Space Bunny Alpha API
Coming SoonAnonymous stealth reasoning model on OpenRouter with 1M context, multimodal text/image/video input, strong coding, and free preview access.
Space Bunny Alpha API Background
Overview
Space Bunny Alpha is an anonymous stealth reasoning model exposed through the Space Bunny Alpha API on OpenRouter, with a parallel free preview route as space-bunny-free on OpenCode Zen. It appeared on 2026-09-23 and is positioned as a fast, multimodal model for text generation from text, image, and video inputs. The most notable published API traits are a 1,000,000-token context window, a 524,288-token maximum completion length, mandatory reasoning with adjustable effort levels, and strong coding-oriented behavior. Its developer, architecture family, parameter count, training data, and knowledge cutoff remain undisclosed, so most public analysis focuses on API behavior, benchmark performance, and identity fingerprinting rather than official model documentation.
Development History
Space Bunny Alpha entered the market as a stealth model on 2026-09-23, following the broader industry pattern of releasing anonymous preview models to gather production traffic and user feedback before formal attribution. Within days, independent benchmark sites, tokenizer fingerprint studies, and community evaluations began testing the Space Bunny Alpha API across long-context retrieval, coding, SVG generation, latency, and reasoning quality. By 2026-09-30, the public evidence suggested a stable API surface with mandatory reasoning controls, multimodal input support, and unusually strong long-context retention. At the same time, identity remained unconfirmed, with the strongest third-party hypothesis pointing to a MiniMax-family preview rather than an officially named release.
Key Innovations
- A 1,000,000-token context window with third-party evidence of successful retrieval at roughly 200,000-token depth, making the Space Bunny Alpha API unusually suitable for large repositories and long-document analysis.
- Mandatory reasoning with controllable effort levels from low to max, allowing the Space Bunny Alpha API to trade off speed and depth while still preserving a reasoning-first interaction model.
- Native multimodal input support for text, image, and video paired with strong text output quality, especially in coding and SVG-style creative generation workflows.
Space Bunny Alpha API Technical Specifications
Architecture
The underlying architecture of Space Bunny Alpha is not officially disclosed. Public API metadata identifies the tokenizer only as 'Other' and provides no architecture family, parameter count, or knowledge cutoff. Independent fingerprint research on the Space Bunny Alpha API found a tokenizer and token-delta pattern that matched MiniMax-family models with high confidence, while also noting a distinct chat template overhead and a separate service path from earlier MiniMax endpoints. These findings are informative but not official. From an API perspective, the model supports text, image, and video as inputs, returns text only, and exposes controls such as reasoning, reasoning_effort, include_reasoning, max_tokens, temperature, top_p, tools, tool_choice, and response_format.
Parameters
The number of parameters, training scale, and model size of Space Bunny Alpha have not been published. No authoritative source provides a numeric parameter estimate, and third-party benchmark sites explicitly avoid attributing one without evidence. What is known from the Space Bunny Alpha API is operational scale rather than model size: it supports a 1,000,000-token context window, a 524,288-token maximum completion length, multimodal input handling, and mandatory reasoning. For technical buyers, this means capacity and behavior are better understood than model internals. Any claims that map Space Bunny Alpha to a specific checkpoint, parameter count, or vendor should be treated as unconfirmed community inference rather than official specification.
Capabilities
- Processes text, image, and video inputs and produces text output, enabling multimodal understanding workflows through the Space Bunny Alpha API.
- Handles very long contexts, with public tests showing successful retrieval of multiple embedded code snippets from approximately 200,000-token inputs in correct order.
- Performs strongly on coding, structured extraction, and domain-specific tasks, with AI BENCHY reporting perfect scores in data extraction and tool calling and an overall score of 7.0 at the high reasoning setting.
- Supports JSON-style structured output through response_format and basic tool integration, making it usable in agent and backend automation pipelines.
Limitations
- The developer, architecture, parameter count, and knowledge cutoff are undisclosed, so governance teams using the Space Bunny Alpha API must plan around incomplete model provenance.
- Reasoning is mandatory and defaults to a very high effort level, which can increase latency, token usage, and overthinking on simple tasks; tool_choice support is also limited to auto, and JSON Schema enforcement is not documented.
Space Bunny Alpha API Performance
Strengths
- Strong long-context behavior backed by public retrieval tests, plus fast inference with reported median first-token latency near 1.07 seconds and throughput around 87 tokens per second on one OpenRouter snapshot.
- Balanced real-world utility across coding, extraction, and creative SVG generation, with AI BENCHY at 7.0 overall, GPQA Diamond at 82.0% on a 60-question subset, MMLU-Pro at 75%, and Humanity's Last Exam at 46.1% on a 300-question subset.
Real-world Effectiveness
In practice, the Space Bunny Alpha API looks most compelling as a high-capacity preview model for developers who value context length, multimodal input, and strong coding assistance over top-tier frontier benchmark leadership. Third-party testing suggests it is meaningfully better than a paper-spec long-context model: it successfully retrieved three hidden code items from a roughly 200K-token prompt and showed stable token accounting across large context sizes. It also appears token-efficient relative to some competing models in comparable benchmark runs. However, real-world evaluations note that mandatory reasoning can make the model less crisp in tight agent loops, and some users found competing models more stable for tool orchestration and multi-step planning.
Space Bunny Alpha API When to Use
Scenarios
- You have a very large codebase, legal archive, or research corpus that does not fit comfortably in conventional context windows. The Space Bunny Alpha API is a strong fit because it combines a 1,000,000-token context limit with public evidence of accurate deep retrieval in long prompts. This can reduce chunking complexity, preserve cross-document relationships, and improve repository-wide debugging, policy comparison, or transcript analysis in a single request path.
- You have a multimodal workflow where teams need one model endpoint to read text, inspect screenshots or diagrams, and reason over video input before returning written analysis. The Space Bunny Alpha API is well suited here because it accepts text, image, and video inputs without requiring separate model routing. That simplifies application design and can improve developer velocity for support copilots, QA review systems, and media-understanding assistants.
- You have an internal developer tool or agent prototype that needs strong coding help, structured outputs, and experimentation with reasoning controls. The Space Bunny Alpha API works well for this scenario because it supports JSON-oriented responses, tool integration, and adjustable reasoning effort while showing strong benchmark behavior in extraction, tool calling, and code-heavy tasks. Teams can prototype repository assistants, migration scripts, and test-generation workflows with fewer infrastructure changes.
Best Practices
- Lower reasoning effort for straightforward tasks and reserve high or max settings for difficult analysis, because mandatory reasoning in the Space Bunny Alpha API can add avoidable latency and verbosity.
- Validate all structured output externally, apply route-specific privacy review, and benchmark the Space Bunny Alpha API on your own workloads before production rollout because JSON Schema guarantees, model identity, and retention characteristics are not uniform across access paths.