OpenAIReleased Aug 2025Proprietary
GPT-5 Nano has no score on the current Vision Evals. On the earlier Vision Evals board (last updated July 9, 2026) it passed 58.2% of visual understanding tasks (#51 of 77).
It is a proprietary model from OpenAI, so commercial use follows OpenAI's terms.
Drag and drop an image here, or click to browse
Model settings
Thinking level
Max output tokens
Default 65,536 · max 65,536
Sign in to adjust thinking and output length per run.
Results appear here. Add an image or pick an example to run GPT-5 Nano.
GPT-5 Nano has not yet been evaluated on the current benchmark. The results below are from the legacy version of Vision Evals, our previous benchmark. See the current Vision Evals
| Category | Passed | Score |
|---|---|---|
| Defect Detection | 13 / 15 | 86.7% |
| Document Understanding | 6 / 9 | 66.7% |
| Object Understanding | 9 / 14 | 64.3% |
| Spatial Understanding | 11 / 19 | 57.9% |
| Object Counting | 0 / 10 | 0% |
| Category | Passed | Score |
|---|---|---|
| License Plate Recognition | 25 / 30 | 83.3% |
| VQA & Extraction | 44 / 60 | 73.3% |
| Text Recognition | 21 / 30 | 70% |
| Focused Scene OCR | 64 / 99 | 64.6% |
| Handwritten Math | 4 / 10 | 40% |
Scores based on a single evaluation run · Methodology
View all legacy Vision Evals results →GPT-5 Nano, released by OpenAI on August 7, 2025, is the smallest and most cost-efficient model in the GPT-5 family. Like its larger counterparts, it is multimodal—accepting text and images, supporting tool use, structured outputs, and reasoning—but it is optimized for speed, low latency, and affordability. It features input and output token limits of roughly 272K and 128K tokens respectively, enabling large-context processing even at its compact scale. Its knowledge cutoff is around May 2024, slightly earlier than the full GPT-5 model.
GPT-5 Nano costs $0.050 per 1M input tokens and $0.400 per 1M output tokens.
Pricing updated Nov 10, 2026
GPT-5 Nano runs as a hosted REST endpoint through Roboflow Workflows. Pick a task, then hand the prompt to your coding agent or copy the code. Deploying the workflow into a free Roboflow workspace replaces the your-workspace and YOUR_API_KEY placeholders with your own.
Add the Roboflow MCP server
claude mcp add --transport http roboflow https://mcp.roboflow.com/mcp
Run /mcp and authorize Roboflow in your browser when the OAuth flow opens.
Start a new Claude Code session so the MCP loads, then paste the prompt below (it works the same in any agent).
Deploy this workflow to your Roboflow workspace to use it.
Integrate the Roboflow "GPT-5 Nano" workflow into my app.
- Endpoint: POST https://serverless.roboflow.com/<your-workspace>/workflows/gpt-5-nano-captioning
- Auth: send my Roboflow API key as `api_key` in the request body, read from the ROBOFLOW_API_KEY env var (never hardcode).
- Body: { "api_key": ..., "inputs": { `image`: { type: "url" | "base64", value } } }.
- Billing: this workflow needs no provider API key — inference runs on my Roboflow credits. A BYO provider key can be added to the model step in the Roboflow workflow editor later.
With the Roboflow MCP connected, call `workflows_get` on "gpt-5-nano-captioning" to read the exact input schema (the source of truth), then `workflows_run` on a sample image to confirm the output shape before writing code (the MCP is authenticated, so this needs no key). Without the MCP, use the contract above.
Before running the app, set up these keys so it does not error at runtime:
- `ROBOFLOW_API_KEY` (sent as `api_key`) from https://app.roboflow.com/settings/api
Create a .gitignore'd .env with these variables, using placeholder values for any I haven't given you. Then pause and tell me directly, in your reply: the full path to the .env file, exactly which keys I need to paste in, and the link to get each one. Wait for me to confirm I've added them before you run anything. Do not run the app until I confirm.
Then add the integration to my codebase: match my project's language, framework, and conventions; read every key from environment variables (never hardcode); add basic error handling; and include a small runnable example. If you can't tell what language my project uses, ask me.pip install inference-sdkDeploy this workflow to your Roboflow workspace to use it.
# Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
# 1. Import the library
from inference_sdk import InferenceHTTPClient
# 2. Connect to your workflow
client = InferenceHTTPClient(
api_url="https://serverless.roboflow.com",
api_key="YOUR_API_KEY"
)
# 3. Run your workflow on an image
result = client.run_workflow(
workspace_name="your-workspace",
workflow_id="gpt-5-nano-captioning",
images={
"image": "YOUR_IMAGE.jpg" # Path to your image file
},
use_cache=True # cache workflow definition for 15 minutes
)
# 4. Get your results
print(result)Deploy this workflow to your Roboflow workspace to use it.
// Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
const response = await fetch('https://serverless.roboflow.com/your-workspace/workflows/gpt-5-nano-captioning', {
method: 'POST',
headers: {
'Content-Type': 'application/json'
},
body: JSON.stringify({
api_key: 'YOUR_API_KEY',
inputs: {
"image": {"type": "url", "value": "IMAGE_URL"}
}
})
});
const result = await response.json();
console.log(result);Deploy this workflow to your Roboflow workspace to use it.
# Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
curl --location 'https://serverless.roboflow.com/your-workspace/workflows/gpt-5-nano-captioning' \
--header 'Content-Type: application/json' \
--data '{
"api_key": "YOUR_API_KEY",
"inputs": {
"image": {"type": "url", "value": "IMAGE_URL"}
}
}'Add the Roboflow MCP server
claude mcp add --transport http roboflow https://mcp.roboflow.com/mcp
Run /mcp and authorize Roboflow in your browser when the OAuth flow opens.
Start a new Claude Code session so the MCP loads, then paste the prompt below (it works the same in any agent).
Deploy this workflow to your Roboflow workspace to use it.
Integrate the Roboflow "GPT-5 Nano" workflow into my app.
- Endpoint: POST https://serverless.roboflow.com/<your-workspace>/workflows/gpt-5-nano-ocr
- Auth: send my Roboflow API key as `api_key` in the request body, read from the ROBOFLOW_API_KEY env var (never hardcode).
- Body: { "api_key": ..., "inputs": { `image`: { type: "url" | "base64", value } } }.
- Billing: this workflow needs no provider API key — inference runs on my Roboflow credits. A BYO provider key can be added to the model step in the Roboflow workflow editor later.
With the Roboflow MCP connected, call `workflows_get` on "gpt-5-nano-ocr" to read the exact input schema (the source of truth), then `workflows_run` on a sample image to confirm the output shape before writing code (the MCP is authenticated, so this needs no key). Without the MCP, use the contract above.
Before running the app, set up these keys so it does not error at runtime:
- `ROBOFLOW_API_KEY` (sent as `api_key`) from https://app.roboflow.com/settings/api
Create a .gitignore'd .env with these variables, using placeholder values for any I haven't given you. Then pause and tell me directly, in your reply: the full path to the .env file, exactly which keys I need to paste in, and the link to get each one. Wait for me to confirm I've added them before you run anything. Do not run the app until I confirm.
Then add the integration to my codebase: match my project's language, framework, and conventions; read every key from environment variables (never hardcode); add basic error handling; and include a small runnable example. If you can't tell what language my project uses, ask me.pip install inference-sdkDeploy this workflow to your Roboflow workspace to use it.
# Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
# 1. Import the library
from inference_sdk import InferenceHTTPClient
# 2. Connect to your workflow
client = InferenceHTTPClient(
api_url="https://serverless.roboflow.com",
api_key="YOUR_API_KEY"
)
# 3. Run your workflow on an image
result = client.run_workflow(
workspace_name="your-workspace",
workflow_id="gpt-5-nano-ocr",
images={
"image": "YOUR_IMAGE.jpg" # Path to your image file
},
use_cache=True # cache workflow definition for 15 minutes
)
# 4. Get your results
print(result)Deploy this workflow to your Roboflow workspace to use it.
// Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
const response = await fetch('https://serverless.roboflow.com/your-workspace/workflows/gpt-5-nano-ocr', {
method: 'POST',
headers: {
'Content-Type': 'application/json'
},
body: JSON.stringify({
api_key: 'YOUR_API_KEY',
inputs: {
"image": {"type": "url", "value": "IMAGE_URL"}
}
})
});
const result = await response.json();
console.log(result);Deploy this workflow to your Roboflow workspace to use it.
# Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
curl --location 'https://serverless.roboflow.com/your-workspace/workflows/gpt-5-nano-ocr' \
--header 'Content-Type: application/json' \
--data '{
"api_key": "YOUR_API_KEY",
"inputs": {
"image": {"type": "url", "value": "IMAGE_URL"}
}
}'Add the Roboflow MCP server
claude mcp add --transport http roboflow https://mcp.roboflow.com/mcp
Run /mcp and authorize Roboflow in your browser when the OAuth flow opens.
Start a new Claude Code session so the MCP loads, then paste the prompt below (it works the same in any agent).
Deploy this workflow to your Roboflow workspace to use it.
Integrate the Roboflow "GPT-5 Nano" workflow into my app.
- Endpoint: POST https://serverless.roboflow.com/<your-workspace>/workflows/gpt-5-nano-open-prompt
- Auth: send my Roboflow API key as `api_key` in the request body, read from the ROBOFLOW_API_KEY env var (never hardcode).
- Body: { "api_key": ..., "inputs": { `image`: { type: "url" | "base64", value }, `prompt`: text } }.
- Billing: this workflow needs no provider API key — inference runs on my Roboflow credits. A BYO provider key can be added to the model step in the Roboflow workflow editor later.
With the Roboflow MCP connected, call `workflows_get` on "gpt-5-nano-open-prompt" to read the exact input schema (the source of truth), then `workflows_run` on a sample image to confirm the output shape before writing code (the MCP is authenticated, so this needs no key). Without the MCP, use the contract above.
Before running the app, set up these keys so it does not error at runtime:
- `ROBOFLOW_API_KEY` (sent as `api_key`) from https://app.roboflow.com/settings/api
Create a .gitignore'd .env with these variables, using placeholder values for any I haven't given you. Then pause and tell me directly, in your reply: the full path to the .env file, exactly which keys I need to paste in, and the link to get each one. Wait for me to confirm I've added them before you run anything. Do not run the app until I confirm.
Then add the integration to my codebase: match my project's language, framework, and conventions; read every key from environment variables (never hardcode); add basic error handling; and include a small runnable example. If you can't tell what language my project uses, ask me.pip install inference-sdkDeploy this workflow to your Roboflow workspace to use it.
# Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
# 1. Import the library
from inference_sdk import InferenceHTTPClient
# 2. Connect to your workflow
client = InferenceHTTPClient(
api_url="https://serverless.roboflow.com",
api_key="YOUR_API_KEY"
)
# 3. Run your workflow on an image
result = client.run_workflow(
workspace_name="your-workspace",
workflow_id="gpt-5-nano-open-prompt",
images={
"image": "YOUR_IMAGE.jpg" # Path to your image file
},
parameters={
"prompt": "Describe what you see in the image"
},
use_cache=True # cache workflow definition for 15 minutes
)
# 4. Get your results
print(result)Deploy this workflow to your Roboflow workspace to use it.
// Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
const response = await fetch('https://serverless.roboflow.com/your-workspace/workflows/gpt-5-nano-open-prompt', {
method: 'POST',
headers: {
'Content-Type': 'application/json'
},
body: JSON.stringify({
api_key: 'YOUR_API_KEY',
inputs: {
"image": {"type": "url", "value": "IMAGE_URL"},
"prompt": "Describe what you see in the image"
}
})
});
const result = await response.json();
console.log(result);Deploy this workflow to your Roboflow workspace to use it.
# Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
curl --location 'https://serverless.roboflow.com/your-workspace/workflows/gpt-5-nano-open-prompt' \
--header 'Content-Type: application/json' \
--data '{
"api_key": "YOUR_API_KEY",
"inputs": {
"image": {"type": "url", "value": "IMAGE_URL"},
"prompt": "Describe what you see in the image"
}
}'Add the Roboflow MCP server
claude mcp add --transport http roboflow https://mcp.roboflow.com/mcp
Run /mcp and authorize Roboflow in your browser when the OAuth flow opens.
Start a new Claude Code session so the MCP loads, then paste the prompt below (it works the same in any agent).
Deploy this workflow to your Roboflow workspace to use it.
Integrate the Roboflow "GPT-5 Nano" workflow into my app.
- Endpoint: POST https://serverless.roboflow.com/<your-workspace>/workflows/gpt-5-nano-object-detection
- Auth: send my Roboflow API key as `api_key` in the request body, read from the ROBOFLOW_API_KEY env var (never hardcode).
