DeepSeek: R1

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DeepSeek R1 is DeepSeek's first-generation reasoning model released January 2025, trained via large-scale reinforcement learning to achieve performance comparable to OpenAI o1 on math, code, and reasoning tasks. It pioneered open-source reasoning capabilities with self-verification and reflection behaviors.

Context Window 164K

tokens

Max Output 164K

tokens

Input Cost $0.5

per million tokens

Output Cost $2.15

per million tokens

Release Date Jan 20, 2025

Code Example

Add AI to your app with the Puter.js AI API, no API keys or setup required.

// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';

puter.ai.chat("Explain quantum computing in simple terms").then(response => {
    document.body.innerHTML = response.message.content;
});

<html>
<body>
    <script src="https://js.puter.com/v2/"></script>
    <script>
        puter.ai.chat("Explain quantum computing in simple terms").then(response => {
            document.body.innerHTML = response.message.content;
        });
    </script>
</body>
</html>

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DeepSeek V4.1 Flash

DeepSeek V4.1 Flash is DeepSeek's fast, low-cost chat model, a 552B-parameter Mixture-of-Experts system using a new causal encoder-decoder design that activates 8B parameters while reading a prompt and 16B while generating a response. It succeeds DeepSeek V4 Flash, keeping a 1,000,000-token context window and adding a continuously adjustable reasoning effort setting (1-100). DeepSeek reports it beating the larger V4 Pro on performance, cost, speed, and total runtime. In DeepSeek's own tests it scored 90.6 on Terminal-Bench 2.1, ahead of Claude Opus 5 (89.1) and GPT-5.6 Sol (88.8), 88.1 on CyberGym versus 84.5 for GPT-5.6 Sol and GLM 5.3, and 74.2 on DeepSWE v1.1 versus 74.0 for Claude Opus 5. It supports tool calling, and DeepSeek says its KV cache footprint is about a quarter of V4 Flash's for cheaper long-context serving. At $0.11/M input and $0.45/M output tokens, it fits high-throughput coding agents, tool-calling pipelines, and long-context workloads.

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DeepSeek V4 Flash Vision Experimental

DeepSeek V4 Flash Vision Experimental is an experimental multimodal version of DeepSeek's V4 Flash model, released by DeepSeek in August 2026. It is a sparse Mixture-of-Experts model with 284B total parameters and 13B activated per token, and it accepts images (JPEG, PNG, GIF, WebP) alongside text through the same API used for V4 Flash. DeepSeek reports that it matches V4 Flash on text tasks including agentic reasoning and world knowledge, while adding image understanding for document and chart parsing, visual question answering, and agent workflows that interleave text and images. On multimodal agent benchmarks, DeepSeek's own numbers show gains over the text-only V4 Flash, with Terminal Bench 2.1 rising from 82.7 to 83.9, ApexBench from 26.2 to 36.5, and Agents' Last Exam from 25.2 to 27.3, approaching Anthropic's Opus 4.8 (85.0 on Terminal Bench 2.1). On NL2Repo it trails Opus 4.8, 57.7 versus 69.7.

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DeepSeek V4 Pro 0813

DeepSeek V4 Pro 0813 is the production release of DeepSeek's V4 Pro flagship model, a 1.6-trillion-parameter Mixture-of-Experts model with 49B parameters active per token, marking the end of a four-month preview. It keeps the preview's 1,048,576-token context window, 384,000-token max output, and three reasoning modes (non-thinking, high, and max effort). DeepSeek reports large gains over the preview build: Terminal Bench 2.1 rose from 72.1 to 87.9 and DeepSWE from 12.8 to 62.7. At max reasoning effort it scores 80.6% on SWE-bench Verified, matching Gemini 3.1 Pro, along with 90.1% on GPQA Diamond, 87.5% on MMLU-Pro, and a Codeforces rating of 3,206, ahead of GPT-5.4's 3,168. Priced at $0.38 per million input tokens and $1.14 per million output tokens. Pinning the 0813 route locks an application to this exact checkpoint rather than the rolling deepseek-v4-pro alias, useful for agentic coding and long-context reasoning tasks.

Frequently Asked Questions

How do I use R1?

R1 is no longer available through Puter.js. Explore other AI models for alternatives.

What is the pricing for R1?

R1 costs $0.5 per 1M input tokens and $2.15 per 1M output tokens.

	Price per 1M tokens
Input	$0.5
Output	$2.15

Who created R1?

R1 was created by DeepSeek and released on Jan 20, 2025.

What is the context window of R1?

R1 supports a context window of 164K tokens. For reference, that is roughly equivalent to 328 pages of text.

What is the max output length of R1?

R1 can generate up to 164K tokens in a single response.

How does R1 perform on benchmarks?

R1 scores 13.1 on the Artificial Analysis Intelligence Index, outperforming 52% of tracked models. On math, it scores 76.0 (outperforms 71% of models).

Does it work with React / Vue / Vanilla JS / Node / etc.?

Yes — the R1 API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.

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