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Analyzing "Space Bunny": Evaluating Inference Latency and Reasoning Depth Across Variable Effort Levels in Stealth LLMs

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Analyzing "Space Bunny": Evaluating Inference Latency and Reasoning Depth Across Variable Effort Levels in Stealth LLMs

The landscape of Large Language Models (LLMs) is currently undergoing a period of intense volatility, characterized by the emergence of "stealth" models—unannounced releases that appear on platforms like OpenRouter and OpenCode without formal documentation. The most recent subject of interest is "Space Bunny," a model currently available for a limited trial period. Unlike many previous stealth deployments where user data was utilized for post-training refinement, early indicators from OpenRouter suggest that the provider for Space Bunny is not training on prompts or completions during this window, offering a rare opportunity for privacy-centric benchmarking of unknown architectures.

The Methodology: Edge-Case Evaluation and Effort Scaling

To evaluate the true utility of Space Bunny, a rigorous benchmarking suite was deployed across four distinct software projects involving PHP, Dart (Flutter), and Go. Rather than focusing on "happy path" scenarios—where code is expected to work under ideal conditions—the evaluation focused heavily on edge cases and non-standard execution paths.

A critical component of this benchmark is the implementation of "Effort Levels" within the OpenCode environment. This parameter allows users to scale the computational budget allocated to a single prompt, effectively modulating the model's reasoning depth (or "thinking" duration). The testing was conducted using an OpenCode TPS (Tokens Per Second) meter plugin (Version 1) to monitor real-time throughput and token consumption.

Performance Analysis: The Latency vs. Accuracy Trade-off

The experimental results reveal a stark divergence in performance metrics when transitioning between Medium, High, and Max effort levels.

1. Maximum Effort Level: High Precision, Extreme Latency

At the "Max" or "Default" level, Space Bunny demonstrates significant reasoning capabilities but suffers from severe inference latency. In several test iterations, the model entered extended "thinking" phases, with durations exceeding five minutes per thought block.

Key Metrics at Max Level:

  • Accuracy: The model achieved a highly impressive score of 18.5/20 on edge-case testing for specific projects (including React, TypeScript, and PHP/Laravel backends). This places its quality in the same tier as high-end models like GPT-4o or even speculative future iterations mentioned in recent benchmarks.
  • Throughput: Token throughput plummeted to approximately 0.4 TPS during intensive reasoning phases.
  • Latency: Total execution time for a single prompt ranged from 9 minutes to over an hour, with some prompts failing to complete within the 30-minute timeout window.
  • Token Consumption: Significant increases in context usage were observed, with token counts rising from approximately 35k at medium levels to over 60k tokens during max-level reasoning.

2. Medium Effort Level: High Throughput, Reduced Robustness

When the effort level was scaled back to "Medium," the model's performance profile shifted toward that of a high-speed, "flash" class model. This tier is characterized by rapid response times and higher tokens per second, but at the cost of edge-case reliability.

Key Metrics at Medium Level:

  • Latency: Prompt execution was significantly optimized, with completion times dropping to between 80 and 100 seconds.
  • Throughput: The model maintained a much higher TPS, making it viable for real-time coding assistance.
  • Accuracy Degradation: While the speed was "blazingly fast," the model began failing tests involving complex validation logic (e.g., API validation from multi-device sources and various edge-case scenarios).

The performance of Space Bunny at this medium tier appears comparable to other high-efficiency models such as Gemini Flash, DeepSeek Flash, or GLM 5.3 Flash. This suggests that the model's architecture is highly scalable, capable of acting as either a lightweight, rapid-response agent or a heavy-duty reasoning engine depending on the allocated compute budget.

Speculative Identification: The Minimax Hypothesis

The identity of "Space Bunny" remains unconfirmed, but technical indicators point toward a potential iteration from the Minimax team. Observations regarding pricing structures and the current trajectory of model quality suggest that Space Bunny may be an upgraded version of the Minimax M3 architecture (potentially M3.1).

While the existing Minimax M3 has been noted as "average" in recent leaderboards, the performance jump observed in the Space Bunny trial—specifically regarding its ability to handle complex PHP and Go logic at high effort levels—suggests a significant leap in post-training or architectural refinement. If the pricing model aligns with previous Minimax releases, this model could become a dominant force in the "flash" category if it maintains its medium-level speed while closing the accuracy gap found in its higher-effort tiers.

Conclusion for Developers

For developers utilizing OpenCode and OpenRouter, the takeaway from the Space Bunny trial is clear: Effort level selection is as critical as model selection.

If your objective is rapid prototyping or simple unit testing, the Medium effort level provides a high TPS that rivals Gemini Flash. However, if you are debugging complex architectural edge cases in Go or PHP, the Max level—despite its 0.4 TPS bottleneck and risk of timeout—is necessary to leverage the model's superior reasoning capabilities. As the trial period concludes, monitoring whether this model transitions into a permanent, priced tier will be essential for optimizing CI/CD pipelines and automated testing workflows.