- Body: { "api_key": ..., "inputs": { `image`: { type: "url" | "base64", value }, `classes`: string array } }.
- Billing: this workflow needs no provider API key — inference runs on my Roboflow credits. A BYO provider key can be added to the model step in the Roboflow workflow editor later.
With the Roboflow MCP connected, call `workflows_get` on "gpt-5-nano-object-detection" to read the exact input schema (the source of truth), then `workflows_run` on a sample image to confirm the output shape before writing code (the MCP is authenticated, so this needs no key). Without the MCP, use the contract above.
Before running the app, set up these keys so it does not error at runtime:
- `ROBOFLOW_API_KEY` (sent as `api_key`) from https://app.roboflow.com/settings/api
Create a .gitignore'd .env with these variables, using placeholder values for any I haven't given you. Then pause and tell me directly, in your reply: the full path to the .env file, exactly which keys I need to paste in, and the link to get each one. Wait for me to confirm I've added them before you run anything. Do not run the app until I confirm.
Then add the integration to my codebase: match my project's language, framework, and conventions; read every key from environment variables (never hardcode); add basic error handling; and include a small runnable example. If you can't tell what language my project uses, ask me.pip install inference-sdkDeploy this workflow to your Roboflow workspace to use it.
# Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
# 1. Import the library
from inference_sdk import InferenceHTTPClient
# 2. Connect to your workflow
client = InferenceHTTPClient(
api_url="https://serverless.roboflow.com",
api_key="YOUR_API_KEY"
)
# 3. Run your workflow on an image
result = client.run_workflow(
workspace_name="your-workspace",
workflow_id="gpt-5-nano-object-detection",
images={
"image": "YOUR_IMAGE.jpg" # Path to your image file
},
parameters={
"classes": ["class1", "class2", "class3"]
},
use_cache=True # cache workflow definition for 15 minutes
)
# 4. Get your results
print(result)Deploy this workflow to your Roboflow workspace to use it.
// Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
const response = await fetch('https://serverless.roboflow.com/your-workspace/workflows/gpt-5-nano-object-detection', {
method: 'POST',
headers: {
'Content-Type': 'application/json'
},
body: JSON.stringify({
api_key: 'YOUR_API_KEY',
inputs: {
"image": {"type": "url", "value": "IMAGE_URL"},
"classes": ["class1", "class2", "class3"]
}
})
});
const result = await response.json();
console.log(result);Deploy this workflow to your Roboflow workspace to use it.
# Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
curl --location 'https://serverless.roboflow.com/your-workspace/workflows/gpt-5-nano-object-detection' \
--header 'Content-Type: application/json' \
--data '{
"api_key": "YOUR_API_KEY",
"inputs": {
"image": {"type": "url", "value": "IMAGE_URL"},
"classes": ["class1", "class2", "class3"]
}
}'Add the Roboflow MCP server
claude mcp add --transport http roboflow https://mcp.roboflow.com/mcp
Run /mcp and authorize Roboflow in your browser when the OAuth flow opens.
Start a new Claude Code session so the MCP loads, then paste the prompt below (it works the same in any agent).
Deploy this workflow to your Roboflow workspace to use it.
Integrate the Roboflow "GPT-5 Nano" workflow into my app.
- Endpoint: POST https://serverless.roboflow.com/<your-workspace>/workflows/gpt-5-nano-classification
- Auth: send my Roboflow API key as `api_key` in the request body, read from the ROBOFLOW_API_KEY env var (never hardcode).
- Body: { "api_key": ..., "inputs": { `image`: { type: "url" | "base64", value }, `classes`: string array } }.
- Billing: this workflow needs no provider API key — inference runs on my Roboflow credits. A BYO provider key can be added to the model step in the Roboflow workflow editor later.
With the Roboflow MCP connected, call `workflows_get` on "gpt-5-nano-classification" to read the exact input schema (the source of truth), then `workflows_run` on a sample image to confirm the output shape before writing code (the MCP is authenticated, so this needs no key). Without the MCP, use the contract above.
Before running the app, set up these keys so it does not error at runtime:
- `ROBOFLOW_API_KEY` (sent as `api_key`) from https://app.roboflow.com/settings/api
Create a .gitignore'd .env with these variables, using placeholder values for any I haven't given you. Then pause and tell me directly, in your reply: the full path to the .env file, exactly which keys I need to paste in, and the link to get each one. Wait for me to confirm I've added them before you run anything. Do not run the app until I confirm.
