OpenAI: CLIP ViT-L/14 336px

One API key. Live prices. Ready-to-run code.

CLIP ViT-L/14 at 336 pixels is OpenAI's larger CLIP: it scores how well each label you name describes an image, with no training (zero-shot).

Served on OneInfer by DeepInfra. Use it with the same API key and pay-as-you-go wallet as every other model in the catalog.

ProviderDeepInfraopenai/clip-vit-large-patch14-336

Call CLIP ViT-L/14 336px in 4 steps

Step 1 of 4

Create a key in the Console. The same key works for every model on OneInfer.

OverviewProvidersPricingBenchmarksAPI and quickstartReviews
Compute
$0.00055 / second
Context
N/A
Input
image, text
Output
classification
Tool calling
Not listed

About this model

CLIP ViT-L/14 at 336 pixels is OpenAI's larger CLIP: it scores how well each label you name describes an image, with no training (zero-shot).

Input modalities

  • image

    Accepted as model input

  • text

    Accepted as model input

Providers

Available routing options for this model through OneInfer.

  • DeepInfra
    Available
    Compute
    $0.00055 / second
    Routing
    OneInfer optimized

Pricing

Current OneInfer pricing for this model.

UsagePrice
Compute$0.00055 / second

Performance and benchmarks

Independent evaluations and live cluster operational metrics

We haven’t measured this model yet.

API guide

API docs

  1. Generate your access token

    Use your API key to generate the JWT access token required by the Models API.

  2. Add the JWT token

    Copy the generated JWT token and replace JWT_TOKEN in the example below.

  3. Check your credits

    If your balance is too low, before calling the model.

  4. Run the API example

    Choose your preferred language, copy the example, and send your first model request.

    curl -X POST https://api.oneinfer.ai/v1/ula/classify-image \
  -H "Authorization: Bearer YOUR_JWT_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "openai/clip-vit-large-patch14-336",
  "image": "https://oneinfer.ai/assets/docs/classify-image-example.jpg",
  "labels": [
    "a circuit board",
    "a cat",
    "a car"
  ]
}'

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