LLM Comparison
Maple-Preview vs MiniMind
Side-by-side specs, pricing & capabilities · Updated October 2026
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2/6 models| Organization | ||
| OpenTools Score | 45 | |
| Family | Maple | MiniMind |
| Status | Current | Current |
| Release Date | Aug 2026 | Sep 2026 |
| Context Window | 131K tokens | 1K tokens |
| Input Price | — | — |
| Output Price | — | — |
| Pricing Notes | Open-source model card; no hosted API pricing found in the official source. Local inference costs depend on user hardware and runtime. | Open-source project; API pricing is not applicable unless a downstream host adds serving costs. |
| Capabilities | textreasoningcodeon-device-inference | texteducationtraining |
| Training Cutoff | — | Not specified |
| Max Output | — | 1K tokens |
| API Identifier | deepgrove/maple-preview | jingyaogong/minimind |
| Benchmarks | ||
| LiveCodeBench v6 | 75.1DeepGrove model card | — |
| AIME 2026 | 87.5DeepGrove model card | — |
| HMMT 2026 | 78.8DeepGrove model card | — |
| GPQA Diamond | 73.5DeepGrove model card | — |
| View Maple-Preview | View MiniMind | |
DeepGrove
Maple-Preview
Maple-Preview is DeepGrove’s open-source 20B-A1B ternary-weight reasoning LLM for efficient on-device inference. The model card reports 20.2B total parameters, 1.49B active parameters, a 131,072-token context window, and 5.31 GB checkpoint size. The model card specifies 131,072 tokens of context but does not establish a separate maximum output-token limit.
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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