Then add the integration to my codebase: match my project's language, framework, and conventions; read every key from environment variables (never hardcode); add basic error handling; and include a small runnable example. If you can't tell what language my project uses, ask me.pip install inference-sdkDeploy this workflow to your Roboflow workspace to use it.
# Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
# 1. Import the library
from inference_sdk import InferenceHTTPClient
# 2. Connect to your workflow
client = InferenceHTTPClient(
api_url="https://serverless.roboflow.com",
api_key="YOUR_API_KEY"
)
# 3. Run your workflow on an image
result = client.run_workflow(
workspace_name="your-workspace",
workflow_id="gpt-5-nano-classification",
images={
"image": "YOUR_IMAGE.jpg" # Path to your image file
},
parameters={
"classes": ["class1", "class2", "class3"]
},
use_cache=True # cache workflow definition for 15 minutes
)
# 4. Get your results
print(result)Deploy this workflow to your Roboflow workspace to use it.
// Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
const response = await fetch('https://serverless.roboflow.com/your-workspace/workflows/gpt-5-nano-classification', {
method: 'POST',
headers: {
'Content-Type': 'application/json'
},
body: JSON.stringify({
api_key: 'YOUR_API_KEY',
inputs: {
"image": {"type": "url", "value": "IMAGE_URL"},
"classes": ["class1", "class2", "class3"]
}
})
});
const result = await response.json();
console.log(result);Deploy this workflow to your Roboflow workspace to use it.
# Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
curl --location 'https://serverless.roboflow.com/your-workspace/workflows/gpt-5-nano-classification' \
--header 'Content-Type: application/json' \
--data '{
"api_key": "YOUR_API_KEY",
"inputs": {
"image": {"type": "url", "value": "IMAGE_URL"},
"classes": ["class1", "class2", "class3"]
}
}'GPT-5 Nano is proprietary: the weights are not distributed, and the GPT-5 Nano license is the vendor's commercial terms of service that you accept when you call the API.
Vendor terms govern data retention, whether your inputs can be trained on, rate limits, and regional availability, and they can change with notice. Review them if you handle regulated or customer data.
Proprietary terms are set by the vendor rather than negotiated per project, and no open-source obligation attaches to your code. If you would rather deploy an open model instead of GPT-5 Nano, Roboflow's licensing page lists the models whose commercial license is included when you deploy on Roboflow.
Do not hesitate to reach out with questions for your commercial project. Our team will help you start solving business problems on the first call. See Roboflow commercial licensing for the models included in each plan.
Talk to salesLicense information is provided as a guide and is not legal advice.
Usage
Past 30 DaysEstimated cost per task vs. Visual Understanding score, for this model and others ranked near it. Upper-left is the sweet spot (high quality, low cost). Based on Vision Evals (legacy) results.
10 of 10 models plotted
| Model | Score | Median tokens | Est. cost / task | Compare |
|---|---|---|---|---|
| GPT-5.4 Nano | 62.7% | 1.8K | $0.0004 | Compare |
| Llama 4 Maverick | 59.7% | 2.4K | $0.0008 | Compare |
| Claude Sonnet 4.5 | 59.7% | 2.3K | $0.0092 | Compare |
| Claude Opus 4.1 | 59.7% | 2.1K | $0.040 | Compare |
| Claude Haiku 4.5 | 58.2% | 2.3K | $0.0030 | Compare |
| GPT-5 Nano(this model) | 58.2% | 2.7K | $0.0003 | — |
| Qwen3.5 397B A17B | 58.2% | 1.5K | $0.0009 | Compare |
| Gemini 2.5 Flash | 55.2% | 476 | $0.0005 | Compare |
| Gemini 2.5 Flash-Lite | 53.7% | 301 | <$0.0001 | Compare |
| Kimi K2.5 | 35.8% | 2.7K | $0.0034 | Compare |
Other models worth comparing for similar use cases.
Other versions in the same family as GPT-5 Nano.
Yes. GPT-5 Nano accepts image input, and on Roboflow's previous vision benchmark it passed 58.2% of visual understanding tasks (#51 of 77) and scored 69% on OCR. You can test it on your own image in the demo above.
Yes. The demo on this page runs GPT-5 Nano in the free Roboflow Playground: upload an image and see results in seconds. A free account unlocks unlimited runs.