Ox Alpha free: Pricing, Limits, and API Setup Guide

Ox Alpha is a stealth reasoning model designed for software engineering, sustained agentic work, complex reasoning, and workflows that combine text with visual context. The current OpenRouter listing shows a free price, with both input and output rates displayed as $0 per million tokens. That makes it an attractive option for testing large prompts, coding agents, and multimodal experiments without an immediate token charge.

The model was listed on August 20, 2026, and its provider remains anonymous during the preview. OpenRouter acts as the routing and access platform rather than the developer or owner. For the latest availability details, review the Ox Alpha OpenRouter listing before building a production workflow.

Video Highlights:

• Ox Alpha is tested across coding, vector illustration, 3D modeling, and interactive applications.
• The demonstrations emphasize a 1M-token context window and multimodal input.
• Reported experiments include a React and Python full-stack task board.
• The model is presented as free during the current preview period.

A free listing does not automatically mean unlimited or permanent access. Availability can change as capacity, preview terms, or provider policies change. Treat the current price as a snapshot of the listing on August 22, 2026, not as a long-term guarantee.

The most important distinction is between zero token pricing and unrestricted usage. OpenRouter reports one provider for Ox Alpha, with a displayed P50 latency of 5.30 seconds and throughput of 23 tokens per second on the provider row. The same page includes broader recent performance percentiles, so observed speed can vary by request type, prompt size, time, and traffic.

The listing reports 99.99% uptime and 99.51% availability over the previous three days in the captured data. These figures describe a recent monitoring window and should not be interpreted as a permanent service-level commitment. The page also reports an average tool-call error rate of 2.27% and an average cache hit rate of 81.72%.

For casual testing, the reported speed is reasonable for a reasoning-oriented model. For interactive applications, design around streaming responses and retry handling. Long outputs, complex tool calls, and large visual prompts may feel slower than short text requests.

The model’s activity data also indicates significant use by coding and agent applications. Listed applications include Hermes Agent, Claude Code, omp, DeepSeek Harness, and pi. This suggests that the model’s strongest practical identity is not casual conversation alone, but extended coding and agentic work.

The simplest technical route is OpenRouter’s OpenAI-compatible API. You need an OpenRouter API key, the model slug stealth/ox-alpha, and a request sent to the platform’s compatible endpoint. The OpenRouter documentation provides SDK and raw request examples, while the basic flow remains consistent across languages.

A minimal shell setup can use the following environment variable:

export OPENROUTER_API_KEY=sk-or-v1-...

The model slug is the key detail that changes from one model to another. OpenRouter’s page states that its API is OpenAI-compatible, so many existing SDK patterns can be adapted by changing the base URL, API key, and model name.

For advanced requests, the listing exposes parameters such as max_tokens, temperature, top_p, tools, tool_choice, top_k, and response_format. Begin with conservative settings. Increase output limits only when the task needs them, and use structured response formats when downstream code expects predictable data.

Ox Alpha’s reported demonstrations cover a broad set of tasks, but the strongest pattern is interactive technical work. The model reportedly handled a vector raccoon illustration, a seven-compartment 3D pill organizer, a responsive scissor lift, a premium landing page, a basketball physics prototype, and a React/FastAPI task board.

These examples show useful range rather than a universal quality guarantee. A good evaluation should match your own workload. Test code correctness, visual interpretation, tool reliability, and response consistency instead of judging the model from one impressive output.

The video source reports an 80% score on 10 DeepSWE SWE coding tasks, compared with several other models in that particular test. Because this is a single reported evaluation, use it as a signal rather than a definitive ranking. Benchmarks can vary based on task selection, harness configuration, tool access, and scoring rules.

For practical use, Ox Alpha may be especially appealing when you need to combine a large codebase with visual references. For example, you could provide a repository summary, an interface screenshot, and a detailed implementation request in one workflow. Even then, review generated code manually and run tests before deployment.

The provider notice is important for anyone testing confidential material. OpenRouter states that prompts and completions for this stealth model are retained by the provider and are not used for training; other use is governed by the Stealth Model Terms. The provider is anonymous during the preview, so teams should review the applicable terms before sending proprietary or personal information.

Use a staged rollout. Begin with synthetic examples, remove secrets from code samples, and keep production credentials outside prompts. If your workflow uses images or videos, confirm that those assets are appropriate for third-party processing.

The safest interpretation of Ox Alpha free is “currently listed at no token charge for preview access,” not “appropriate for every sensitive workload.” Free pricing reduces one cost barrier, but it does not remove engineering, compliance, monitoring, or review requirements.