Capital & Compute

What Is Ox Alpha? The Free Stealth AI Model, Explained

Ox Alpha is a free anonymous reasoning model on OpenRouter. Specs, the GLM-5.3 fingerprint case, benchmark rumors, and who pays for 100T tokens a day.

· ai· coding· pricing· By Capital & Compute

Ox Alpha is an anonymous AI model that appeared on the OpenRouter API marketplace on August 20, 2026, priced at zero, backed by more than a million tokens of context, and offered by a provider that refuses to say who it is. In its first days it absorbed billions of tokens of real coding-agent traffic, drew a public test from Stripe CEO Patrick Collison, and became the subject of a crowdsourced forensic effort that points, with real but unconfirmed confidence, at one specific Chinese lab. This piece covers what is confirmed, what the fingerprinting shows, how good the model appears to be, and the economics question nobody can answer yet: who pays for 100 trillion free tokens a day?

$0
Price per Mtok, in and out
Free during the preview window
1M
Context window
1,048,576 tokens listed on OpenRouter
131K
Maximum output tokens
Text, image, and video input
100T
Claimed capacity, tokens/day
Provider's own servicing claim

What is Ox Alpha?

Ox Alpha is what the industry calls a stealth model: a frontier-class system served through an API platform under an anonymous brand so the lab behind it can collect real-world traffic, feedback, and load data before putting its name on an official launch. The OpenRouter listing describes it as “a reasoning model designed for coding, sustained agentic work, and production workloads,” suited to long-horizon software engineering and workflows that mix text with visual context. OpenRouter states plainly that it only routes requests and is not the developer, owner, or operator.

The release was coordinated with OpenCode, the open-source coding agent, which announced the model would be free for about a week with near-unlimited usage, and that its provider had servicing capacity for 100 trillion tokens per day. For scale, Business Insider noted on August 22 that this is roughly 100 times the number of AI tokens Visa said it burns in an entire month. Stripe CEO Patrick Collison tried the model and posted that “it’s very impressive.”

This is also not a one-off. Ox Alpha is the fifth anonymous release on OpenRouter in roughly six months, and, as the tracker blog Coursiv documented, all four predecessors eventually turned out to come from Chinese labs.

  1. February 2026

    Pony Alpha turns out to be GLM-5

    An anonymous listing that Zhipu AI (now Z.ai) claimed about five days after launch, establishing the stealth-release playbook the current wave follows.

  2. Spring 2026

    Hunter Alpha becomes MiMo-V2-Pro

    Confirmed by Xiaomi at launch after weeks of anonymous preview traffic on OpenRouter.

  3. Mid 2026

    Owl becomes LongCat-2.0; Ling-2.6-flash surfaces

    Meituan's LongCat-2.0 and Ant Group's Ling-2.6-flash complete the pattern: four anonymous drops, four Chinese labs.

  4. August 20, 2026

    Ox Alpha appears, unclaimed

    Free, 1M context, multimodal, 100T tokens/day of claimed capacity. No lab has put its name on it as of August 23.

Confirmed specs, and what stays unconfirmed

Spec Value
Model ID stealth/ox-alpha (OpenRouter), opencode/x-preview-f-free (OpenCode Zen)
Context window 1,048,576 tokens (1M)
Maximum output 131,072 tokens
Input Text, images, video
Output Text only
Tool calling / structured output Yes; JSON response_format, no schema enforcement
Reasoning variants low, high, max
Price during preview $0 input, $0 output, $0 cache read

These figures come from OpenCode’s model catalog and the OpenRouter model page, cross-checked by the review site Developers Digest against the live endpoint on August 21. Just as important is what no one has published: parameter count, architecture, training details, formal benchmarks from a named lab, or post-preview pricing. OpenCode’s first announcement pointed to “the next week” starting August 20, so the sensible planning assumption, per Developers Digest, is that the free window closes around August 27 unless extended.

At listing time the single Stealth provider showed median latency around four seconds and throughput in the mid-20s of tokens per second with better than 99 percent uptime, though those live numbers drift hour to hour. Early traffic was heavy anyway: OpenRouter’s apps tab already showed tens of billions of prompt tokens flowing through coding agents within the first two days, led by Claude Code at 36.2B tokens and Hermes Agent at 28.8B, according to a snapshot published by MoClaw.

