Model catalog
H

HappyHorse-1.0-T2V

API

HappyHorse-1.0-T2V is a video model from happyhorse. Provider list price is $0.10 per video; on Allocate you pay $0.11 with the 7% transaction fee. It is a closed model served over API; the weights are not published.

Pricing

Provider listOn Allocate
Price, per video$0.10$0.11

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

Price against its peers

Veo 3.1$0.08
Vidu Q3$0.098
HappyHorse-1.0-T2V$0.10

Provider list prices per video, HappyHorse-1.0-T2V against its nearest video peers by price.

What a real workload costs

Generating 100 clips costs 100 × $0.10 = $10 at list (per 5 seconds of video), or $10.70 billed on Allocate with the 7% transaction fee included.

HappyHorse-1.0-T2V 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 happyhorse/happyhorse-1.0-t2v 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": "happyhorse/happyhorse-1.0-t2v",
    "messages": [{"role": "user",
      "content": "Summarise the attached contract."}]
  }'
200 · happyhorse/happyhorse-1.0-t2v · inside your boundary

Common questions

How much does HappyHorse-1.0-T2V cost?

Provider list price is $0.10 per video (per 5 seconds of video). On Allocate you pay list plus the 7% transaction fee: $0.11.

Can I fine-tune HappyHorse-1.0-T2V?

No. HappyHorse-1.0-T2V 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 HappyHorse-1.0-T2V on Allocate?

Send happyhorse/happyhorse-1.0-t2v 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.