COR Brief
Join Beta
AI Tools›Code & Development›Multi-Token Prediction
Code & Development

Multi-Token Prediction

Multi-Token Prediction (MTP) is a training objective and architectural technique used in large language models to predict multiple future tokens simultaneously at each position, rather than one token at a time. This approach densifies training signals by extending the prediction scope beyond the immediate next token, which can improve data efficiency and overall performance on evaluation benchmarks. Models such as DeepSeek-V3 and GLM-4.5 implement MTP to enhance training and inference capabilities. For example, DeepSeek-V3 is a 671 billion parameter mixture-of-experts model that activates 37 billion parameters per token and uses MTP alongside Multi-head Latent Attention for efficient training and inference. GLM-4.5 incorporates an MTP layer to support speculative decoding during inference after pre-training on large corpora of general and code/reasoning tokens.

Updated Feb 4, 2026unknown
Visit Multi-Token Prediction ↗Visual Guide
Overview

Multi-Token Prediction enables simultaneous prediction of multiple future tokens per position to improve training efficiency and model performance.

Pricing
unknown
Category
Code & Development
Company
Visual Guide
Interactive PresentationOpen Fullscreen ↗
Key Features
01
Predicts k additional future tokens per position using a shared output head with the main model after softmax on logits.
02
Extends the prediction scope beyond the immediate next token to improve data efficiency during pre-training.
03
Enables faster inference by supporting speculative decoding, as implemented in GLM-4.5.
04
Achieves higher accuracy on code generation tasks, e.g., 95% accuracy with 4-token prediction versus 80% for single-token baselines.
05
Works with MoE models like DeepSeek-V3, which activates a subset of parameters per token for efficient training and inference.
Real-World Use Cases

Code Generation

Training models on large code corpora to improve generation accuracy and speed.

Efficient Large Language Model Training

Incorporating MTP into training pipelines to densify training signals and improve data efficiency.

Faster Inference via Speculative Decoding

Using MTP-enabled models like GLM-4.5 to perform speculative decoding during inference.

Quick Start
1
Review Technical Reports
Study detailed MTP implementation in papers such as the DeepSeek-V3 technical report on arXiv to understand output heads and softmax usage.
2
Access Pre-trained Models
Use platforms like Dataloop.ai to obtain pre-trained MTP models, for example, a 7B parameter model trained on 1T code tokens.
3
Integrate MTP Modules
Add MTP modules after transformer layers in your training pipeline to enable multi-token target prediction.
4
Benchmark Performance
Evaluate model performance on code and math benchmarks, comparing results against single-token prediction baselines.
5
Enable Speculative Decoding
For inference, activate speculative decoding features if supported by the model, such as in GLM-4.5.
📊

Strategic Context for Multi-Token Prediction

Get weekly analysis on market dynamics, competitive positioning, and implementation ROI frameworks with AI Intelligence briefings.

Try Intelligence Free →
7 days free · No credit card
Pricing
Model: unknown

No pricing information is available as Multi-Token Prediction is a research technique integrated into various models, which may have separate API or hosting costs.

Assessment
Strengths
  • Improves benchmark performance when added to models like DeepSeek-V3.
  • Achieves higher code accuracy (95% at n=4 vs. 80% at n=1).
  • Enhances data efficiency via denser training signals.
  • Enables speculative decoding for faster inference in GLM-4.5.
  • Compatible with efficient mixture-of-experts architectures.
Limitations
  • No centralized official website or single repository for Multi-Token Prediction as it is a research technique.
  • Limited open-source implementations; main research code (MuToR) is pending full upload.
Alternatives
Single-Token Prediction→Multi-Token Attention (MTA)→