Space Bunny vs Needle 3
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
Space Bunny compared with Needle 3
| Detail | 
Space Bunny | N3
Needle 3 |
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| What it does | Long-context reasoning model with multimodal input and adjustable reasoning
| Foundation model for mobile, wearables, robots, smart home, automotive and microcontrollers
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| Stage | Not given
| Startup
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| Founded | Not given
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| Based in | Not given
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| Pricing model | Paid
| Open source
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| Pricing | Starter: $9.9 for 100,000 permanent credits; Pro: $99 for 1,000,000 permanent credits; Enterprise: $999 for 11,000,000 permanent credits (includes 10% extra). One-time purchase, no subscription, credits never expire.
| Not given
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| Key features | - 1 million token context window
- Text, image and video input support
- Five adjustable reasoning levels
- Tool calling and function execution
- JSON output format support
- OpenAI-compatible API
- Online playground for prompt evaluation
- Maximum 524,288 completion tokens
- Server-side API key management
- Streaming and structured responses
| - Tool calling with function selection and argument filling
- Structured extraction with guaranteed JSON parsing
- Text embeddings for local semantic search and matching
- Confidence scoring for routing and gating calls
- Laddered subnetwork architecture from 2 to 20 layers
- LoRA fine-tuning support for customization
- 400-4k tokens per second inference speed on Raspberry Pi 5
- Python package with pip install and Hugging Face distribution
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| What makes it different | - One million token context window for handling long documents and entire codebases without fragmentation
- Adjustable reasoning effort allows matching speed and depth to specific task requirements
- Multimodal input combining text, images and video in a single conversation
- Independent service with anonymous model developer and transparent limitations
- Built-in tool calling keeps application control over permissions and side effects
| - Beats models 10x its size on mobile tool calling benchmarks
- Matches models 2-3x larger on structured extraction tasks
- Entire model in a single 8-29 MB binary for tiny devices
- Simple Attention Network architecture uses 2x fewer MFLOPs than transformers
- Fine-tuned subnetworks outperform larger models like DeepSeek V4 Flash
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 51 ▲ +47 (up) since last check | 56 ● no change since last check |
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| Trust Flow (Majestic) | 13 | 14 |
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| Citation Flow (Majestic) | 22 | 39 |
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| Referring domains (Majestic) | 57 | 1592 |
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4 details filled in for both companies.
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