Motif 3 is a large-scale, decoder-only Mixture-of-Experts (MoE) language model with 314 billion total parameters and 13.2 billion parameters activated per token. It is built from the ground up by Motif Technologies following a fully in-house, proprietary design.
Motif 3 is built around Grouped Differential Latent Attention (GDLA), which integrates grouped differential attention with the compressed key–value representation of Multi-head Latent Attention. The architecture further incorporates modified manifold-constrained hyper-connections (mHC), Expert-Specific PolyNorm activations, and a Multi-Token Prediction (MTP) head to improve optimization stability, expert specialization, and inference efficiency.
The model is pretrained on approximately 12.5 trillion tokens spanning web documents, STEM, code, mathematics, multilingual content, and domain-specialized corpora, with additional emphasis on Korean, reasoning-intensive, legal, and financial data. Post-training combines general supervised fine-tuning, six RL-trained specialist teachers, a software-engineering teacher, and Multi-teacher On-Policy Distillation (MOPD) into a single unified model.
Key Features
🧠 Fine-grained sparse MoE — 384 routed experts with only 8 activated per token (plus 1 shared expert), providing a large expert pool at limited per-token compute.
⚙️ Novel architecture — GDLA attention, Expert-Specific PolyNorm, modified mHC, and a built-in MTP head enabling self-speculative decoding.
🌐 Multilingual & general-purpose, with a strong bytes-per-token tokenizer for English, Korean, code, and math.
🎯 Agentic strengths — particularly strong on long-horizon agentic tool use and terminal-based problem solving, with calibrated abstention on hallucination-sensitive evaluations.
2. Model Summary
Architecture
Mixture-of-Experts (MoE), decoder-only
Total Parameters
~314B
Activated Parameters
~13.2B / token
Number of Layers
53 (2 dense + 51 MoE)
Hidden Dimension
4096
Dense FFN Intermediate
12,288 (first 2 layers)
Attention
Grouped Differential Latent Attention (GDLA) with gated output
For contextual comparison, Motif 3 is compared with strong open-weight models using scores reported on the corresponding benchmark leaderboards. All Motif 3 evaluations were performed with sampling temperature = 1.0, top-p = 0.95, and a maximum sequence length of 262,144 tokens.
(*: public dataset only)
Benchmark
Motif 3 314B-A13B
MiniMax-3 428B-A23B
GLM-5.1 744B-A40B
Kimi-K2.6 1T-A32B
Qwen-3.7 max
DS-v4-Pro 1.6T-A49B
Agentic
GDPVal v2
38.7
44.4
37.8
34.4
39.0
40.2
τ²-Bench Telecom
94.7
88.9
97.7
95.9
94.7
96.2
τ³-Banking
35.3
15.3
13.6
23.3
12.0
30.1
ITBench*
51.5
—
40.3
31.2
42.5
38.3
Coding
SWE-Bench Verified
76.2
75.0
76.4
76.2
80.4
77.4
Terminal-Bench 2.1
74.9
65.2
61.8
65.9
75.0
64.0
SciCode
40.6
45.4
43.8
53.5
53.5
50.0
Reasoning & Knowledge
IMOAnswerBench
83.2
—
83.8
81.8
90.0
89.8
Apex-Shortlist
75.5
—
71.1
77.4
44.5
85.8
GPQA Diamond
83.4
92.9
86.8
91.1
92.4
88.8
HLE
37.0
39.0
30.1
37.5
41.4
37.5
CritPt
6.6
3.7
4.6
8.0
11.4
12.9
OmniScience — Accuracy
30.1
16.7
23.7
32.6
31.0
42.9
OmniScience — Non-Hallucination
71.6
81.6
70.1
59.5
74
5.9
Long Context & Instruction Following
AA-LCR
72.3
80.3
68.0
76.7
75.0
70.0
IFBench
78.2
82.9
76.3
76.0
79.1
76.5
Comparison scores are taken from the corresponding benchmark leaderboards.
Motif 3 performs particularly well on agentic and tool-oriented benchmarks, while maintaining competitive performance across coding, mathematical reasoning, and general knowledge. On AA-Omniscience it pairs its accuracy with one of the highest non-hallucination scores, indicating a favorable balance between answering correctly and abstaining when unsupported.
Motif 3 is a fully in-house design and introduces several custom components (full details in the technical report):
Grouped Differential Latent Attention (GDLA) — integrates grouped differential attention (asymmetric signal/noise heads with a token-dependent differential coefficient) with the compressed KV latent of Multi-head Latent Attention, plus a query-dependent output gate. Retains the expressive attention dynamics of differential attention while substantially reducing KV-cache requirements.
Expert-Specific PolyNorm — replaces the SiLU gate with a learned polynomial normalization whose coefficients are learned independently per expert, reducing activation outliers while allowing each expert to specialize.
Modified manifold-constrained hyper-connections (mHC) — replaces conventional residual additions with a doubly-stochastic (Birkhoff-polytope) mixing of 4 parallel residual streams; the post-mapping multiplier is annealed from 2 → 1 during pretraining to limit activation-outlier accumulation.
Multi-Token Prediction (MTP) — a 1-layer MTP head (DeepSeek-V3 style) used as an auxiliary pretraining objective and enabling self-speculative decoding at inference.
5. Deployment — vLLM (Recommended)
[!Note]
Tested on B200 and H200 GPUs.
The model ships with a built-in MTP head (num_nextn_predict_layers=1), so it supports self-speculative decoding — add --speculative-config as shown below (num_speculative_tokens: 1 is optimal for this model).
Supports online block-fp8 quantization with --quantization modelopt_blockfp8
If you encounter any issues, please open an HF issue.
This model is openly available — anyone can download the weights, no access request required.
7. License
This model is released under the MIT License. See the LICENSE file for details.
If you build on Motif 3, we'd truly appreciate a mention (e.g., "Built with Motif 3") when you share your work. Thanks for building with Motif!
8. Citation
@misc{lim2026motif3technicalreport,
title={Motif 3: Technical Report},
author={Junghwan Lim and Joon Son Chung and Sungmin Lee and Wai Ting Cheung and Gihun Cho and Minsu Ha and Sangho Kang and Beomgyu Kim and Dongseok Kim and Jangwoong Kim and Taehyun Kim and Taewhan Kim and Jeesoo Lee and Jeongdoo Lee and Junhyeok Lee and Dongpin Oh and Hyeyeon Cho and Dahye Choi and Jaeheui Her and Hanbin Jung and Changjin Kang and Minjae Kim and Youngrok Kim and Hyukjin Kweon and Hongjoo Lee and Yeongjae Park and Bokki Ryu},
year={2026},
eprint={2608.09119},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2608.09119},
}
