GPT-4.1 nano
OpenAI
Overall Trust Score
OpenAI's smallest and most efficient GPT-4.1 variant, designed for high-volume, cost-sensitive applications. Optimized for speed and resource efficiency with basic capabilities.
Trust Vector
Performance & Reliability
Basic performance optimized for speed and efficiency. Best for simple tasks where ultra-low latency and cost are priorities.
task accuracy code64
task accuracy reasoning66
task accuracy general70
output consistency72
latency p50Value: 0.4s
latency p95Value: 0.8s
context windowValue: 32,000 tokens
uptime98
Security
Good security posture with standard OpenAI safety measures. Smaller model may have slightly lower resistance to adversarial attacks.
prompt injection resistance78
jailbreak resistance80
data leakage prevention83
output safety84
api security85
Privacy & Compliance
Standard OpenAI privacy practices. 30-day data retention for abuse monitoring.
data residencyValue: US (primary)
training data optout90
data retentionValue: 30 days
pii handling82
compliance certifications88
zero data retention75
Trust & Transparency
Basic transparency features. Smaller model size limits explainability depth. Higher hallucination rate than premium models.
explainability72
hallucination rate74
bias fairness76
uncertainty quantification73
model card quality82
training data transparency74
guardrails80
Operational Excellence
Excellent operational maturity leveraging OpenAI's established infrastructure. Same high-quality developer experience as larger models.
api design quality91
sdk quality93
versioning policy85
monitoring observability84
support quality87
ecosystem maturity94
license terms90
✨ Strengths
- •Ultra-low latency (~0.4s p50) ideal for real-time applications
- •Most cost-effective option in GPT-4.1 family
- •Good for high-volume, simple tasks
- •Smaller context window reduces processing overhead
- •Same API and ecosystem as premium OpenAI models
- •Reliable uptime and infrastructure
⚠️ Limitations
- •Limited coding capabilities (29.4% HumanEval)
- •Basic reasoning and knowledge (50.3% MMLU)
- •Higher hallucination rate than larger models
- •Not suitable for complex or specialized tasks
- •30-day data retention
- •Limited context window (32K tokens)
📊 Metadata
Use Case Ratings
code generation
Basic code generation for simple tasks. 29.4% HumanEval indicates limited capability for complex programming.
customer support
Good for high-volume, simple customer queries. Fast response times make it suitable for basic support automation.
content creation
Adequate for simple content tasks. Limited creativity and depth compared to larger models.
data analysis
Basic data interpretation. Not suitable for complex analytical tasks.
research assistant
Suitable for simple research queries and summaries. Limited depth for complex topics.
legal compliance
Not recommended for legal applications due to limited accuracy and reasoning.
healthcare
Not suitable for healthcare applications. Lacks accuracy and HIPAA eligibility.
financial analysis
Basic financial calculations only. Not suitable for complex financial modeling.
education
Suitable for basic educational content and simple tutoring. Limited for advanced topics.
creative writing
Basic creative writing. Less nuanced and creative than larger models.