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OpenAI: GPT-4.1 Nano Coding Benchmark

For tasks that demand low latency, GPT‑4.1 nano is the fastest and cheapest model in the GPT-4.1 series. It delivers exceptional performance at a small size with its 1 million...

Context1,047,576tokens
Max Output32,768tokens
Inputmodalities
Price$0.10/1M input
Ranks from Kilo Code LeaderboardPricing via OpenRouter

Try OpenAI: GPT-4.1 Nano in Kilo Code

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Coding Performance

Coding benchmarks and performance metrics for development tasks

Security

Enkrypt AI red-team scores for OpenAI: GPT-4.1 Nano. Each value is the share of successful attacks on a 0–100 scale — lower is safer.

Overall risk
30.8

Lower is safer

Safety

21.1/ 100

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

NIST

31.0/ 100

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

OWASP

35.0/ 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.

Jailbreak9.7/ 100

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

Bias87.1/ 100

Share of tests that elicited biased responses.

Harmful content40.0/ 100

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

Toxicity3.9/ 100

Share of tests that produced toxic or abusive content.

CBRN10.5/ 100

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

Insecure code12.4/ 100

Share of tests that produced vulnerable or malicious code.

Score
Value
Overall risk
30.8 / 100
Safety
21.1 / 100
NIST
31.0 / 100
OWASP
35.0 / 100
Jailbreak
9.7 / 100
Bias
87.1 / 100
Harmful content
40.0 / 100
Toxicity
3.9 / 100
CBRN
10.5 / 100
Insecure code
12.4 / 100

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

Real-World Usage

Real-world usage statistics from the Kilo Code community

Weekly Token Usage

No ranking data available for this model yet.

Real-world metrics from the Kilo Code Leaderboard

Pricing

Cost per 1 million tokens

Input Tokens
$0.10
per 1M tokens
Output Tokens
$0.40
per 1M tokens

Example Cost

Analyzing a 10,000 line codebase (≈40k input tokens, 10k output tokens) costs approximately $0.0080

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
openai/gpt-4.1-nano
Created
April 14, 2025
Tokenizer
GPT
Input Modalities
ImageTextFile
Context Window
1,047,576 tokens
Max Completion Tokens
32,768 tokens
Input Price
$0.10 per 1M tokens
Output Price
$0.40 per 1M tokens
Cache Read Price
$0.02 per 1M tokens
Content Moderation
Enabled

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