Reviewing the 27-Day Anonymous AI Model Public Test and Migration Guidelines

I. The 27-Day Anonymous Public Test: Timeline and Reveal

In late September 2026, an open model platform quietly launched an anonymous AI model codenamed “Hidden Rabbit.” Without published technical documentation or parameter scale, its API status was simply marked as “stealth.” However, due to its fast response and stable quality, it quickly gained traction in the developer community. Key milestones include:

• September 24: The model first appeared on the platform list, supporting text generation and code completion. Priced about 30% lower than mainstream models on the same platform, it spread quickly by word of mouth.
• September 27: Just three days post-launch, it topped the daily Token consumption charts on the platform and third-party open-source programming tools, exceeding 10 trillion Tokens in a single day.
• October 5: Speculation about its identity emerged. Due to the “rabbit” imagery and an approaching autumn harvest festival, many linked it to a research institute with a lunar theme.
• October 15: Cumulative processed Tokens surpassed 180 trillion, drawing media coverage.
• October 19: The platform officially claimed the model and released the new Flash-level production version, ending the anonymous phase.

The official announcement confirmed three points: the anonymous model was an early iteration of the production version; the anonymous phase processed over 200 trillion Tokens, acting as a massive real-world stress test; and the production version achieved generational improvements in inference speed, cost efficiency, and multitasking. However, specific performance metrics and negative cases were not disclosed, sparking debate over whether high Token consumption equates to high-quality testing.

Evaluation Scope Statement: This assessment is based on public documentation and limited hands-on testing through late October 2026. Inference speed and cost data are estimated using a local Region, an input/output ratio of roughly 3:1, during off-peak hours. Conclusions may change with version iterations; refer to the latest official information.

II. Why the Community Misjudged the Model’s Identity

During the anonymous phase, mainstream guesses pointed toward a well-known lunar-named research institute. Reasons included the lab’s visual model prowess in generating rabbit imagery and the product’s celestial naming conventions. However, a post-mortem revealed clear misjudgments.

Three Major Misjudgment Factors

1. Over-interpretation of visual associations: “Rabbit + Moon” was over-analyzed as an astronomical theme. In reality, model codenames are often临时 creative decisions by product managers without fixed logic.
2. Timing coincidence: The launch overlapped with an autumn holiday, reinforcing the “lunar rabbit” association. In hindsight, it was more likely timed to avoid holiday traffic peaks and facilitate low-profile testing.
3. Echo chamber effect: Discussions focused heavily on a few famous labs, ignoring the possibility of mid-sized tech teams releasing highly competitive anonymous models.

Missed Correct Clues

• Tested API response latency was around 250 milliseconds, matching the edge optimization characteristics of a certain vendor’s previous-generation Flash series.
• Code generation comments were concise and practical, lacking academic phrasing.
• The roughly 70% discounted pricing during the anonymous phase aligned perfectly with that vendor’s “extreme cost-effectiveness” positioning.

These clues were too subtle and lacked milestone evidence. This reminds us that in deducing anonymous models, fine-grained analysis of performance metrics is often more valuable than brand imagery associations.

III. Production Version Capability Assessment: Speed, Multitasking, and Compliance

As a new-generation Flash-level model, the production version features multi-dimensional upgrades. Each subsection below provides a conclusion first for quick assessment, with data parameters and limitations noted.

Inference Speed and Cost Efficiency

Official claims state online inference speed is 3x faster than the previous generation, with costs reduced by 70%. Our estimates are based on a local Region, an input/output ratio of about 3:1, and off-peak testing in late October 2026:

• With a 10K Token prompt, the streaming Time to First Token (TTFT) dropped from ~1.2s to ~400ms (Note: single test measurement; peak concurrency not covered).
• Under the same benchmark, inference cost per Token dropped from ~2.2 CNY/million Tokens to ~0.7 CNY/million Tokens (estimated; refer to the official pricing page).

For a mid-sized team consuming 10 million Tokens daily, monthly API expenses could be reduced by roughly 15,000 to 30,000 CNY. Suitable scenarios include high-frequency text classification, batch processing, and AI-assisted coding plugins. However, peak concurrency limits may cause queue wait times, requiring robust traffic planning.

Agent Tasks

The model excels in tool calling, multi-step planning, and error recovery. In a simulated booking scenario, it autonomously completed the closed loop of “search - compare - order - cancel abnormal order.” The accuracy rate for tool calling parameter generation increased from 82% to 94%—this data is cited from an official internal evaluation report; the test set scale and conditions are undisclosed and cannot be independently verified. Treat as an estimated reference.

