MiniMax has stepped into the debate over who built Space Bunny. On October 19, 2026, its official X account posted “Welcome back. Space Bunny.” followed by “MiniMax-M3.1-Flash is coming.” The post links the codename to MiniMax’s Flash model, although it stops short of a detailed release announcement. MiniMax’s announcement
There is already more to examine than a teaser. MiniMax’s current API documentation uses MiniMax-M3.1-Flash without the Preview suffix, while its subscription documentation still lists M3.1 Flash Preview. The names differ across those pages, so developers should distinguish what the company has announced, what its documentation describes, and what their accounts can actually access.
From an anonymous listing to MiniMax’s teaser
Space Bunny Alpha appeared on OpenRouter on September 23, 2026. Its listing described an anonymous third-party model with a million-token context window, adjustable reasoning effort, and text, image, and video inputs. OpenRouter explicitly stated that it routed requests and was not the model’s developer, owner, or provider. The listing deliberately left the developer unnamed. OpenRouter’s model record
Developers tried to identify it by examining how it behaved. YFarmX reported that Space Bunny’s token counts matched those of eight MiniMax models across all 50 test strings. Its report also explained the limit of that finding: a shared tokenizer can point to a model family, but cannot identify a specific release. The measurements supported the MiniMax theory without settling which model was being served. YFarmX’s fingerprinting record
The official post adds the company’s own voice to that evidence. It does not establish that every Space Bunny session used the same model version, or that M3.1-Flash will reproduce every preview result. Anyone who saved prompts and outputs from the anonymous service has a useful starting point for a fresh comparison.
What MiniMax documents for M3.1-Flash
MiniMax’s API overview lists the model ID MiniMax-M3.1-Flash, a 1,000,000-token context window, and support for text generation and tool calls. It also describes access through HTTP requests and compatible SDKs. Developers can use that model ID to check their tool configuration, while confirming availability through their own account.
The model invocation guide specifies text, image, and video inputs. For a coding assistant, that opens up a useful combination: repository files alongside a screenshot of a broken interface or a recording of an interaction. The practical question becomes whether the model can connect visual evidence to the right change in code.
A large context window provides more room for source material; it does not guarantee that the model will use every detail correctly. Consider a test involving a slow support-ticket screen. Give the assistant steps to reproduce the problem, relevant source files, and a recording of the delay. Measure whether its patch improves response times and check that existing features still work. Explaining the likely bottleneck is only the first step.
That test also needs a sensible context budget. MiniMax’s API overview defines the token limit as input plus output combined. Leave room for the response and, over successive turns, any tool results added to the conversation. The million-token figure should not be treated as a separate allowance for input alone.
Reasoning effort changes the speed trade-off
One setting deserves particular attention: reasoning effort. MiniMax lists five values: low, medium, high, xhigh, and max, with max as the default. According to its documentation, higher levels use more output tokens and take longer. Reasoning cannot be disabled; requests that try to switch it off return an error. Chat Completions uses reasoning_effort, while the Anthropic-compatible interface uses output_config.effort. Thinking configuration
That matters when evaluating a model called Flash. Try a small, well-defined edit at a lower effort level, then run the same task at a higher level. Repeat the comparison with a bug that requires investigation. Record how long each attempt takes, whether the fix works, and how often someone has to intervene. Generating tokens quickly saves little time if the assistant keeps choosing the wrong fix.
When connecting the model to tools, preserve the conversation history correctly. MiniMax’s tool-use guide says to send back the model’s full response, including any thinking fields, after a tool call. Some simple tool calls may omit thinking content, so clients should handle that possibility rather than assuming the field is always present.
Repeated context can also affect efficiency. MiniMax lists M3.1-Flash as supporting automatic prefix caching for requests with at least 512 input tokens. That threshold does not guarantee a cache hit: the prefix must match. Keep reusable instructions and tool definitions at the beginning, put changing content later, and inspect reported cache usage when comparing repeated requests.
What to check before paying for access
As of October 19, MiniMax’s M Plan overview still lists M3.1 Flash Preview in its Go, Explore, and Build tiers. Subscribers can use MiniMax Code or connect other AI tools with a subscription key. That key is separate from a pay-as-you-go API key. Before changing an integration, check which model your account can use and which billing arrangement applies. M Plan overview
The pricing overview checked on the same date lists MiniMax-M3 rates, with no separate M3.1-Flash entry. It would be premature to apply those M3 prices to Flash. Teams planning a deployment need an applicable rate and confirmed account access before they can produce a meaningful monthly cost estimate. MiniMax pricing overview
For developers who liked Space Bunny, MiniMax-M3.1-Flash is a reason to revisit those saved tasks. Run them with explicit effort settings and compare how long it takes to get a working result. The official connection gives those earlier impressions a clear point of reference. Testing the actual model available to your account will show how well those impressions hold up.