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Model details

DeepSeek-R1

DeepSeek-R1 is a specialized language model engineered to excel at complex reasoning, math, and coding tasks. By prioritizing long-context reasoning capabilities in both English and Chinese, the model is designed to handle intricate problem-solving scenarios that require deep logical processing. It serves as a powerful foundation for users seeking a model that can compete with top-tier reasoning systems while maintaining accessibility for a wide range of academic and commercial applications.

The model is built upon a foundation of large-scale reinforcement learning, which allows it to achieve significant performance gains with minimal reliance on human-labeled data. This post-training approach enables the model to refine its reasoning processes effectively. Beyond the primary model, the ecosystem includes a series of distilled versions that bring these advanced reasoning capabilities to smaller parameter scales, providing flexible options for developers. With its open-source license, the model empowers the community to leverage its weights and outputs for further fine-tuning and specialized development.

Azuredeepseek-r1deepseek-thinkingdeprecated

Quick Info

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Provider
Azure
Model key
deepseek-r1
Release date
Jan 20, 2025
Last updated
Jan 20, 2025
Knowledge cutoff
2024-07
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$1.35
Output token cost
$5.40

Limits

Output tokens
163,840 tokens
Context window
163,840 tokens

Transparent token rates

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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Latest news about DeepSeek-R1

Vercel AI Gateway

CoverageBenchmark

A peer-reviewed meta-analysis published in the Journal of Big Data (Volume 13, article 26, 2026; published 19 December 2025) directly benchmarks DeepSeek-R1 against GPT-4 Turbo, Gemini Ultra, Qwen, and LLaMA 3.1 using standardized tasks including MMLU, HumanEval, FLORES-200, and TyDiQA. The hybrid meta-analysis aggrega Reported results for DeepSeek-R1 include 80.2 ± 1.5% on HumanEval and 78.5 ± 1.8% on MMLU, compared with ChatGPT-4 Turbo's 86.5 ± 1.9% on HumanEval, with the gap falling within observed heterogeneity (I² = 14.6%). The study concludes R1 demonstrates strong coding and multilingual efficiency, trails GPT-4 Turbo in reaso

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