Morning Edition · Monday, August 24, 2026Published at 3:20 AM EDT · New York
A developer's run of the DeepSWE agent benchmark put the stealth model at 80 percent against 65 percent for Claude Fable 5 and 52 percent for GPT-5.6 Sol, but no vendor has published the result or claimed the model.

A model listed only as Ox Alpha appeared on OpenRouter's stealth channel and in OpenCode on 20 August, offered free, with a context window of 1,048,576 tokens, text, image and video input, and a maximum output of roughly 128,000 tokens. The provider is undisclosed. Coverage of the listing notes only that a third party operates it and has chosen to stay anonymous during the preview.
The attention comes from a single independent run. The developer Ben Davis put Ox Alpha through DeepSWE, a coding-agent benchmark, and reported 80 percent against 65 percent for Claude Fable 5 and 52 percent for GPT-5.6 Sol. That is one test setup, one configuration, one runner, and neither OpenRouter nor the anonymous provider has published a benchmark card. Treat the ranking as something that needs independent reproduction, not as a settled result.
Community tokeniser probes have pointed toward Z.ai's GLM family, which released GLM-5.2 Turbo on 17 August, but nobody has confirmed the model's origin. The stealth-listing pattern itself is now routine: a lab ships an unbranded endpoint, collects real agentic traffic for free for about a week, then attaches a name and a price once it knows how the model behaves under load.
For engineers, the practical facts do not depend on who built the model. A million-token window with video input, free during preview, is a cheap way to test long-horizon agent workloads that would otherwise be expensive to evaluate. The free window is expected to close after roughly a week, and neither pricing nor continued availability has been confirmed.
An undisclosed provider collects free real-world agent traffic and favourable coverage before it sets a price, and OpenRouter strengthens its position as the channel where a new model's reputation is formed before launch.
Part of a tracked trend
Frontier Labs Race on AI Coding Capability
Coding is becoming a primary competitive battleground among frontier labs, with incumbents standing up permanent coding teams and investing in new training stages (e.g. midtraining) to match leaders like Anthropic; expect recurring reorganizations, benchmarks, and model releases aimed specifically at code.
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The 80 percent came from a ten-task subset of DeepSWE, and the same developer's full 113-task run two days later returned roughly 63 percent, which places the model below Claude Fable 5 rather than ahead of it, while attribution rests on tokeniser fingerprints matching Zhipu's GLM family and no confirmation from any lab.
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What this means
Stealth preview listings are becoming the standard distribution channel for pre-launch frontier coding models, and the party that gains is the router: OpenRouter and similar aggregators now sit between labs and the evaluation traffic that determines how a new model's launch is perceived. Anthropic and OpenAI are exposed through a specific channel: a free anonymous endpoint that beats their flagship models in even one credible independent run changes the terms of the price comparison before they can respond with their own numbers. The decisive question is whether an evaluation using multiple independent test setups confirms the 80 percent DeepSWE figure, or whether the score falls back toward other models' results once other testers try it, as usually happens with single-run leaderboard jumps.
What to watch
Observations to monitor, not financial advice.
Synthesized from: Polylog editors · OpenRouter · explainx.ai · Local AI Zone
Comments
1Aug 25, 5:00 AM · edited
OpenRouter routes stealth model traffic through its API and has a billing relationship with the upstream provider, so the vendor identity is known to OpenRouter even if not published to users.