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Ox Alpha: what is known, what is guessed, and how to try it free

On August 20, 2026, a model with no company name attached appeared on OpenRouter as stealth/ox-alpha and inside OpenCode, free, with a 1,048,576-token context window — and three days later nobody has claimed it. The listing calls it a reasoning model for coding and sustained agentic work; it takes text, images, and video. Community forensics — a leaked Java stack trace, an operator-specific error code, 30/30 tokenizer probe matches, identical video-encoder token accounting — point at Zhipu AI (Z.ai) and an unreleased next-gen GLM with high confidence; a Manifold market prices the Zhipu theory at 84%. That is still inference, not confirmation. Here is the line between what is known and what is guessed, and how to try it before the free window closes.

What actually appeared on August 20

The OpenRouter listing is the only primary document, so start there. Its self-description: "Ox Alpha is a reasoning model designed for coding, sustained agentic work, and production workloads. It is suited for long-horizon software engineering, complex reasoning, and workflows that combine text with visual context." The provider is listed simply as "Stealth" — a third party that, in OpenRouter's words, "has chosen to remain anonymous during this preview." OpenRouter routes to it and is explicit that it is not the developer.

OpenCode announced the same model the same day: "free for the next week — 1M Context — Multi-modal — Zero Data Retention. Generous rate limits, near unlimited usage. We have capacity for 100T tokens per day." Take the capacity claim as marketing until someone stress-tests it, and note the retention wording — we will come back to it. The week framing puts the expected end of the free window around August 27. Demand is real: Stripe CEO Patrick Collison tried it and called it "very impressive," and as I write this the OpenRouter listing shows a P50 throughput of 23 tokens per second at 5.4 s P50 latency — free-tier congestion numbers, and they move around, so read them off the page rather than off this post.

The case for Zhipu, ranked by strength

None of what follows is confirmation. All of it is community forensics, mostly published August 21–22 by researchers Chetaslua and @aitrackerbot and written up in detail by explainx.ai. It is unusually good forensics, which is why the attribution market moved.

• The stack trace. A malformed request made the upstream server leak its own Java domain model: com.wd.paas.api.domain.v4.chat.ChatCompletionRequest. That class path maps directly onto Zhipu's documented public API route, /api/paas/v4/chat/completions. This is the strongest single artifact — servers do not borrow each other's internal class names.
• Error code 1214. Feed it malformed role data and it returns {"code":"1214","message":"Incorrect role information"} — the same error Z.AI-hosted GLM models return. The control test matters: the same GLM weights served by DeepInfra produce generic validation errors instead, so the signature identifies the operator's infrastructure, not merely the weights. Someone is serving Ox Alpha on Z.AI's stack.
• The tokenizer. Token-count probes across 14 writing systems, emoji, code, and SQL matched GLM-5.3 on 30 of 30 tests. The probes also found a consistent hidden wrapper of about 75 tokens on every request — a fixed system-prompt signature.
• The video encoder. Across four test videos, token spend was reported token-for-token identical to GLM-5V-Turbo, matching three independent design choices: FPS-invariant frame sampling, duration scaling at roughly 147 tokens per second, and per-frame resolution scaling. One match is a coincidence; three engineering decisions lining up is not, the argument goes.
• Eliminations. Ox Alpha rejects audio input outright, which rules out Xiaomi's MiMo v2.5 (which accepts and tokenizes audio); other candidates fail the tokenizer test.

The minority theory is Microsoft's MAI family, based on a competing reading of the tokenizer, and early guessing sprayed across half the industry before the fingerprints narrowed it. The Manifold market "Who is behind Ox Alpha?" currently sits at 84% for Z.ai/Zhipu across 118 holders and some 26,000 trades, with a sibling market on when any company will claim credit. Worth the reminder AI analyst Andrew Curran offered as the weekend arrived: people were "less sure of anything" than the confidence scores suggest. Zhipu has said nothing on the record. If the pattern from this year's Chinese coding-model wave holds, the reveal comes with the paid launch.

The benchmark numbers do not reconcile

Treat every Ox Alpha benchmark score as vibes, because the community runs contradict each other. One developer's DeepSWE run reported 80%, ahead of frontier closed models; a full 113-task DeepSWE run elsewhere reported 63%; another run of the same family landed at 58%. A "Kingbench" score of 87.5% is circulating with no methodology attached. None of these are on a formal public leaderboard, none are independently validated, and — the objection raised in the community threads — nobody outside the anonymous lab knows what inference budget or scaffolding the free endpoint is running. Techstrong's framing is the honest one: enough verified capability on paper to deserve a serious trial, not enough public evidence to deserve trust by default.

The practical move during a free window is to skip the leaderboard argument entirely and benchmark it on your own codebase — same task, your repo, against the model you currently pay for. A week of free tokens is exactly the budget that comparison needs.