Who built Ox Alpha? The fingerprint case

The identity question has produced one leading theory, one serious counter-theory, and a lot of noise. The table sums up where things stand as of August 23:

Theory Evidence cited Status
Z.ai (Zhipu AI), an unreleased multimodal GLM-5.3 variant Tokenizer counts match GLM-5.3 exactly across 25 prompts (constant +75-token offset); 6 of 9 probes on the community modelprint tool matched; similar video-encoder behavior and error codes Leading inference; unconfirmed
Xiaomi MiMo V3 Ran the stealth playbook before (Hunter Alpha); capacity claim resembles earlier MiMo promotions Floated, largely discounted on tokenizer evidence
Microsoft MAI / Phi lineage One analysis argues the tokenizer is cl100k_base, which would match Microsoft’s models and rule out Chinese labs Contested; conflicts with the GLM matches
A router over several models Would explain inconsistent behavior across prompts and censorship canaries Speculative

The strongest single piece of evidence is tokenizer forensics. Independent researcher Ben Davis ran 25 varied test prompts and found Ox Alpha’s raw token counts matched Z.ai’s GLM-5.3 exactly each time, apart from a fixed +75-token wrapper he attributed to hidden system prompting, a result reported by the fingerprinting writeup at OrcaRouter and by Local AI Zone, which put Davis at “99 percent certain” of GLM-5.x lineage. Tokenizer matching is hard to fake because a model cannot change how it segments text without retraining.

There is one honest wrinkle: GLM-5.3, released August 14 as a text-only model, has no public vision or video endpoints, while Ox Alpha advertises image and video input. If the theory holds, this is an unreleased multimodal upgrade of the GLM-5.3 line rather than the shipping model. That reading fits Z.ai’s stated push toward multimodality and its precedent of testing under a codename before claiming.

The counter-evidence exists too. Wccftech highlighted a competing analysis arguing that stealth/ox-alpha tokenizes like a cl100k_base model, which would point to Microsoft’s Phi/MAI family and rule out every Chinese frontier lab. And the analyst Andrew Curran captured the mood in a post Business Insider quoted on August 22: GLM was the leading theory Friday night, but “by Saturday morning, people seem less sure of anything.”

The fair summary: strong circumstantial evidence for Z.ai, no confirmation from anyone, and a track record that makes a Chinese lab the statistical favorite. Until a company puts its name on it, attribution stays pattern-matching.

How good is it? Read the numbers with care

No official benchmark table exists, so everything here is third-party and should be weighted accordingly.

The most concrete data point is Kingbench, a third-party leaderboard, where Ox Alpha scored 87.5 percent against GLM-5.3’s 91.25 percent, per Wccftech’s August 22 report. That is consistent with the multimodal-upgrade theory: close to the GLM line it presumably descends from.

Kingbench score, percent correctGLM-5.3 scored 91.25 percent and Ox Alpha scored 87.5 percent on the Kingbench third-party leaderboard as reported by Wccftech on August 22, 2026.0%20%40%60%80%100%GLM-5.391.25%Ox Alpha87.5%
Kingbench score, percent correct
ItemValue
GLM-5.391.25%
Ox Alpha87.5%
Kingbench leaderboard scores as reported by Wccftech on August 22, 2026: GLM-5.3 at 91.25 percent and Ox Alpha at 87.5 percent. Two models had reported scores on this board at the time of writing; the gap is small enough that the ranking could move with any retest.Source: Wccftech, reporting the Kingbench community leaderboard, August 22, 2026

Beyond that, the numbers are screenshots and social posts. A popular thread on r/singularity circulated results from one agentic SWE evaluation showing ox-alpha at 80 percent against 65 percent for Claude Fable 5 and 52 percent for GPT-5.6 Sol; the same set, retested by a community member, reportedly landed “more or less on par with GPT-5.6 Sol mid.” These are single-run, unaudited figures from anonymous testers, which is exactly how benchmarks get distorted. Our earlier coverage of DeepSWE versus FrontierCode explains why harness, sampling temperature, and run count matter as much as the headline percentage on agentic sets.

What is not in dispute is adoption. Within roughly two days, real agent traffic pushed well over 100 billion prompt tokens through the endpoint, with Claude Code, Hermes Agent, and other harnesses among the heaviest users per MoClaw’s snapshot of OpenRouter’s apps tab. Developers do not burn tokens like that on a model that cannot code at all.

