DeepReinforce: Ornith-1.0-35B-FP8
Ornith-1.0-35B-FP8 is a reasoning model from DeepReinforce released on Jun 25, 2026. It reads text and images and writes text, with a 262K token context window and up to 100K tokens per reply.
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It costs $0 per million input tokens and $0 per million output tokens.
It supports tool calling, and its weights are open, so you can host it yourself.
- Modalities
- In / out price
- $0 / $0 per 1M
- Context
- 262K
- Max output
- 100K
- Released
- Jun 25, 2026
- Knowledge cutoff
- n/a
- Tool calling
- Structured output
- Reasoning
- Prompt caching
- Open weights
- Free to use
Ornith-1.0-35B-FP8 pricing
USD, as listed on Oct 15, 2026. Most bills are input heavy, so the cached input rate matters as much as the headline price.
| Input | $0 / 1M tokens | |
|---|---|---|
| Output | $0 / 1M tokens |
What Ornith-1.0-35B-FP8 costs per month
Pick a workload close to yours or type your own volumes.
Supported request parameters
What you can set when you call Ornith-1.0-35B-FP8 through an OpenAI compatible API.
- Tools
tools - Reasoning
reasoning - Temperature
temperature
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Ornith-1.0-35B-FP8 questions
How much does the Ornith-1.0-35B-FP8 API cost?
$0 per million input tokens and $0 per million output tokens. The estimator on this page turns that into a monthly bill for your own workload.
What is the context window of Ornith-1.0-35B-FP8?
262,000 tokens of input, and it can write up to 100,000 tokens in one reply. As a rough guide, a million tokens is around 750,000 English words.
Does Ornith-1.0-35B-FP8 support tool calling?
Yes. It accepts tool definitions, which is what an agent needs to call your APIs.
Is Ornith-1.0-35B-FP8 open source?
Its weights are published, so you can run it on your own hardware or a cloud of your choice. Read the license before commercial use.
When was Ornith-1.0-35B-FP8 released?
Jun 25, 2026.
Catalog data from models.dev (MIT licensed), refreshed daily, last on Oct 15, 2026. The summary is written from these facts.
Building on Ornith-1.0-35B-FP8?
I test your real tasks on it and the two closest alternatives, then ship the agent on whichever wins on cost and accuracy.
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