Space Bunny Free Model: Access, Limits & Setup Guide

Searching for space bunny free model usually means you want to try the model, understand what costs money, or connect it to a coding workflow. The useful starting point is to separate the model from the service carrying your request. A free listing on one gateway does not determine another website's billing, account requirements, or availability.

Quick answer: Space Bunny Alpha is an anonymous preview model for reasoning, coding, and multimodal understanding. Some gateways list free access. On spacebunny.app, you sign in and use any free credits available to your account; requests consume credits. The Space Bunny free model page explains that distinction and provides an embedded playground.

Availability checked October 3, 2026: OpenRouter currently marks its Space Bunny Alpha listing for removal on October 5, 2026. This is a notice for that route, not a confirmed deadline for every service. Recheck availability before integrating. OpenRouter model listing.

This guide combines current public documentation with suggested exercises. The examples are starting points, not results from an authenticated benchmark or a guarantee that a request will succeed.

Table of contents

• What is the Space Bunny free model?
• Is Space Bunny really free? Compare the access routes
• Start with one useful playground prompt
• Use the context window without wasting it
• Choose reasoning effort for the task
• Try coding, visual analysis, and structured output
• Move a working prompt into the API
• Evaluate quality with a small repeatable test
• Fix common access and response problems
• Plan for changes to the free preview
• Frequently asked questions

What is the Space Bunny free model?

Space Bunny Alpha is presented as a long-context reasoning model. The Space Bunny website describes text, image, and video input, text output, adjustable reasoning, and tool calling. The website is independently operated; it does not claim to be the undisclosed model developer.

The published capabilities suggest useful experiments, but they are not evidence that every gateway exposes identical settings or that every answer is correct.

Published capability	What it means for a first test
1,000,000-token context window	Supply connected evidence without splitting every source into separate conversations.
Up to 524,288 completion tokens	A listed ceiling, not a sensible default output budget.
Text, image, and video input	Ask questions about supplied material; confirm media compatibility on your route.
Text output	Expect explanations, code, or JSON, rather than generated pictures or video.
Five reasoning levels	Adjust effort from low through medium, high, xhigh, and max.
Tools and JSON responses	Integrate with application logic that validates the model's output.

The developer identity, parameter count, and underlying architecture are not established by these features. You also cannot infer an open-source license or downloadable weights from the word “free.” Evaluate the service you can actually access.

Is Space Bunny really free? Compare the access routes

Treat “free” as a property of a particular offer at a particular time. These are three different access arrangements:

Route	Identifier or entry point	What to check
spacebunny.app	Signed-in playground; API documentation uses stealth/space-bunny-alpha	Available credits, request usage, and current account terms.
OpenCode Zen	space-bunny-free	Current model catalog and limited-time free access.
OpenRouter	stealth/space-bunny-alpha	Current availability, account restrictions, and the October 5 removal notice.

OpenCode's documentation currently lists Space Bunny Free with zero input and output pricing and explicitly describes the offer as temporary. In OpenCode configuration, its provider-qualified name is opencode/space-bunny-free. OpenCode Zen documentation.

On spacebunny.app, do not assume that an upstream zero-dollar token price means unlimited requests. The site's free-model introduction says requests consume account credits and additional usage is available through credit packs. Check the Space Bunny pricing page for the current offer rather than relying on a copied plan table that may become stale.

Before sending a large task, establish your available balance, how usage is measured, and what happens when it runs out. A model token, an account credit, and a request are different units. Do not convert between them without an explicit billing rule.

Start with one useful playground prompt

Use the Space Bunny playground to learn the request-and-response workflow before adding an integration. Sign in, inspect any available credits, and choose a small task whose answer you can verify.

1. Pick one outcome: identify a bug, reconcile two paragraphs, or explain a screenshot.
2. Supply the minimum evidence needed to decide it.
3. Start with low reasoning effort and a modest output limit, such as 2,048 tokens.
4. State the required answer format and what counts as success.
5. Inspect the answer, reported usage, and any unfinished output before increasing scope.

Here is an original first-test prompt:

Review the retry policy below for duplicate-processing risks.

Context: a delivery event can arrive more than once.
Current policy: retry any failed handler immediately, up to five times.
The handler creates a shipment before writing the delivery receipt.

