LLM Comparison
Cactus Needle 2 vs MiniCPM
Side-by-side specs, pricing & capabilities · Updated September 2026
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2/6 models| Organization | ||
| OpenTools Score | ||
| Family | Needle | MiniCPM |
| Status | Current | Current |
| Release Date | Aug 2026 | — |
| Context Window | — | 128K tokens |
| Input Price | — | — |
| Output Price | — | — |
| Pricing Notes | Apache 2.0 model artifact; runtime cost depends on local hardware, not token API pricing. | Open-weight GitHub and Hugging Face model family. There is no fixed vendor API price; runtime cost depends on the host, hardware, or inference provider. |
| Capabilities | tool-usestructured-outputjson-schemadevice-controledge-inferenceoffline | textcodereasoninglocal-inference |
| Training Cutoff | — | Not publicly specified in queued source |
| Max Output | — | 33K tokens |
| API Identifier | Cactus-Compute/needle2 | OpenBMB/MiniCPM |
| Benchmarks | ||
| Mobile Actions Ordered Strict Exact Match | 63.7cactus-compute | — |
| BFCL v4 Single-Turn Overall | 42.6cactus-compute | — |
| MiniCPM-SALA standard benchmark average | — | 76.53official-github-readme |
| MiniCPM-SALA long-context average | — | 38.97official-github-readme |
| MiniCPM-SALA 2048K extrapolation score | — | 81.6official-github-readme |
| MiniCPM4.1 reasoning decoding speedup | — | 3official-github-readme |
| MiniCPM4 Jetson AGX Orin decoding speedup vs Qwen3-8B | — | 7official-github-readme |
| View Cactus Needle 2 | View MiniCPM | |
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.
OpenBMB
MiniCPM
MiniCPM is OpenBMB’s ultra-efficient open language-model family for edge and end-device deployment. The MiniCPM4 and MiniCPM4.1 lines focus on fast local reasoning, while MiniCPM-SALA extends the family toward sparse/linear attention and million-token context research.
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