---
title: "DeepSeek-V3.1 Doubles Agent Coding Scores and Merges Reasoning Into One Model"
subtitle: "DeepSeek-V3.1 merges thinking and non-thinking modes into one 671B open-weight model, with major agentic coding and search improvements"
url: "https://alphasignal.ai/news/deepseek-v3-1-doubles-agent-coding-scores-and-merges-reasoning-into-one-model"
author: "AlphaSignal Newsroom"
author_url: "https://alphasignal.ai/editorial-team"
company: "DeepSeek"
company_url: "https://alphasignal.ai/company/deepseek_ai"
published: "2026-07-04T02:44:38.000Z"
updated: "2026-07-04T02:52:47.627Z"
type: "Model"
topics:
  - "Llms"
  - "Post Training"
subtopics:
  - "Long Context"
  - "Mixture Of Experts"
upvotes: 4473
source_url: "https://huggingface.co/deepseek-ai/DeepSeek-V3.1"
comments: 0
truncated: true
---

# DeepSeek-V3.1 Doubles Agent Coding Scores and Merges Reasoning Into One Model

> DeepSeek-V3.1 merges thinking and non-thinking modes into one 671B open-weight model, with major agentic coding and search improvements

[DeepSeek-V3.1](https://huggingface.co/deepseek-ai/DeepSeek-V3.1) is out, and it is a meaningful step up from the V3 line. The headline feature is a single model that can switch between a fast, direct-answer mode and a slower, chain-of-thought reasoning mode, just by changing the chat template. But the deeper story is what happened to its agentic capabilities: tool use, code agents, and search agents all got a substantial post-training overhaul that pushed V3.1 well past its predecessors on real-world software engineering benchmarks.

## One model, two brains

Previous DeepSeek releases split reasoning and non-reasoning into separate model families. V3.1 collapses that into a single hybrid architecture that supports both thinking and non-thinking modes, delivering faster responses and stronger tool usage for intelligent agents. The switch is a one-line change to the chat template: prepend `<think>` to activate reasoning mode, or `</think>` to skip it entirely.

In thinking mode, V3.1 reaches answers in significantly less time compared to DeepSeek-R1-0528, while post-training optimization gives the model major improvements in tool usage and intelligent agent tasks. That is a notable claim: comparable answer quality to a dedicated reasoning model, but faster. The efficiency gain comes from the model learning to reason more concisely rather than generating sprawling chains of thought.

## The architecture under the hood

With 671 billion total parameters but only 37 billion activated per token, V3.1 uses a Mixture-of-Experts (MoE) design to achieve high performance with reduced computational cost. MoE works by routing each token through a small subset of specialized sub-networks ("experts") rather than the entire model, keeping inference costs manageable despite the enormous total parameter count.

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## Sources

- [DeepSeek-V3.1 Model Card – Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-V3.1)
- [DeepSeek-V3.1 Release – DeepSeek API Docs](https://api-docs.deepseek.com/news/news250821)
- [DeepSeek-V3 Technical Report – arXiv](https://arxiv.org/html/2412.19437v1)
- [Introducing DeepSeek-V3.1: A Hybrid AI Model – Medium](https://medium.com/@CherryZhouTech/introducing-deepseek-v3-1-a-hybrid-ai-model-with-advanced-agentic-capabilities-8827e1829c2e)
- [DeepSeek Release Notes – Releasebot](https://releasebot.io/updates/deepseek)