Last refreshed 2026-05-10. Next refresh: weekly.
Why use GPT-4.1 Nano on Replicate API?
Replicate API offers GPT-4.1 Nano with pay-as-you-go pricing at $0.10/1M input tokens. Replicate is a cloud-based platform that enables users to run machine learning models easily and efficiently.
Compare GPT-4.1 Nano across 4 providers to find the best fit for your use caseInput / 1M
$0.10
Output / 1M
$0.40
Cache
Not sourced
Batch
Not sourced
Setup recipe
Python + curlInstall
pip install replicateAuth
export REPLICATE_API_TOKEN=...Call
import replicate
output = replicate.run(
"openai/gpt-4.1-nano",
input={"prompt": "Hello"}Model ID
openai/gpt-4.1-nanoRequest example
import replicate
# reads REPLICATE_API_TOKEN from env
# openai/gpt-4.1-nano format: "owner/model-name" (latest version) or "owner/model-name:version-hash"
output = replicate.run(
"openai/gpt-4.1-nano",
input={"prompt": "Hello"}
)
# Output is a list or generator depending on the model
print("".join(output))Gotchas
- Use provider model ID "openai/gpt-4.1-nano", not the LLMReference slug "gpt-4.1-nano".
- Replicate uses "owner/model-name" format (e.g. "meta/meta-llama-3-8b-instruct") for the latest version, or "owner/model-name:version-sha" to pin to a specific version. The REST endpoint splits owner and model-name into the path: /v1/models/{owner}/{model-name}/predictions.
- The examples expect REPLICATE_API_TOKEN; rename it only if your application config maps the new variable.
Compare GPT-4.1 Nano Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Replicate API | $0.10 | $0.40 |
| OpenRouter | $0.10 | $0.40 |
| OpenAI API | $0.10 | $0.40 |
| Vercel AI Gateway | $0.10 | $0.40 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.10 |
| Output tokens | $0.40 |
Capabilities
VisionMultimodalJSON / Tool useStructured OutputsCode ExecutionPrompt CachingBatch API
About GPT-4.1 Nano
Fastest and most cost-effective GPT-4.1 variant from OpenAI. Released April 2025 alongside GPT-4.1 and GPT-4.1 Mini. 1 million token context window optimized for high-volume applications.
Get Started
Model Specs
Released2025-04-01
Context1.05m
ArchitectureDecoder Only
Knowledge cutoff2025-01