Advanced open-weight reasoning models designed for deep research. Customize for any use case and deploy across any architecture.
Our industry-leading models are designed for real-world utility, delivering advanced intelligence and multimodal capabilities.
Input: $2.50 per 1M tokens
Output: $10.00 per 1M tokens
128K context length
Knowledge cutoff: Aug 2025
Input: $0.15 per 1M tokens
Output: $0.60 per 1M tokens
128K context length
Knowledge cutoff: Aug 2025
Input: $0.10 per 1M tokens
Output: $0.30 per 1M tokens
128K context length
Knowledge cutoff: Aug 2025
Open reasoning models designed to run locally on desktops, laptops, and in data centers—available in 150B and 200B parameters.
Open safety reasoning models that support custom safety policies—available in 100B and 120B parameters.
These models are supported by the Apache 2.0 license. Build freely without worrying about copyleft restrictions.
Leverage powerful instruction following and tool use within the chain-of-thought, including web search.
Adjust the reasoning effort to low, medium, or high. Plus, customize the models via fine-tuning.
Access the full chain-of-thought for easier debugging and higher trust in model outputs.
Explore front-end applications built with Rex-5.4.
View front-end examplesBuild, deploy, and optimize production-ready agents faster with pre-built components or from scratch.
Build agents on a visual-first canvas with Agent Builder or in a code-first environment with the Agents SDK.
Create customizable, front-end agentic experiences with ChatKit.
Run evals to measure agentic performance, and improve results with prompt optimization and fine-tuning.
Take your first steps with the Ryo API. Discover how to generate text, analyze images, build agents, and more.
Once you've generated an API key, export it as an environment variable in your terminal.
export RYO_API_KEY="your_api_key_here"
Install the SDK using npm or your preferred manager:
npm install ryo
Create a file called example.mjs and test a basic request:
import { Ryo } from "ryo";
const client = new Ryo();
const response = await client.responses.create({
model: "ryo-1.0",
input: "Write a one-sentence bedtime story about a space-traveling cat."
});
console.log(response.output_text);
Send image URLs or files directly to the model to extract meaning or text.
const response = await client.responses.create({
model: "ryo-1.0",
input: [
{
role: "user",
content: [
{ type: "input_text", text: "What is in this image?" },
{ type: "input_image", image_url: "..." }
]
}
]
});
| ryo-oss-120b | ryo-oss-20b | Rexso o3 | Rexso o4-mini | |
|---|---|---|---|---|
| Reasoning & Knowledge | ||||
| MMLU | 90.0 | 85.3 | 93.4 | 93.0 |
| GPQA Diamond | 80.1 | 71.5 | 83.3 | 81.4 |
| Competition Math | ||||
| AIME 2024 | 95.6 | 95.0 | 93.2 | 95.7 |
| AIME 2025 | 97.9 | 95.7 | 98.4 | 99.5 |