DeepSeek-V3.1

Advanced reasoning model with hybrid thinking capabilities

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About model

Revolutionary Hybrid AI:
DeepSeek-V3.1 is a groundbreaking hybrid model that switches between thinking and non-thinking modes, delivering exceptional performance in reasoning, coding, and agent tasks. Built on a massive 671B parameter MoE architecture with 37B activated parameters, it offers unparalleled flexibility for developers seeking both fast responses and deep analytical capabilities.

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Quickstart guides

RAG

Building a RAG Workflow

Agents

Agent Workflows

Apps

Next.js Chat Quickstart

Performance benchmarks

Model	AIME 2025	GPQA Diamond	HLE	LiveCodeBench	MATH500	SWE-bench verified
DeepSeek-V3.1	49.8%	74.9%
Related open-source models
Competitor closed-source models
Claude Opus 4.6		90.5%	34.2%			78.7%
OpenAI o3		83.3%	24.9%		99.2%	62.3%
OpenAI o1		76.8%			96.4%	48.9%
GPT-4o		49.2%	2.7%	32.3%	89.3%	31.0%

API usage

cURL
Python
Typescript

Endpoint:

deepseek-ai/DeepSeek-V3.1

curl -X POST "https://api.together.xyz/v1/chat/completions" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-ai/DeepSeek-V3.1",
    "messages": [
      {
        "role": "user",
        "content": "What are some fun things to do in New York?"
      }
    ]
}'

from together import Together

client = Together()

response = client.chat.completions.create(
  model="deepseek-ai/DeepSeek-V3.1",
  messages=[
    {
      "role": "user",
      "content": "What are some fun things to do in New York?"
    }
  ]
)
print(response.choices[0].message.content)

import Together from 'together-ai';
const together = new Together();

const completion = await together.chat.completions.create({
  model: 'deepseek-ai/DeepSeek-V3.1',
  messages: [
    {
      role: 'user',
      content: 'What are some fun things to do in New York?'
     }
  ],
});

console.log(completion.choices[0].message.content);

Model card
Architecture Overview:
• Mixture-of-Experts (MoE) architecture with 671B total parameters and 37B activated parameters
• Built upon DeepSeek-V3.1-Base with two-phase long context extension approach
• Extended training with 630B tokens for 32K phase and 209B tokens for 128K phase
• Compatible with UE8M0 FP8 scale data format for microscaling optimization

Training Methodology:
• Post-trained on expanded dataset with additional long documents
• 10-fold increase in 32K extension phase training
• 3.3x extension in 128K phase training
• Advanced post-training optimization for tool usage and agent tasks

Performance Characteristics:
• Hybrid mode supporting both thinking and non-thinking operations
• Superior performance on MMLU-Redux (91.8% non-thinking, 93.7% thinking)
• Exceptional coding capabilities with LiveCodeBench Pass@1 of 56.4% (non-thinking) and 74.8% (thinking)
• Advanced math reasoning with AIME 2024 Pass@1 of 66.3% (non-thinking) and 93.1% (thinking)
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Applications & use cases
Advanced Reasoning & Analysis:
• Complex mathematical problem solving with step-by-step reasoning
• Scientific research and analysis with transparent thought processes
• Academic writing and research with comprehensive literature review capabilities
• Strategic planning and decision-making with multi-factor analysis

Software Development & Engineering:
• Full-stack application development with multi-language support
• Code review and optimization with detailed explanations
• Architecture design and system planning
• Debugging and troubleshooting with systematic approaches

Agent & Automation Tasks:
• Autonomous code agents for software development workflows
• Search agents for information gathering and analysis
• Multi-step task automation with tool integration
• Workflow orchestration and process optimization

Enterprise & Business Applications:
• Data analysis and reporting with comprehensive insights
• Technical documentation and knowledge management
• Customer support and query resolution
• Process automation and workflow optimization
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Related models

Model specifications

Model data

Model provider
DeepSeek
Type
LLM
Chat
Reasoning
Main use cases
Chat
Fine tuning
Supported
Speed
High
Intelligence
Very High
Deployment
Serverless
On-Demand Dedicated
Monthly Reserved
Endpoint
deepseek-ai/DeepSeek-V3.1
Parameters
671B
Activated parameters
37B
Context length
128K
Input price
$0.60 / 1M tokens
Output price
$1.70 / 1M tokens
Input modalities
Text
Output modalities
Text

Released
August 20, 2025
Last updated
August 26, 2025
Quantization level
FP4
External link
Provider docs
Category
Chat

Run in Playground

Quickstart docs

Deploy model

DeepSeek-V3.1

About model

API usage

Model card

Applications & use cases