How to try it today

Two routes, both free during the stealth window. On OpenRouter, the model ID is stealth/ox-alpha — usable from the playground or any OpenAI-compatible client:

curl https://openrouter.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "stealth/ox-alpha",
       "messages": [{"role": "user", "content": "Refactor this module."}]}'

Inside OpenCode, it ships through Zen, the curated model gateway: run /connect in the TUI, select OpenCode Zen, paste your API key, then /models and pick Ox Alpha Free — listed at $0 for input, output, and cached reads. If OpenCode is new to you, start with what OpenCode is and the five-minute install. And because it is just another entry in the model list, you can switch to it mid-session, poke it on a real task, and switch back the moment it disappoints — no commitment, no new tooling.

Read the data policy before you paste your codebase

Here is the discrepancy that matters more than the attribution game. OpenRouter's listing states: prompts and completions "are retained by the provider and are not used for training; all other use is governed by the Stealth Model Terms." OpenCode's announcement said "Zero Data Retention." Those two statements cannot both be literally true, and the party they describe is anonymous — there is no named counterparty to hold to either one. Retention-for-the-provider is the norm for stealth listings: feedback on real traffic is the entire reason labs run these previews. The Next Web adds the regulatory angle — an unnamed processor is not something an EU team can square with GDPR, and the AI Act's transparency provisions expect a named party.

The operating assumption follows: everything you send is being read by an unidentified lab, probably in a jurisdiction you cannot verify. That is a fine trade for evaluating capability on open-source code or throwaway tasks. It is the wrong trade for proprietary code, credentials, or anything under NDA — for that, wait for the named release with published terms.

The stealth-launch playbook, third iteration

This is now a standard release motion, and the precedents tell you how it ends. In April 2024, "gpt2-chatbot" appeared unannounced on the LMSYS arena, dominated vibes for a week, and turned out to be OpenAI A/B-testing GPT-4o. In February 2026, "Pony Alpha" appeared on OpenRouter — free, no attribution — topped the popularity rankings, and five days later Zhipu confirmed it was GLM-5, a 744B coding and agentic foundation model, revealed simultaneously with the paid launch. Ox Alpha is running the same play, plausibly by the same lab: free frontier-scale inference in exchange for a week of real-world traffic, a hype cycle that no ad budget could buy, and a reveal timed to a product launch. OpenCode Zen is even listing a second stealth model, "Big Pickle," in the same free tier — the pipeline is becoming a genre.

For working developers the takeaway is structural: the model layer now churns on a weekly cadence, and the labs themselves have decided that anonymous drops into existing tools are the best way to ship. The rational response is not loyalty to any model — it is tooling that treats models as swappable, and the habit of running new ones against your own work. Running several models as a council is the logical endpoint of that habit; a free week of a possible GLM-5.x is a cheap seat at the table.

Frequently asked questions

Where Backgrind fits

Backgrind is not a model and not an agent. It is a desktop overlay for macOS and Windows that PTY-wraps the CLI you already run — Claude Code, Cursor, Codex, or OpenCode — and floats it above your other windows, including games running in borderless fullscreen. It pings you when the agent needs a decision or finishes, so a long Ox Alpha run does not need watching.

Weeks like this one are why the overlay is deliberately model-agnostic. When a mystery model drops into OpenRouter and OpenCode on a Thursday, trying it is a config change in the CLI you already have — /models, pick, go — not a new app, and Backgrind never sees that layer. It renders and forwards whatever your CLI does, whether that CLI is pointed at a frontier lab, an aggregator, or an anonymous stealth endpoint. In BYO-CLI mode your agent's content never touches our servers — which, in a week about who retains your prompts, is the point. And when you want to know whether the mystery model actually beats your daily driver, the Fusion council runs your own CLIs on one hard question in parallel and judges the answers.

Sources

Specs, self-description, pricing, throughput, and the retention wording read directly from the listing: openrouter.ai/stealth/ox-alpha and the Stealth provider page. Launch announcement, free-week framing, and the 100T-tokens-per-day claim: OpenCode's announcement post; access steps and the Zen free-model list: OpenCode Zen docs. The forensic evidence chain — stack trace, error code 1214, tokenizer probes, video-encoder accounting, eliminations: explainx.ai's evidence write-up and its launch coverage. Collison and Curran quotes, the Microsoft MAI theory, and the GDPR angle: The Next Web. Benchmark caution and the trial-not-trust framing: Techstrong.ai; the contradictory community scores: daily.dev. Attribution odds: the Manifold market and the claim-credit market. Launch virality: Wccftech. Precedents: OpenRouter's Pony Alpha = GLM-5 reveal and Simon Willison's gpt2-chatbot notes. All attribution evidence above is community-run forensics; as of publication no lab has claimed the model.