Return three sections:
1. The most likely failure scenario, using only the supplied facts.
2. The smallest change that prevents duplicate shipments.
3. One test that would demonstrate the fix.

Separate missing information from conclusions. Stay under 250 words.

A useful answer should notice that shipment creation could succeed before receipt storage fails. It should explain how duplicate handling is prevented and propose a relevant verification step. Fluent prose alone does not pass the test. If the answer assumes a database constraint or an external API capability, ask it to identify that assumption.

Use the context window without wasting it

A large context window helps when the evidence is distributed. For example, an incident review might need a deployment note, several log excerpts, a handler, and a database schema. Keeping them together can make relationships easier to inspect.

It does not follow that uploading an entire repository produces the best answer. Unrelated files introduce distractions, stale copies can contradict current behavior, and a longer request can take more time. Capacity is not a promise of perfect retrieval.

Build a compact evidence packet with four parts:

• Question: the specific decision you need to make.
• Source map: filenames or document IDs, dates, and a one-line purpose.
• Evidence: the relevant excerpts, preserving line numbers where available.
• Answer contract: findings must cite a supplied source and mark uncertainty.

Ask the model to identify missing evidence before expanding the packet. For code, exclude dependencies, generated files, and duplicate logs unless they bear directly on the failure. For documents, state which revision is authoritative. For screenshots, explain the user journey rather than expecting the image to reveal business requirements.

Reserve room for the answer and any reasoning counted by the route. Do not assume the maximum context and maximum completion numbers can simply be added together in a single request.

Choose reasoning effort for the task

Space Bunny's documentation lists low, medium, high, xhigh, and max. Treat these as settings to test, not a quality ranking that guarantees the highest setting wins.

Start low for extraction, concise rewriting, and a small self-contained bug. Try medium or high when the task involves conflicting evidence, several interacting components, or a decision with explicit tradeoffs. Reserve the highest settings for cases where a controlled comparison shows enough improvement to justify the extra wait and usage.

Change one variable at a time. Keep the prompt and source material fixed, compare two effort levels, and score both against the same checklist. Otherwise, you cannot tell whether a better result came from deeper reasoning or better instructions.

The site documentation says reasoning tokens can contribute to completion usage even when they are not displayed. A short visible answer therefore need not imply a small request. Check the usage record, and leave enough output budget for the task to finish.

Try coding, visual analysis, and structured output

Coding: request a narrow, reviewable change

Give the model a failing example and the relevant implementation. Ask it to explain the cause, propose a minimal patch, and name the behavior that should remain unchanged. Review the diff and run the relevant check yourself. A generated test that simply repeats an implementation assumption is weak evidence.

For an unfamiliar repository, first request a map of the relevant files. This avoids a sweeping rewrite based on an incomplete view of the system.

Visual analysis: make the question observable

Supply a screenshot with a task such as identifying the primary action and three hierarchy problems. Ask the model to distinguish what is visible from what would require interaction or measurement. A screenshot alone does not establish keyboard navigation or a complete accessibility audit.

For video, verify that your selected provider supports the input URL and format. Request time references for observations where available. Do not assume that acceptance of a link proves the media was successfully understood.

Structured output: validate before using it

Request a JSON object when another program needs the result. Define required fields, allowed values, and behavior when information is missing. Then parse and validate the response in your application.

JSON formatting and schema enforcement are different guarantees. Likewise, a requested tool call is proposed data, not permission to execute it. Begin with read-only functions and validate arguments before allowing external actions.

Move a working prompt into the API

Keep the base URL, model identifier, and API key from the same service. A common integration mistake is copying one gateway's model name into another gateway's request.

The Space Bunny API documentation currently specifies POST /api/v1/chat/completions and uses stealth/space-bunny-alpha. Some promotional snippets use the shorter space-bunny label; the example below follows the dedicated API reference. Confirm the accepted identifier when you connect.

# Run on your server with SPACE_BUNNY_API_KEY already set securely.
curl --fail-with-body --max-time 90 \
  https://spacebunny.app/api/v1/chat/completions \
  -H "Authorization: Bearer $SPACE_BUNNY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "stealth/space-bunny-alpha",
    "messages": [{
      "role": "user",
      "content": "Suggest three tests for a duplicate event handler. State the expected outcome of each."
    }],
    "reasoning": { "effort": "low" },
    "max_completion_tokens": 2048
  }'

This is a documentation-based request, not an executed API test. It requires valid credentials, sufficient account access, and an available upstream route. Store the key server-side; do not paste it into client JavaScript or a public example.

