LLM Comparison
Cactus Needle 2 vs MiniMind
Side-by-side specs, pricing & capabilities · Updated October 2026
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2/6 models| Organization | ||
| OpenTools Score | ||
| Family | Needle | MiniMind |
| Status | Current | Current |
| Release Date | Aug 2026 | Sep 2026 |
| Context Window | — | 1K tokens |
| Input Price | — | — |
| Output Price | — | — |
| Pricing Notes | Apache 2.0 model artifact; runtime cost depends on local hardware, not token API pricing. | Open-source project; API pricing is not applicable unless a downstream host adds serving costs. |
| Capabilities | tool-usestructured-outputjson-schemadevice-controledge-inferenceoffline | texteducationtraining |
| Training Cutoff | — | Not specified |
| Max Output | — | 1K tokens |
| API Identifier | Cactus-Compute/needle2 | jingyaogong/minimind |
| Benchmarks | ||
| Mobile Actions Ordered Strict Exact Match | 63.7cactus-compute | — |
| BFCL v4 Single-Turn Overall | 42.6cactus-compute | — |
| View Cactus Needle 2 | View MiniMind | |
Cactus Compute
Cactus Needle 2
Cactus Needle 2 is a 45M-parameter, 14 MB agentic model for tiny devices. It focuses on tool calling, device control, schema-constrained JSON, and structured extraction; Cactus reports 28 MB session RAM, 800+ tok/s prefill, and 500+ tok/s decode on Raspberry Pi 5. Its 256-token sliding attention window is not a total context or output limit; those ceilings are not established by the model documentation.
Jingyao Gong
MiniMind
MiniMind is a small open-source language-model training project that demonstrates how to train a 64M-parameter LLM from scratch. The repository is aimed at builders who want a compact, inspectable model-training path rather than a hosted frontier API.
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