In this comprehensive analysis, I'll explore the key differences between two advanced AI models: DeepSeek-V3 and kimi-1.5. These models represent distinct approaches to large language model development, each bringing unique innovations and technological advancements to the field.

DeepSeek-V3 distinguishes itself with its sophisticated Mixture-of-Experts (MoE) architecture and impressive 671B total parameters, while kimi-1.5 takes a groundbreaking approach with its multimodal capabilities and advanced vision-language integration. The contrast between these models provides valuable insights into different strategies for achieving state-of-the-art AI performance. Let's examine their distinct characteristics, from their fundamental architectural choices and training methodologies to their specialized capabilities and deployment strategies, understanding how each model contributes uniquely to the advancement of AI technology.

We'll explore how DeepSeek-V3's focus on efficient large-scale training and kimi-1.5's emphasis on multimodal integration represent different paths toward pushing the boundaries of AI capabilities.

How to read lengthy and complex technical papers faster and more in-depth? This blog uses rflow.ai to help with analysis.

The ResearchFLow digested version of the DeepSeek-R1 paper is here.

The original paper link is here.

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Key Differences Between DeepSeek-V3 and kimi-1.5

Model Architecture

DeepSeek-V3:

• Uses Mixture-of-Experts (MoE) architecture with 671B total parameters, 37B activated
• Employs Multi-head Latent Attention (MLA) and DeepSeekMoE
• Utilizes auxiliary-loss-free strategy for load balancing
• Implements multi-token prediction training objective

kimi-1.5:

• Specific architecture details not provided, but uses a variant of Transformer decoder
• Integrates multimodal capabilities
• Employs improvements in architecture and optimization strategies for stable large-scale training

Training Approach

DeepSeek-V3:

• Focuses on efficient pretraining and supervised fine-tuning
• Uses FP8 mixed precision training framework
• Employs pipeline parallelism and expert parallelism

kimi-1.5:

• Emphasizes reinforcement learning (RL) for continued scaling and improvement
• Uses long context scaling up to 128k tokens
• Implements improved policy optimization methods
• Employs curriculum sampling and prioritized sampling strategies

Training Data

DeepSeek-V3:

• Trained on 14.8 trillion diverse and high-quality tokens
• Focuses on text data with emphasis on English and Chinese

kimi-1.5:

• Uses a diverse, high-quality multimodal corpus
• Includes text, image, and video data
• Covers five domains: English, Chinese, Code, Mathematics & Reasoning, and Knowledge

Multimodal Capabilities

DeepSeek-V3:

• Primarily focused on text processing
• No specific mention of multimodal capabilities

kimi-1.5:

• Designed as a multimodal model from the ground up
• Can process and understand information from text, images, and videos
• Includes specialized training for vision-language tasks

Training Stages

DeepSeek-V3:

• Pretraining
• Supervised Fine-Tuning (SFT)
• Reinforcement Learning (RL) stages

kimi-1.5:

1. Vision-language pretraining stage
2. Vision-language cooldown stage
3. Long-context activation stage

Performance and Benchmarks

DeepSeek-V3:

• Strong performance on language tasks, especially in English and Chinese
• Excels in code and math-related tasks
• Outperforms other open-source models on many benchmarks

kimi-1.5:

• Achieves state-of-the-art results across multiple modalities
• Strong performance in vision-language tasks
• Competitive or superior performance on text, vision, and reasoning challenges

Unique Features

DeepSeek-V3:

• Focuses on economical training costs
• Implements efficient cross-node all-to-all communication
• Uses DualPipe algorithm for efficient pipeline parallelism

kimi-1.5:

• Emphasizes long-context reasoning capabilities
• Implements 'long2short' methods for improving short-CoT models
• Uses partial rollouts technique for handling long-CoT features efficiently

Infrastructure and Deployment

DeepSeek-V3:

• Trained on a cluster with 2048 NVIDIA H800 GPUs
• Uses a custom HAI-LLM framework for training
• Implements specific deployment strategies for inference load balancing

kimi-1.5:

• Uses a hybrid deployment framework for training and inference
• Implements a code sandbox for secure code execution and evaluation
• Utilizes Kubernetes for scalability and resilience in deployment