Model catalog

ByteDance Seedream 5.0 Lite

API

ByteDance Seedream 5.0 Lite is a image model from ByteDance. Provider list price is $0.035 per item; on Allocate you pay $0.037 with the 7% transaction fee. It is a closed model served over API; the weights are not published.

Pricing

Provider listOn Allocate
Price, per item$0.035$0.037

Token usage bills at the provider list price plus the 7% transaction fee. Prices checked 2026-07-08.

Price against its peers

Provider list prices per item, ByteDance Seedream 5.0 Lite against its nearest image peers by price.

What a real workload costs

Generating 1,000 one-megapixel images costs 1,000 × $0.035 = $35 at list, or $37.45 billed on Allocate with the 7% transaction fee included.

ByteDance Seedream 5.0 Lite is served over API. Route traffic to it by name, meter every token, and swap it out in one click when a better fit ships.

Example usage

Point a route at bytedance/seedream-5.0-lite and the endpoint never changes; swap the model behind it whenever you want.

api.allocate.network
curl https://api.allocate.network/v1/chat/completions \
  -H "Authorization: Bearer $ALLOCATE_KEY" \
  -d '{
    "model": "bytedance/seedream-5.0-lite",
    "messages": [{"role": "user",
      "content": "Summarise the attached contract."}]
  }'
200 · bytedance/seedream-5.0-lite · inside your boundary

Common questions

How much does ByteDance Seedream 5.0 Lite cost?

Provider list price is $0.035 per item (Pricing is $0.035 for both 2K & 3K outputs). On Allocate you pay list plus the 7% transaction fee: $0.037.

Can I fine-tune ByteDance Seedream 5.0 Lite?

No. ByteDance Seedream 5.0 Lite is a closed model served over API; the weights are not published. If you want a model you can train and own, start from an open-weights base in the catalog and fine-tune that.

How do I call ByteDance Seedream 5.0 Lite on Allocate?

Send bytedance/seedream-5.0-lite in the model field of the OpenAI-compatible endpoint at api.allocate.network/v1, or point a route name (like prod/support-agent) at it so you can swap the model later without a deploy.