Space Bunny Alpha: How to Evaluate an Anonymous Model

Space Bunny Alpha appeared on OpenRouter in late September 2026 under a stealth/ prefix, with no named provider, no model card, no licence and no published benchmarks. The listing described fast inference, strong coding, native multimodal input, adjustable reasoning effort and a 1M-token window, at a price of zero.

On 19 October 2026 OpenRouter updated the page to say the model was developed and operated by MiniMax and has been revealed as MiniMax M3.1 Flash. At the time of writing the stealth listing showed no active endpoints. The model is now a footnote, but the way to handle it is not: the next anonymous model will raise the same question, which is how to get value from it without building something that breaks the day it disappears.

What the listing said

Everything below comes from the OpenRouter catalogue listing while the model was in its stealth preview. During that preview none of it was confirmed by a vendor; the listing said only that an anonymous third-party provider operated the model and that OpenRouter routed requests to it.

• Model id: stealth/space-bunny-alpha, listed from 23 September 2026.
• Context window: 1,000,000 tokens.
• Input: text, images and video. Output: text.
• Capabilities listed: tool calling, JSON mode, a reasoning mode and reasoning effort control.
• Price: $0 for input and output.

What was not known mattered more: parameter count, architecture, training cutoff, licence, and whether the model behind the name would stay the same from one week to the next. An alpha label is a warning that it may not.

Why anonymous models exist

Labs release unnamed models to collect real-world feedback before launch. A name brings expectations and press coverage; an anonymous endpoint gets judged on its output. The lab gets traffic from developers with genuine workloads, which shows it how the model behaves outside its own test sets.

The trade for you is straightforward. You get early, free access to an unreleased model. In return, your traffic tells the lab how it performs. For Space Bunny Alpha, OpenRouter's page said prompts and completions may be retained by the provider but are not used for training, with all other use governed by its stealth model terms. Read those terms before sending anything, and do not send customer data, credentials, unreleased source you care about, or anything under a confidentiality agreement.

Free is not the same as cheap to depend on

A price of zero removes the token bill and replaces it with three other costs.

Disappearance. Stealth endpoints are temporary by design. Space Bunny Alpha is the example: once the vendor was named, the stealth listing stopped serving, and anything hard-wired to its id had to move. The named model that replaces a stealth one may be priced, rate-limited or behave differently.

Drift. An alpha can be swapped for a newer checkpoint without notice. A prompt tuned on Tuesday may behave differently on Friday, and there is no version string to tell you why.

Rate limits. Free endpoints are throttled. Fine for exploration, not for a production queue. Free tier strategies covers how to get useful answers out of free access without letting it carry load.

The rule that follows: use an anonymous model for evaluation and side projects, and keep anything that matters on a named model with a published deprecation policy. If you do put it in a pipeline, put it behind a fallback, as fallback model selection describes, so that the day it vanishes is a log line rather than an outage.

How to test it in an afternoon

When there are no published benchmarks, your own tests are the only evidence available. The Space Bunny listing made four claims, and the same checks apply to the next stealth model and to the named release that follows it.

1. Coding. Take five tasks from your own repository with known correct solutions. Run them against the new model and against the model you use today, with the same prompt and tools. Count passes and turns. Building a 20-task eval covers how to make this repeatable.
2. Speed. Measure time to first token and tokens per second over twenty requests at different times of day. Free endpoints vary with load, so one fast response tells you little.
3. Long context. A 1M window on a listing is a ceiling, not a guarantee of useful recall. Plant a specific fact deep inside a long document and ask a question that needs it. Then ask one that needs two facts from opposite ends.
4. Multimodal input. Send a screenshot of a real UI bug and ask for the fix. Then send a short video clip and ask what happened in a specific second. Video input is listed; whether it is useful is something only a test will show.

Save every prompt and response with a timestamp. If behaviour changes mid-week, that record is the only way you will notice.

Reasoning effort is worth testing separately

The listing included reasoning effort control, as does the named MiniMax listing. Run your coding tasks at the lowest and highest setting and compare both quality and latency. A model marketed on speed may be fast only at low effort, and the high setting may close much of the quality gap with slower models. Knowing where that curve bends tells you more about the model than any single score would.

A decision rule

• Use it for exploration, personal tools, and side-by-side evaluation against your current model.
• Do not use it for sensitive data, production traffic without a fallback, or anything whose prompts you have spent time tuning.
• Watch for the named launch. For Space Bunny Alpha that is MiniMax M3.1 Flash; if your tests showed the stealth version was strong, rerun the same saved prompts against the named listing before you plan around it, since nothing guarantees the launch checkpoint is identical.