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DeepSeek R1

Open reasoning model trained for hard multi-step math, science, and code.

DeepSeek’s reasoning-focused model trained with reinforcement learning for multi-step math, science, and coding problem solving.

Why DeepSeek R1 matters

DeepSeek R1 showed that open (or open-weight) reasoning traces can rival closed thinking models on hard problems—at attractive cost. It reset expectations for what open reasoning stacks can deliver.

Tool calling · Thinking · Coding

Last reviewed: 31 July 2026

Official pricing →

When to choose DeepSeek R1

Decision guidance for architects—not a feature list.

Best for

  • Hard math and science questions
  • Competitive-programming style tasks
  • Chain-of-thought heavy agents
  • Open reasoning baselines

Avoid if

  • You need strong multimodal (vision/audio) in one model
  • Low latency without long thinking traces is mandatory

Strengths

Qualitative snapshot for architects—not a public ranking.

  • Hard-problem reasoning★★★★★
  • Open weights / research★★★★★
  • Cost for reasoning★★★★★
  • Multimodal★☆☆☆☆
  • Latency when thinking★★☆☆☆

Ecosystem

Built by

  • DeepSeek

    R1 is DeepSeek’s reasoning-focused open-weight flagship.

Competes with

  • GPT-5

    Open reasoning alternative to proprietary thinking / frontier models.

  • Llama 4

    Competes as an open stack choice when hard reasoning is the priority.

Works with

  • vLLM

    Common serving path for DeepSeek open weights.

Recommended for

  • Generative AI

    Landmark open reasoning model for modern generative AI stacks.

Often paired with

  • DeepSeek V4

    V4 is the current general/agent API generation; R1 remains the dedicated open reasoner.

  • DeepSeek V3

    V3 often handles general chat; R1 handles hard reasoning steps.

How DeepSeek R1 evolved

Key moments in chronological order.

  1. Benchmark

    Reasoning benchmark wave

    GPQA / LiveBench-style comparisons put R1 on the open-reasoning map.

  2. Research

    DeepSeek-R1 technical report

    RL-trained reasoning model with public traces reshapes open-model expectations.

  3. Open source

    Open weights release

    Weights and distillations enable self-hosted reasoning baselines worldwide.

  4. API

    DeepSeek API availability

    Hosted API makes R1 accessible without local GPU fleets.

  5. Model

    V3 generalist context

    Strong MoE generalist precedes R1 and pairs with it in many stacks.

Overview

DeepSeek’s reasoning-focused model trained with reinforcement learning for multi-step math, science, and coding problem solving.

Capabilities

  • Vision: No
  • Audio: No
  • Tool calling: Yes
  • Thinking: Yes
  • MCP: No
  • Coding: Yes
  • Structured output: Yes

Technical specifications

Provider
DeepSeek
License
Model License (open weights)
Context window
128K
Parameters
Reasoning MoE family
Architecture
Reasoning / RL-trained
Release
2025
Modalities
Text
Vision
No
Audio
No
Tool calling
Yes
Thinking
Yes
MCP
No
Open weights
Yes
API
Yes
Pricing (input)
DeepSeek API
Pricing (output)
DeepSeek API

Supported modalities

Text

Context window

128K (128,000 tokens)

Pricing

Input: DeepSeek API
Output: DeepSeek API

Availability

API: Yes
Chat UI: Yes
Open weights: Yes

Use cases

  • Hard math and science questions
  • Competitive programming style tasks
  • Chain-of-thought heavy research agents
  • Open reasoning baselines

Strengths

  • Strong reasoning traces and hard-problem performance
  • Open weights for research and self-hosting
  • Attractive cost for reasoning workloads

Limitations

  • Longer outputs / higher latency when thinking
  • Less multimodal than proprietary frontier models

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