DeepSeek: DeepSeek V3 0324

deepseek/deepseek-chat-v3-0324

Created Mar 24, 202564,000 context
$0.27/M input tokens$1.1/M output tokens

DeepSeek V3, a 685B-parameter, mixture-of-experts model, is the latest iteration of the flagship chat model family from the DeepSeek team.

It succeeds the DeepSeek V3 model and performs really well on a variety of tasks.

Providers for DeepSeek V3 0324

OpenRouter routes requests to the best providers that are able to handle your prompt size and parameters, with fallbacks to maximize uptime.

DeepSeek
CN
fp8
Context
64K
Max Output
8K
Input
$0.27
Output
$1.1
DeepInfra
US
fp8
Context
164K
Max Output
164K
Input
$0.4
Output
$0.89
NovitaAI
US
Context
128K
Max Output
16K
Input
$0.4
Output
$1.3
inference.net
US
fp8
Context
128K
Max Output
33K
Input
$0.75
Output
$1.5
SambaNova
US
Context
8K
Max Output
8K
Input
$1
Output
$1.5
Parasail
US
fp8
Context
164K
Max Output
164K
Input
$1.15
Output
$1.2
Hyperbolic
US
fp8
Context
164K
Max Output
164K
Input
$1.25
Output
$1.25
kluster.ai
Context
64K
Max Output
64K
Input
$1.25
Output
$1.25

Apps using DeepSeek V3 0324

Top public apps this week using this model

1.
Cline
Autonomous coding agent right in your IDE
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2.
Roo Code
A whole dev team of AI agents in your editor
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3.
SillyTavern
LLM frontend for power users
1.88B tokens
4.
Chub AI
GenAI for everyone
528M tokens
5.
OpenRouter: Chatroom
Chat with multiple LLMs at once
411M tokens
6.
Aider
AI pair programming in your terminal
198M tokens
7.
Open WebUI
Extensible, self-hosted AI interface
170M tokens
8.
RA.Aid
new
100M tokens
9.
CHIM
AI framework for Skyrim
80.9M tokens
10.
FastLLM
new
71.2M tokens
11.
Agnaistic
A "bring your own AI" chat service
68.5M tokens
12.
liteLLM
Open-source library to simplify LLM calls
68.5M tokens
13.
CrushChat
new
66.9M tokens
14.
Chatbox AI
new
63.2M tokens

Recent activity on DeepSeek V3 0324

Tokens processed per day

Mar 24Mar 25Mar 26Mar 27Mar 28Mar 29Mar 30Mar 31Apr 101.5B3B4.5B6B

Versions by Token Share

Mar 25Mar 26Mar 27Mar 28Mar 29Mar 30Mar 31Apr 10%25%50%75%100%
Currently Viewing
DeepSeek: DeepSeek V3 0324
Created March 24, 2025131,072 context
24.8B tokens

Uptime stats for DeepSeek V3 0324

Uptime stats for DeepSeek V3 0324 across all providers

When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it.

Learn more about our load balancing and customization options.

Sample code and API for DeepSeek V3 0324

OpenRouter normalizes requests and responses across providers for you.

OpenRouter provides an OpenAI-compatible completion API to 300+ models & providers that you can call directly, or using the OpenAI SDK. Additionally, some third-party SDKs are available.

In the examples below, the OpenRouter-specific headers are optional. Setting them allows your app to appear on the OpenRouter leaderboards.

from openai import OpenAI

client = OpenAI(
  base_url="https://openrouter.ai/api/v1",
  api_key="<OPENROUTER_API_KEY>",
)

completion = client.chat.completions.create(
  extra_headers={
    "HTTP-Referer": "<YOUR_SITE_URL>", # Optional. Site URL for rankings on openrouter.ai.
    "X-Title": "<YOUR_SITE_NAME>", # Optional. Site title for rankings on openrouter.ai.
  },
  extra_body={},
  model="deepseek/deepseek-chat-v3-0324",
  messages=[
    {
      "role": "user",
      "content": "What is the meaning of life?"
    }
  ]
)
print(completion.choices[0].message.content)

Using third-party SDKs

For information about using third-party SDKs and frameworks with OpenRouter, please see our frameworks documentation.

See the Request docs for all possible fields, and Parameters for explanations of specific sampling parameters.

More models from DeepSeek

DeepSeek V3 Base

Note that this is a base model mostly meant for testing, you need to provide detailed prompts for the model to return useful responses.

