Model catalog
O

Ox Alpha

API

Ox Alpha is a language model from Stealth with a 1M-token context window. Provider list price is $0 per million input tokens and $0 per million output; on Allocate you pay $0 and $0. It is a closed model served over API; the weights are not published.

Pricing

Provider listOn Allocate
Input, per M tokens$0$0
Output, per M tokens$0$0

Prices checked 2026-07-21.

Price against its peers

Provider list prices per M tokens, Ox Alpha against its nearest language peers by price.

What a real workload costs

Take 1,000,000 requests a month at 1,200 input and 350 output tokens each: 1,200M input and 350M output tokens. At list prices that is 1,200 × $0 + 350 × $0 = $0 a month. Billed on Allocate it is $0.

Ox Alpha 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 stealth/ox-alpha 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": "stealth/ox-alpha",
    "messages": [{"role": "user",
      "content": "Summarise the attached contract."}]
  }'
200 · stealth/ox-alpha · inside your boundary

Common questions

How much does Ox Alpha cost per million tokens?

Provider list price is $0 per million input tokens and $0 per million output tokens. On Allocate you pay $0 in and $0 out.

What context window does Ox Alpha have?

1,048,576 tokens (1M). At roughly 0.75 words per token, that is about 786k words of English text per request.

Can I fine-tune Ox Alpha?

No. Ox Alpha 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 Ox Alpha on Allocate?

Send stealth/ox-alpha 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.

Compare against