The catch: two data policies and an anonymous counterparty

Free frontier compute comes with a privacy split that most coverage buries. OpenCode’s Zen documentation says the provider follows a zero-retention policy and does not train on user data. The OpenRouter listing says the opposite on retention: “Prompts and completions for this model are retained by the provider and are not used for training.” Both can be true at once, because they describe different doors into the same model, but they are not the same policy. Developers Digest traced the paperwork: OpenRouter’s default Stealth Program terms, dated July 6, 2026, are a training-data license that grants the unnamed provider rights to use prompts and completions; Ox Alpha’s model page walks back the training part, and does not walk back retention.

The anonymity is not just an aesthetic problem for European businesses. As The Next Web argued on August 22, EU data protection law requires a contract with a named processor, and the AI Act’s transparency obligations took effect on August 2 with penalties reaching 15 million euros or 3 percent of global turnover. An anonymous counterparty fails both tests by construction.

Community testing has also surfaced odd behavior relevant to trust. On the Hacker News thread tracking the model (245 points as of August 23), testers reported inconsistent answers to well-known censorship probes: some sessions refused questions about Tiananmen Square while others answered them in detail, and one reported refusal in Chinese alongside candor in English. Explanations range from host-level filtering to A/B variants to the model actually being a router over several systems. None is confirmed, and each is itself a reason to be cautious about what gets sent through the pipe.

Who pays for 100 trillion tokens a day?

This is the question worth asking after the identity speculation dies down, because free at this scale is not generosity; it is a budget line.

The default Stealth Program terms give away the business model: free access in exchange for rights to train on your traffic. Ox Alpha’s listing carves training out “this time,” which tells you what earlier stealth drops were for. Even without the data, an anonymous week buys three things money struggles to buy: load-testing at genuine production scale, a firehose of real-world task feedback before launch day, and a hype cycle timed to whatever announcement follows. Z.ai claimed Pony Alpha about five days after its February debut; if history repeats, the reveal lands when the free window closes.

The capacity claim itself signals who can play this game. Serving 100 trillion tokens per day requires serious inference infrastructure, which is why speculation concentrates on large Chinese labs rather than startups. It fits the pattern we documented in why Chinese AI models are so cheap: vertically integrated labs competing aggressively on price to buy Western developer mindshare. If Ox Alpha emerges as a GLM variant with official multimodal endpoints, expect post-preview pricing aimed squarely at undercutting closed-frontier rates, continuing the dynamic described in why cheaper AI models sometimes cost you more. Whether Washington responds with access restrictions is the open question covered in will the US ban Chinese AI models.

Bottom line

Ox Alpha is real, free, unusually capable by every unofficial reading, and completely unattributed. Use it this week for experiments you would be happy to publish, treat every benchmark number as a rumor until a named lab confirms one, and watch for the claim date. If the pattern holds, the mystery ends within days of the free window closing, and the interesting question stops being who built it and becomes what it charges.

Frequently asked questions

Is Ox Alpha really free?
Yes, during the preview. OpenRouter lists it at $0 per million tokens in and out, and OpenCode offered roughly a week of near-unlimited usage starting August 20, 2026, which points to the window closing around August 27. There is no confirmed end date, so check the live listing before depending on it.
Who made Ox Alpha?
Nobody has said. The leading community inference is Z.ai (Zhipu AI), based on tokenizer forensics matching GLM-5.3, but no company has confirmed anything. All four previous anonymous OpenRouter drops came from Chinese labs.
Does Ox Alpha train on my prompts?
Not according to both platforms, though their policies differ on retention. OpenCode advertises zero data retention. OpenRouter states prompts and completions are retained by the provider but not used for training. Retention means an unidentified party holds your text either way.
Can I use Ox Alpha in production?
Technically yes through the OpenAI-compatible API with tool calling and structured output. Practically, sending customer code or regulated data to an unnamed provider is hard to square with contractual and, in the EU, legal obligations. Treat it as an evaluation sandbox until a named lab stands behind it.

Sources

Get each breakdown before it makes the rounds

You get one email when a new source-backed analysis goes live: what AI agents actually cost, which models are worth running, and what the benchmarks really mean. No hype.

No spam. Unsubscribe anytime.

← Back to Models & benchmarks