131,072tokens
Max Output117,964tokens
Inputmodality
Price$0.05/1M input

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Coding Performance

Coding benchmarks and performance metrics for development tasks

Security

Enkrypt AI red-team scores for Meta: Llama 3.2 3B Instruct. Each value is the share of successful attacks on a 0โ€“100 scale โ€” lower is safer.

Overall risk
38.3

Lower is safer

Safety

28.5/ 100

Composite safety risk from Enkrypt red-team evaluations. Lower is safer.

NIST

38.0/ 100

Average attack success across NIST-mapped tests: bias, harm, toxicity, CBRN, and insecure code.

OWASP

48.0/ 100

Weighted average of the same tests using OWASP Top 10 for LLMs 2025 risk rankings.

Attack categories

Percentage of successful attacks in each Enkrypt red-team category.

Jailbreak8.0/ 100

Share of jailbreak tests that bypassed the model's safety constraints.

Bias88.9/ 100

Share of tests that elicited biased responses.

Harmful content15.0/ 100

Share of tests that produced dangerous, violent, or hateful content.

Toxicity4.5/ 100

Share of tests that produced toxic or abusive content.

CBRN14.3/ 100

Share of tests that elicited chemical, biological, radiological, or nuclear assistance.

Insecure code68.9/ 100

Share of tests that produced vulnerable or malicious code.

Score
Value
Overall risk
38.3 / 100
Safety
28.5 / 100
NIST
38.0 / 100
OWASP
48.0 / 100
Jailbreak
8.0 / 100
Bias
88.9 / 100
Harmful content
15.0 / 100
Toxicity
4.5 / 100
CBRN
14.3 / 100
Insecure code
68.9 / 100

Security scores from the Enkrypt AI Safety Leaderboard ยท Last checked Sep 17, 2026

Real-World Usage

Real-world usage statistics from the Kilo Code community

No usage data available for this model yet.

No ranking data available for this model yet.

Real-world metrics from the Kilo Code Leaderboard

Pricing

Cost per 1 million tokens

Input Tokens
$0.05
per 1M tokens
Output Tokens
$0.33
per 1M tokens

Example Cost

Analyzing a 10,000 line codebase (โ‰ˆ40k input tokens, 10k output tokens) costs approximately $0.0053

Coding Capabilities

Features and parameters relevant to coding tasks

Coding Features

Function Calling
Can call external functions/APIs
Tool Choice
Control over function selection
Structured Outputs
JSON schema validation
Reasoning Tokens
Extended thinking for complex problems

Pricing details from OpenRouter

Technical Details

Architecture and implementation specifications

Specifications
Model ID
meta-llama/llama-3.2-3b-instruct
Created
September 25, 2024
Tokenizer
Llama3
Input Modalities
Text
Context Window
131,072 tokens
Max Completion Tokens
117,964 tokens
Input Price
$0.05 per 1M tokens
Output Price
$0.33 per 1M tokens
Content Moderation
Disabled

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