Maple Preview
Maple Preview by deepgrove, a text-generation model. Understand and compare features, benchmarks, and capabilities.
Comparison
| Feature | Maple Preview | Interfaze |
|---|---|---|
| Input Modalities | text | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | No | Yes |
| Language Support | unknown | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | No | Yes |
| Context Input Size | 131.1K | 1M |
| Tool Calling | No | Tool calling supported + built in browser, code execution and web search |
Scaling
| Feature | Maple Preview | Interfaze |
|---|---|---|
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |
View model card on Hugging Face
DeepGrove · 2026
Today we introduce Maple-Preview, an open-source 20B-A1B ternary-weight reasoning LLM. Maple-Preview has SOTA reasoning for its weight class and is even competitive with larger models. It solves IMO-level problems and runs at 200+ tokens/sec on a Mac mini M4, 5–16× faster than efficient models like Gemma 4, Qwen3.5, and gpt-oss.
- 20B-A1B Model
- 218 tok/s M4 Mac mini
- 5.31 GB Checkpoint
- 131,072 Token context
[!NOTE] The included Transformers implementation depends on Triton and FlashAttention and is intended for a compatible CUDA environment. The reported Apple Silicon result uses a separate on-device runtime.
Architecture
Maple-Preview is a 20B-A1B reasoning model designed from the start for efficient on-device inference. It utilizes a 24-layer, 256-expert (8 active) configuration with 3:1 SWA-512:GA attention.
Evaluation
On benchmarks, Maple-Preview sets a new point on the Pareto frontier for both memory-to-performance and speed-to-performance, demonstrating its strong reasoning capabilities. However, we note that this preview is focused primarily on raw reasoning and, as such, may underperform on agentic benchmarks. We intend to continue improving general performance through extended training before Maple's full release.
Capability comparison using the dense output head across LCBv6, AIME 2026, HMMT 2026, and GPQA-D.
Limitations
This preview received minimal post-training for agentic tasks and only small-scale general reinforcement learning.
License
Maple-Preview is released under the MIT License.