LLM Comparison
MiniCPM vs MiniMind
Side-by-side specs, pricing & capabilities · Updated September 2026
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2/6 modelsSame tier:
| Organization | ||
| OpenTools Score | ||
| Family | MiniCPM | MiniMind |
| Status | Current | Current |
| Release Date | — | Sep 2026 |
| Context Window | 128K tokens | 1K tokens |
| Input Price | Free | Free |
| Output Price | Free | Free |
| Pricing Notes | 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. | Open-source project; API pricing is not applicable unless a downstream host adds serving costs. |
| Capabilities | textcodereasoninglocal-inference | texteducationtraining |
| Training Cutoff | Not publicly specified in queued source | Not specified |
| Max Output | 33K tokens | 1K tokens |
| API Identifier | OpenBMB/MiniCPM | jingyaogong/minimind |
| Benchmarks | ||
| 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 MiniCPM | View MiniMind | |
Cost Calculator
Enter your expected monthly token usage to compare costs.
| Model | Input | Output | Total / mo | vs Best |
|---|---|---|---|---|
| MiniCPMCheapest | $0.00 | $0.00 | $0.00 | — |
| MiniMindCheapest | $0.00 | $0.00 | $0.00 | — |
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.
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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