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What is Ox Alpha?

Ox Alpha is a stealth reasoning model created for demanding workloads involving programming, autonomous agents, multimodal information, and complex analysis. It became publicly available in August 2026 without the developer revealing which company or research lab produced it. The model is distributed under the stealth/ox-alpha identifier and has quickly attracted interest from developers experimenting with AI coding agents. Ox Alpha can process up to 1,048,576 tokens of context, allowing large amounts of source code, documentation, logs, specifications, or other material to remain available during a task. It can also produce outputs of up to 131,072 tokens, providing substantial room for detailed code, analysis, documentation, or structured results. Input support includes text, images, and video, while responses are generated as text. This multimodal capability allows the model to reason about screenshots, interface designs, diagrams, visual documentation, and video alongside written instructions. Ox Alpha supports native tool calling, which enables AI agents to interact with external functions and systems while completing multi-step workflows. Structured output capabilities can also produce schema-based JSON that applications can process programmatically. The model is particularly geared toward long-horizon software engineering work, including debugging, code review, refactoring, system design, and reasoning across large repositories. Although speculation continues about which AI company developed Ox Alpha, no creator has been publicly confirmed, making its performance and unusual technical specifications the primary basis for evaluating the model today.

What is Muse Spark 1.2?

Muse Spark 1.2 is a coding-focused AI model from Meta designed to support advanced software engineering tasks through Muse Code and the Meta Model API. The model builds on Muse Spark 1.1 with improvements in code generation, complex debugging, codebase understanding, and end-to-end developer workflows. Muse Spark 1.2 powers Muse Code, a terminal coding agent that can plan repository changes, write code, validate outputs, and work across large codebases. Muse Code uses persistent async background agents that stay active throughout a session to reduce redundant information gathering and support difficult multi-step work. The runtime uses a local event log where model calls, tool runs, approvals, and edits are appended, making sessions replay-exact and restart-safe. Muse Spark 1.2 was co-trained with Muse Code so the model can take advantage of its toolset, harness workflows, goals, compaction, and subagent architecture. Meta significantly scaled training compute on coding tasks and expanded training environment diversity to improve the model’s engineering capabilities. The model was also trained on long-horizon coding tasks, including whole-repository generation, large end-to-end projects, auto-research, and extended iterative work. Its training approach uses planning, goal conditioning, context compaction, rejection-sampled harness trajectories, and self-improvement data generated with Muse Spark 1.1. Meta also tested Muse Spark 1.2 on long-running GPU kernel optimization workflows where the model wrote, compiled, profiled, and improved Triton kernels over many tool calls. By combining coding-focused training, agentic runtime integration, persistent subagents, long-horizon reasoning, replay-safe execution, and API availability, Muse Spark 1.2 helps developers and AI agents complete complex software engineering work with less intervention.

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Integrations Supported

Cheaper Inference
Claude Code
Hermes Agent
OpenClaw
.NET
C++
Continue
DeepSeek Harness
Kubernetes
Lua
OpenAI Agents SDK
OpenRouter
PHP
Pi Agent
Ruby
SQL
Solidity
Swift
TypeScript
omp

Integrations Supported

Cheaper Inference
Claude Code
Hermes Agent
OpenClaw
.NET
C++
Continue
DeepSeek Harness
Kubernetes
Lua
OpenAI Agents SDK
OpenRouter
PHP
Pi Agent
Ruby
SQL
Solidity
Swift
TypeScript
omp

API Availability

Has API

API Availability

Has API

Pricing Information

$0 per 1M tokens
Free Version
Free Trial Offered?

Pricing Information

$1.25 per 1M tokens (input)
$1.25 per million tokens in input, and $4.25 per million tokens of output
Free Version
Free Trial Offered?

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Ox Alpha

Company Website

openrouter.ai/compare/stealth/ox-alpha

Company Facts

Organization Name

Meta

Date Founded

2004

Company Location

United States

Company Website

meta.ai

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