DeepSeek ✓

A next-generation V5 line would likely build on the current 1M-token architecture for larger long-context coding, reasoning, and document workloads.

Continued emphasis on agent-oriented development points toward stronger autonomous coding and multi-step tool execution.

DeepSeek’s recent efficiency work suggests the next generation may prioritize higher capability without proportionally increasing inference cost.

Ongoing work on sparse attention and speculative decoding points toward faster handling of very large contexts and extended agent trajectories.

The progression from V4 Preview through Flash and Pro updates suggests V5 would represent the next major foundation-model step rather than another minor post-training refresh.

Self-developed Harness tooling points to a dedicated environment for evaluating and refining agentic workflows.

Closed testing with a limited user pool suggests the Harness system is already entering practical external validation.

V4 GA appears tied to completion of Harness testing, implying agent tooling is a meaningful part of final release readiness.

A staged test-to-release process points to stronger emphasis on stability and real-world behavior before broad availability.

Harness integration suggests better support for long-running task execution, tool coordination, and agent-style workflows.

Introduced two variants: V4-Pro (~1.6T params) and V4-Flash (~284B params)

Major upgrade to Mixture-of-Experts (MoE) architecture, improving efficiency and scalability

Added ~1 million token context window, enabling much longer conversations and code understanding 

Significant boost in coding, reasoning, and agent capabilities compared to previous versions 

Focus on higher performance at lower cost, competing with top closed-source models

Much longer code understanding, with claims of over 1M token context enabled by a new sparse attention approach.

Better repo-level work, aiming for multi-file reasoning like tracking imports, types across modules, and cross-file refactors.

Stronger “memory-like” behavior for projects, via Engram conditional memory meant to retain and recall repo conventions and patterns.

More stable scaling and training efficiency, tied to the mHC (Manifold-Constrained Hyper-Connections) method DeepSeek publishe

Introduced DeepSeek Sparse Attention (DSA), a new sparse-attention system that greatly reduces compute and memory usage.

Long-context performance improved, enabling faster processing of long documents, large prompts, and extended conversations.

Inference cost significantly reduced — API pricing cut by more than half, making large-scale usage much more affordable.

Model efficiency improved while maintaining performance similar to DeepSeek V3.1 on most tasks.

Strong performance on reasoning, coding, and QA tasks, with some areas showing slight improvements.

Continued support for chat mode and reasoner mode, now optimized with V3.2 architecture.

Released with open-source weights, code, and tooling for easy deployment and experimentation.

Introduced DeepSeek Sparse Attention (DSA) for more efficient long-context handling.

API cost reduced by ~50% compared to V3.1.

Improved benchmarks in math/reasoning (e.g., AIME up), with small trade-offs in some knowledge tasks.

Both deepseek-chat and deepseek-reasoner models upgraded to V3.2 weights.

Added native support/optimizations for Chinese accelerators (Huawei Ascend, Cambricon, Hygon) alongside Nvidia GPUs.

Major improvements in reasoning and problem-solving

Handles math, coding, logic, and even Chinese writing more naturally—no need to switch modes

Now supports 128K tokens (that’s about 300+ pages of text!)

Perfect for long conversations, documents, and deep context

Uses a Mixture-of-Experts (MoE) system—think of it as calling in the right expert at the right time

This makes it more efficient and accurate across a wide range of tasks

Free to use, build on, and modify

Released under the permissive MIT License

🧠 671B MoE parameters
🚀 37B activated parameters
📚 Trained on 14.8T high-quality tokens

Supports up to 128,000 tokens, improving management of long-term dependencies.

Advanced Training Techniques

Achieves a score of 80 on the HumanEval benchmark, indicating strong coding capabilities.

Features Multi-head Latent Attention (MLA) for a 93.3% reduction in KV cache size and the DeepSeekMoE architecture for improved training efficiency.

Saves approximately 42.5% on training costs and increases generation throughput by 5.76 times.

Demonstrates robust performance in both English and Chinese.

These enhancements position DeepSeek 2.0 as a more powerful and efficient model for natural language processing tasks compared to its predecessor.