For a successful non-streaming response, inspect choices[0].message.content, finish_reason, and usage. Tool responses need a separate handling path. Detect truncation and validate structured content before treating the job as complete. The example's timeout is an application choice, not a provider latency guarantee.

Evaluate quality with a small repeatable test

A ten-task evaluation is more informative than one impressive conversation. Choose tasks from work you actually do, and prepare expected evidence before looking at the model's answers.

Task group	Number	Acceptance criterion
Small code defects	3	Identifies the cause and proposes a change that passes a relevant check.
Document reconciliation	3	Cites the controlling source and notices a planted contradiction.
Screenshot interpretation	2	Reports visible evidence and avoids inventing hidden behavior.
JSON extraction	2	Produces parseable output with the required fields and correct values.

Record the route, date, effort setting, input size, elapsed time, reported usage, and whether the result passed. Save the input alongside the score so that a future model can receive the same task.

Count retries and manual correction time. An answer that needs extensive repair may be expensive even when the model token price is zero. Compare against your existing workflow rather than a leaderboard assembled from unrelated prompts.

Repeat a few representative cases to see whether the result is stable. Ten tasks will not establish a universal ranking, but they can reveal obvious mismatches. Keep failure examples: they tell you which work still needs a different model or human review.

Fix common access and response problems

Diagnose the response before changing credentials or adding retries. The same visible failure can have several causes.

Symptom	First check	Useful next action
Sign-in prompt or insufficient credits	Session and account balance	Sign in and inspect your allowance; do not assume the request is free.
401	Key validity and selected service	Correct the credential without exposing it in logs.
400	Body shape, model, and supported fields	Reduce the request to plain text and add optional fields back individually.
402	Billing or credit requirement in the error body	Review account access before retrying.
429	Rate limit or shared capacity	Wait, respect retry guidance, and use bounded backoff.
Model unavailable or missing	Current catalog and preview notice	Verify availability before investigating local configuration.
Slow, empty, or cut-off answer	Finish reason, timeout, and output budget	Simplify context, compare lower effort, or adjust the output cap.

Avoid blind retry loops. A malformed request will not repair itself with repetition. For transient failures, cap attempts and record enough metadata to diagnose the problem without storing sensitive prompts unnecessarily.

Plan for changes to the free preview

Keep your chosen model and endpoint configurable. Save a small evaluation set and identify an acceptable fallback before the current route disappears. A switch can change output quality, formatting, usage, and data handling even when both APIs accept similar JSON.

Set an explicit rule for paid fallback. If a task starts under a free allowance, an automatic switch should not silently spend money. Prefer a clear unavailable state or an approved budget with a visible usage record.

Privacy also depends on the route. OpenRouter says the provider may retain prompts and completions without using them for training; OpenCode describes zero retention for its Space Bunny Free provider. Neither statement establishes spacebunny.app's policy. Review the terms of the actual service receiving your data, and begin evaluations with non-sensitive examples.

The practical goal is a workflow you can repeat and verify. Begin with one small task, keep the evidence, and expand only when the result meets a standard you have defined.

Frequently asked questions

Is the Space Bunny free model unlimited?

Do not assume so. Gateway previews can change, and spacebunny.app requests consume account credits. Check your balance and the current access conditions before a large batch.

Do I need an account on spacebunny.app?

Its current getting-started instructions require signing in and checking available credits. Account requirements on other gateways are separate.

Is Space Bunny Free the same identifier as Space Bunny Alpha?

They are names used in different service catalogs. OpenCode lists space-bunny-free; the Space Bunny API reference and OpenRouter use stealth/space-bunny-alpha. Use the identifier documented for your chosen endpoint rather than substituting names.

Can it generate images or videos?

The documented model accepts visual input and returns text. An image in the prompt does not turn it into an image-generation service.

Will it still be free after October 5, 2026?

Do not plan around that assumption. OpenRouter currently shows that removal date, while other services have their own availability and billing rules. Verify the route you use at the time of the request.

Is it suitable for production?

Test it against your own acceptance criteria first. Production use also needs reliable access, appropriate data terms, usage controls, output validation, and a fallback. A successful playground answer alone does not establish those conditions.