Suitable scenarios: automated process orchestration and customer service ticket handling. Use cautiously in contexts sensitive to multi-turn context loss.

Coding Assistance

In common code completion benchmarks, the executable rate of generated code was approximately 78%, up 12 percentage points from the previous generation (benchmarks and measurement conditions are undisclosed; for reference only). It offers fluent support for JavaScript, Python, and TypeScript, while Rust and Go support needs refinement. It performs better at explaining existing code logic and generating unit tests than enforcing unified code styles.

Suitable scenarios: daily coding assistance, code reviews, and test case generation. Not recommended for direct production-grade code deployment.

Computer Use

The model can understand screen screenshots visually and output mouse/keyboard instructions. Tested with production version v1.0 and 1080p screenshots, it achieved a 65% success rate in “extracting PDF tables to Excel,” taking twice as long as manual effort. Failures primarily involved cross-page table merging and special font recognition. Note: This automated operation involves uploading screenshots; ensure your enterprise data security policy permits sending internal document screenshots to a third-party API.

Suitable scenarios: specific format conversion assistance and repetitive desktop operations. Not suitable for completely replacing human labor, and carries data security compliance risks.

Cybersecurity

The model performs well in parsing vulnerability reports and identifying log anomalies, but assisting with penetration testing scripts remains experimental. Explicit Warning: Do not use generated scripts against unauthorized targets; violators may face criminal liability. Recommend strictly for educational research and compliant security drills.

Context Window and File Processing

The production version supports a 128K context window (approx. 100,000 words) and allows simultaneous upload of multiple PDFs, Word, and Excel files for joint analysis. In a mixed task involving a 50-page tech document, 200 lines of code, and a data table, the key point recall rate was about 85%, though it occasionally missed secondary priority constraints.

IV. Pricing Comparison and Tiered Migration Guidelines

The production version uses pay-as-you-go billing. A comparison with similar mainstream lightweight models follows:

Comparison Dimension	Production Version (Flash-level)	Mainstream Model A	Mainstream Model B	Data Source / Estimate Basis
Input Price	~0.7 CNY/Million Tokens	~1.0 CNY/Million Tokens	~0.9 CNY/Million Tokens	Oct 2026 local Region public pricing estimates
Output Price	~2.0 CNY/Million Tokens	~2.5 CNY/Million Tokens	~2.2 CNY/Million Tokens	Same as above
Context Window	128K	128K	64K	Official documentation
Concurrency Limit	Improved but restricted	High	Medium	Official documentation & testing

High concurrency API rate limits may trigger retries, incurring extra costs. Set cost alert thresholds and conduct small-traffic A/B tests before migrating.

Tiered Migration Recommendations

• Suitable for Migration: High-frequency text classification, code completion, data cleaning, FAQ retrieval, and Agent automated workflows. The cost reduction benefits far exceed migration testing overhead.
• Migrate with Caution: Tasks relying on specific modal inputs (like direct audio stream processing), strict output format controls (like full JSON Schema), or real-time interactive systems sensitive to latency over 200ms. Conduct small-traffic A/B tests and set cost alerts first.
• Not Recommended for Migration: Businesses involving sensitive data with data residency compliance requirements should observe until a privatized solution is officially released.

V. Industry Implications and Compliance Reminders

This anonymous test completed a progression from gray-release to massive real-world load validation in just 27 days. It accumulated vast real-scenario data for subsequent fine-tuning, offering a new paradigm of “low-cost anonymous public testing + rapid feedback iteration.” Using real traffic to replace manual evaluation is becoming the gold standard for assessing comprehensive model capabilities.

Compliance Reminder & Team Self-Check List: Whether the 200 trillion Tokens from the anonymous phase contained user data, and whether the platform obtained authorization, requires independent legal confirmation by each team. Follow these self-check steps:

1. Confirm if interaction data during the anonymous phase involved personal information.
2. Review data usage terms regarding anonymous testing in the platform’s user agreement.
3. Assess compliance with GDPR and relevant personal information protection laws.
4. Never use user data for model interaction testing without assessing compliance risks.

Practitioners should maintain sensitivity to new entries on open platforms and leverage anonymous phases for technical pre-research. In today’s rapidly evolving AI landscape, understanding a new model’s capability boundaries a week earlier than others can accelerate the deployment of new application scenarios.