GPT 4.1 Nano
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GPT 4.1 Nano

gpt-4.1-nano
OpenAI
Ultra-lightweight model with million-token context, optimized for speed and low latency, costing only $0.10 per million input tokens. It is suitable for edge computing and real-time interaction. The automatic caching mechanism offers a 75% cost reduction on cache hits.

Pricing

PricingCache ReadImage GenerationWeb Search
$0.100$0.400
$0.025/M tokens-$0.01/request

Input Modalities

  • Text
  • Vision

Output Modalities

  • Text

Capabilities

  • Tools
  • Tool calling
  • Structured outputs
  • Long context

Providers

Azure gpt-4.1-nano
Pricing$0.100$0.400
Cache Read$0.025/M tokens
Web Search$0.01/request
Context1M
Max output32K
Latency1.0S
Throughput113.9TPS
Uptime
100.00% uptime 2 days ago
99.99% uptime yesterday
100.00% uptime today
OpenAI gpt-4.1-nano
Pricing$0.100$0.400
Cache Read$0.025/M tokens
Web Search$0.01/request
Context1M
Max output32K
Latency0.8S
Throughput72.7TPS
Uptime
0.00% uptime 2 days ago
0.00% uptime yesterday
0.00% uptime today

Performance for gpt-4.1-nano

Uptime is the percentage of requests that succeeded over the past 72 hours. AIHubMix continuously monitors every provider and automatically retries with the next-best provider when one returns an error or responds too slowly; Latency is total round-trip time (lower is better); Throughput is how fast the model writes (tokens per second, higher is better).

Uptime
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Latency
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Throughput
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Try this model

Python
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AIHUBMIX_API_KEY"],
    base_url="https://aihubmix.com/v1",
)

response = client.chat.completions.create(
    model="gpt-4.1-nano",
    messages=[
      {
        "role": "user",
        "content": "Hello, how are you?"
      }
    ],
    max_tokens=1024,
    stream=False,
)

print(response.choices[0].message.content)

Frequently asked questions

What is GPT 4.1 Nano?

Ultra-lightweight model with million-token context, optimized for speed and low latency, costing only $0.10 per million input tokens. It is suitable for edge computing and real-time interaction. The automatic caching mechanism offers a 75% cost reduction on cache hits.