1M
Context window
Keep codebases, docs, and task history in view.
FRONTIER REASONING MODEL
Ox Alphafor coding.For what comes next.
A 1M-context AI model for efficient coding and real-world production use.
1M token contextMultimodal input
MODEL SPECIFICATIONS
Built for long, serious work.
The headline limits describe the model rather than a guarantee for every route. Check the active provider listing before planning a workload around a particular quota, modality, or price.
128K
Maximum output
Room for detailed solutions and structured plans.
Text · Image · Video
Multimodal input
Work across the formats your real projects use.
$0
Current availability
Currently freeAT-A-GLANCE REFERENCE
Model parameters and access details.
Use this table as a planning reference, then confirm live availability, routing, and pricing with the provider that serves your requests.
| Parameter | Details |
|---|---|
| Model identifier | stealth/ox-alpha in the documented chat completion request body. |
| Context and output | 1M-token context window and up to 128K output, subject to current provider and routing limits. |
| Input modalities | Described as accepting text, image, and video input. This browser demo supports text and an optional HTTPS image URL. |
| API format | OpenAI-compatible Chat Completions API. Review the API documentation for headers, request fields, and response examples. |
| Authentication | Use a provider API key as a Bearer token. Store production keys in a server-side environment variable, not browser code. |
| Reasoning and tools | The documented request supports reasoning.enabled, tool definitions, and tool choice controls when your provider route supports them. |
| Availability and cost | Verify current availability, limits, routing, and pricing with TokenRa before deployment. |
DESIGNED FOR THE WORK AHEAD
One model. Three ways to move faster.
From the first prototype to production maintenance, the model is useful when the surrounding context matters as much as the next answer.
Ship clearer code.
Generate, refactor, debug, test, and explain code while keeping the surrounding context nearby.
Plan the next action.
Break down long tasks, coordinate tools, summarize state, and keep a workflow moving.
Build for reality.
Analyze documents, design rollouts, assess risk, and turn messy inputs into decisions.
CODING WITH LONG CONTEXT
Use Ox Alpha for coding tasks that depend on the whole picture.
Small coding prompts are easy to isolate. Real maintenance work is not: a request can involve a bug report, a service boundary, a migration, a test suite, configuration, and several versions of an API contract. A long context window gives a coding workflow room to include the material that explains why a change exists, instead of asking the model to infer that context from a short snippet.
Use the model to map an unfamiliar repository, propose a constrained implementation, review a diff against surrounding code, or turn test failures into a debugging checklist. It can also help translate product requirements into acceptance criteria, draft focused test cases, and explain trade-offs in a refactor. The useful unit is not a single generated function; it is a traceable workflow where inputs, assumptions, and proposed edits remain visible to the engineer who reviews them.
For reliable coding use, keep the request bounded. Send the relevant files and constraints, ask for a concrete output format, and validate generated code with your normal tests and review process. Context capacity does not replace a build, a security review, or ownership of the final change.
AGENTIC WORKFLOWS
Support multi-step agentic work without losing task state.
Agentic work usually alternates between planning, gathering evidence, invoking a tool, and deciding what to do next. Each step adds facts that the next step needs. A large context can hold a task brief, prior tool results, policies, intermediate plans, and a compact record of decisions so an application has less reason to discard useful state prematurely.
With the documented OpenAI-compatible request shape, an application can send tool definitions and control tool choice where the active provider route supports them. Common patterns include asking the model to classify an incoming task, generate a structured action plan, select from a narrow set of tools, then summarize the result for a human or the next step. The model can support that workflow; your application still owns permissions, retries, tool execution, and whether an action is allowed.
Design agents with explicit checkpoints. Treat tool output as untrusted external input, make side effects visible, and require confirmation for irreversible operations. Test prompt instructions and schemas against representative edge cases before allowing a workflow to operate beyond a sandbox.
PRODUCTION API USE
Evaluate production use through the API, not a demo alone.
A browser prompt is useful for exploring behavior, but production use starts with an integration plan. The API documentation shows an OpenAI-compatible Chat Completions endpoint, Bearer authentication, and request parameters such as messages, reasoning, sampling controls, and tools. That makes it practical to prototype in an existing chat-completions client while keeping the request boundary explicit.
Before release, test the inputs your product actually receives: prompt size, multimodal URLs where applicable, tool schemas, output limits, error responses, and provider-specific rate or routing behavior. Measure the latency and cost for representative workloads rather than extrapolating from a one-off interaction. The published context and output limits are model characteristics; the usable limits for an application can vary with the provider route and current service settings.
Keep credentials and controls on the server. Store API keys in environment variables, log only what is safe to retain, define timeouts and retries, and build a fallback or user-facing failure path for provider errors. For high-impact outputs, add validation and human review appropriate to the decision being made.
OPENAI-COMPATIBLE API
From first prompt to production endpoint.
Use the documented OpenAI-compatible endpoint with the same model that powers the playground. Read the request shape, model limits, authentication guidance, and integration notes before connecting it to an application.
QUESTIONS, ANSWERED
Questions about access, context, and API use.
These answers describe the documented route and current listing. Confirm live provider details before using the model in a customer-facing workflow.
What is Ox Alpha?
Ox Alpha is a frontier reasoning model available through TokenRa using the model ID stealth/ox-alpha. It is positioned for coding, long-context workflows, and API-based applications.
Is Ox Alpha free?
Ox Alpha is currently listed as free on TokenRa. Provider availability, routing, limits, and pricing can change, so verify the live TokenRa listing before relying on it in a production workflow.
What does 1M context mean?
A 1M-token context window is the amount of prompt history and source material the model can consider in one request. The usable amount depends on current provider limits and the space reserved for the response.
How do I get an API key?
Create a free account at tokenra.io/register, then generate an API key in your account dashboard. Keep the key in a server-side environment variable for production — never expose it in client-side code.
Can I use Ox Alpha for coding and agentic workflows?
The model can be used for code understanding, implementation, debugging, testing, task planning, and tool-oriented workflows. Evaluate prompts, tool schemas, provider limits, and review controls in your own environment before deployment.
What inputs and API format does Ox Alpha support?
It is described as accepting text, image, and video input. The documented API uses an OpenAI-compatible Chat Completions format; this browser demo accepts text plus an optional HTTPS image URL.
Ready to build with Ox Alpha?
Read the API guide and connect your Tokenra workflow.