LLMFly AI 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.
LLMFly AI compared with Needle 3
| Detail | 
LLMFly AI | N3
Needle 3 |
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| What it does | One API to access GPT, Claude, Gemini and Grok models through an OpenAI-compatible interface
| 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 | GB
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| Pricing model | Paid
| Open source
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| Pricing | Usage-based pricing per model. GPT-6 Astra: $3 input, $15 output per 1M tokens (≤272K tokens/request); $6 input, $22.5 output (>272K). GPT-5.6 Sol: $1.5 input, $9 output (≤272K); $3 input, $13.5 output (>272K). Claude Haiku 4.5: $0.5 input, $2.5 output per 1M tokens. Gemini 3.8 Flash: $0.3 input, $1
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| Key features | - OpenAI-compatible API endpoint for multiple models
- Support for GPT, Claude, Gemini, and Grok models
- Per-model transparent pricing with usage tracking
- API key management by application and environment
- One-to-one technical support for setup and integration
- Model rate comparison and pricing calculators
- Support for streaming and structured output
- Compatible with Claude Code, Codex, Cursor and backend services
| - 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 | - Single API for multiple leading models without rebuilding integrations
- Significant pricing discounts compared to official provider APIs
- One-to-one technical support and enterprise-grade daily experience
- Transparent per-request cost tracking with usage records
| - 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) | 54 ▼ −1 (down) since last check | 56 ● no change since last check |
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| Trust Flow (Majestic) | 11 | 14 |
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| Citation Flow (Majestic) | 37 | 39 |
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| Referring domains (Majestic) | 206 | 1592 |
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4 details filled in for both companies.
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