DeepSeek-V3 Base is a 671B parameter open Mixture-of-Experts (MoE) language model with 37B active parameters per forward pass and a context length of 128K tokens. Trained on 14.8T tokens using FP8 mixed precision, it achieves high training efficiency and stability, with strong performance across language, reasoning, math, and coding tasks.

DeepSeek-V3 Base is the pre-trained model behind DeepSeek V3

DeepSeek V3 0324

DeepSeek V3, a 685B-parameter, mixture-of-experts model, is the latest iteration of the flagship chat model family from the DeepSeek team.

It succeeds the DeepSeek V3 model and performs really well on a variety of tasks.

DeepSeek R1 Zero

DeepSeek-R1-Zero is a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step. It's 671B parameters in size, with 37B active in an inference pass.

It demonstrates remarkable performance on reasoning. With RL, DeepSeek-R1-Zero naturally emerged with numerous powerful and interesting reasoning behaviors.

DeepSeek-R1-Zero encounters challenges such as endless repetition, poor readability, and language mixing. See DeepSeek R1 for the SFT model.

R1 Distill Llama 8B

DeepSeek R1 Distill Llama 8B is a distilled large language model based on Llama-3.1-8B-Instruct, using outputs from DeepSeek R1. The model combines advanced distillation techniques to achieve high performance across multiple benchmarks, including:

The model leverages fine-tuning from DeepSeek R1's outputs, enabling competitive performance comparable to larger frontier models.

Hugging Face:

R1 Distill Qwen 1.5B

DeepSeek R1 Distill Qwen 1.5B is a distilled large language model based on Qwen 2.5 Math 1.5B, using outputs from DeepSeek R1. It's a very small and efficient model which outperforms GPT 4o 0513 on Math Benchmarks.

Other benchmark results include:

The model leverages fine-tuning from DeepSeek R1's outputs, enabling competitive performance comparable to larger frontier models.

R1 Distill Qwen 32B

DeepSeek R1 Distill Qwen 32B is a distilled large language model based on Qwen 2.5 32B, using outputs from DeepSeek R1. It outperforms OpenAI's o1-mini across various benchmarks, achieving new state-of-the-art results for dense models.

Other benchmark results include:

The model leverages fine-tuning from DeepSeek R1's outputs, enabling competitive performance comparable to larger frontier models.

R1 Distill Qwen 14B

DeepSeek R1 Distill Qwen 14B is a distilled large language model based on Qwen 2.5 14B, using outputs from DeepSeek R1. It outperforms OpenAI's o1-mini across various benchmarks, achieving new state-of-the-art results for dense models.

Other benchmark results include:

The model leverages fine-tuning from DeepSeek R1's outputs, enabling competitive performance comparable to larger frontier models.

R1 Distill Llama 70B

DeepSeek R1 Distill Llama 70B is a distilled large language model based on Llama-3.3-70B-Instruct, using outputs from DeepSeek R1. The model combines advanced distillation techniques to achieve high performance across multiple benchmarks, including:

The model leverages fine-tuning from DeepSeek R1's outputs, enabling competitive performance comparable to larger frontier models.

R1

DeepSeek R1 is here: Performance on par with OpenAI o1, but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inference pass.

Fully open-source model & technical report.

MIT licensed: Distill & commercialize freely!

DeepSeek V3

DeepSeek-V3 is the latest model from the DeepSeek team, building upon the instruction following and coding abilities of the previous versions. Pre-trained on nearly 15 trillion tokens, the reported evaluations reveal that the model outperforms other open-source models and rivals leading closed-source models.

For model details, please visit the DeepSeek-V3 repo for more information, or see the launch announcement.

DeepSeek V2.5

DeepSeek-V2.5 is an upgraded version that combines DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct. The new model integrates the general and coding abilities of the two previous versions. For model details, please visit DeepSeek-V2 page for more information.

DeepSeek-Coder-V2

DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model. It is further pre-trained from an intermediate checkpoint of DeepSeek-V2 with additional 6 trillion tokens.

The original V1 model was trained from scratch on 2T tokens, with a composition of 87% code and 13% natural language in both English and Chinese. It was pre-trained on project-level code corpus by employing a extra fill-in-the-blank task.

    DeepSeek V3 0324 - API, Providers, Stats | OpenRouter