DeepSeek AI is winning. Incredibly inexpensive to operate with API at higher levels than the rest…

www.deepseekv3.com/en [register (api)]

www.deepseekv3.com/en/chat [try without reg (chatbot)]

DeepSeek model has been upgraded to V3! Until 16:00 on February 8, 2025 (UTC), all calls will be charged at the discounted historical rate. After the promotional period ends, the DeepSeek-V3 API pricing will be adjusted, and future calls will be automatically billed at the new rate.

This pricing structure outlines the costs associated with using the DeepSeek AI model, specifically the deepseek-chat model. Here’s a breakdown of the key components and what they mean:

Model Specifications

Context Length: The deepseek-chat model can process up to 64K tokens in a single context.Maximum Output Tokens: The model can generate up to 8K tokens in response. By default, it’s set to 4K tokens, but users can adjust this up to 8K if needed.

Pricing Structure

The pricing is based on the number of tokens processed, with separate rates for input and output tokens. Prices are listed in USD and CNY per 1 million tokens.
Input Pricing: Cache Hit: $0.014 per 1M tokens | Cache Miss: $0.14 per 1M tokens

Output Pricing: $0.28 per 1M tokens (discounted from $1.10)

Context Caching

The pricing differentiates between “cache hit” and “cache miss” for input tokens:

A cache hit occurs when the input has been processed before, resulting in a lower price.
A cache miss happens when the input is new, incurring a higher price.

Discounted Pricing

Currently, DeepSeek is offering discounted prices until February 8, 2025, at 16:00 UTC. After this date, prices will return to their original, higher rates.

Billing and Deduction Rules

Costs are calculated by multiplying the number of tokens by the respective price.
Fees are deducted from the user’s account balance.
If both topped-up and granted balances are available, the system prioritizes using the granted balance first.

Important Notes

Prices are subject to change, and DeepSeek reserves the right to adjust them.
Users are advised to top up based on actual usage and check the pricing page regularly for updates.
The deepseek-chat model has been upgraded to DeepSeek-V3, which may offer improved performance or features.

This pricing structure allows for flexible usage of the AI model, with costs directly tied to the amount of processing required for both input and output. The discounted rates currently offered provide an opportunity for users to utilize the service at a lower cost until early 2025.

This is a Chinese company that created these models. You are able to pay for API access using PayPal..

We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities. Comprehensive evaluations reveal that DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models. Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training. In addition, its training process is remarkably stable. Throughout the entire training process, we did not experience any irrecoverable loss spikes or perform any rollbacks.

2. Model Summary

Architecture: Innovative Load Balancing Strategy and Training Objective

On top of the efficient architecture of DeepSeek-V2, we pioneer an auxiliary-loss-free strategy for load balancing, which minimizes the performance degradation that arises from encouraging load balancing.
We investigate a Multi-Token Prediction (MTP) objective and prove it beneficial to model performance. It can also be used for speculative decoding for inference acceleration.

Pre-Training: Towards Ultimate Training Efficiency

We design an FP8 mixed precision training framework and, for the first time, validate the feasibility and effectiveness of FP8 training on an extremely large-scale model.
Through co-design of algorithms, frameworks, and hardware, we overcome the communication bottleneck in cross-node MoE training, nearly achieving full computation-communication overlap.
This significantly enhances our training efficiency and reduces the training costs, enabling us to further scale up the model size without additional overhead.
At an economical cost of only 2.664M H800 GPU hours, we complete the pre-training of DeepSeek-V3 on 14.8T tokens, producing the currently strongest open-source base model. The subsequent training stages after pre-training require only 0.1M GPU hours.

Post-Training: Knowledge Distillation from DeepSeek-R1

We introduce an innovative methodology to distill reasoning capabilities from the long-Chain-of-Thought (CoT) model, specifically from one of the DeepSeek R1 series models, into standard LLMs, particularly DeepSeek-V3. Our pipeline elegantly incorporates the verification and reflection patterns of R1 into DeepSeek-V3 and notably improves its reasoning performance. Meanwhile, we also maintain a control over the output style and length of DeepSeek-V3.

3. Model Downloads

Model	#Total Params	#Activated Params	Context Length	Download
DeepSeek-V3-Base	671B	37B	128K	🤗 Hugging Face
DeepSeek-V3	671B	37B	128K	🤗 Hugging Face

Note: The total size of DeepSeek-V3 models on Hugging Face is 685B, which includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights.**

To ensure optimal performance and flexibility, we have partnered with open-source communities and hardware vendors to provide multiple ways to run the model locally. For step-by-step guidance, check out Section 6: How_to Run_Locally.

For developers looking to dive deeper, we recommend exploring README_WEIGHTS.md for details on the Main Model weights and the Multi-Token Prediction (MTP) Modules. Please note that MTP support is currently under active development within the community, and we welcome your contributions and feedback.

4. Evaluation Results

Base Model – Standard Benchmarks

	Benchmark (Metric)	# Shots	DeepSeek-V2	Qwen2.5 72B	LLaMA3.1 405B	DeepSeek-V3
	Architecture	–	MoE	Dense	Dense	MoE
	# Activated Params	–	21B	72B	405B	37B
	# Total Params	–	236B	72B	405B	671B
English	Pile-test (BPB)	–	0.606	0.638	0.542	0.548
	BBH (EM)	3-shot	78.8	79.8	82.9	87.5
	MMLU (Acc.)	5-shot	78.4	85.0	84.4	87.1
	MMLU-Redux (Acc.)	5-shot	75.6	83.2	81.3	86.2
	MMLU-Pro (Acc.)	5-shot	51.4	58.3	52.8	64.4
	DROP (F1)	3-shot	80.4	80.6	86.0	89.0
	ARC-Easy (Acc.)	25-shot	97.6	98.4	98.4	98.9
	ARC-Challenge (Acc.)	25-shot	92.2	94.5	95.3	95.3
	HellaSwag (Acc.)	10-shot	87.1	84.8	89.2	88.9
	PIQA (Acc.)	0-shot	83.9	82.6	85.9	84.7
	WinoGrande (Acc.)	5-shot	86.3	82.3	85.2	84.9
	RACE-Middle (Acc.)	5-shot	73.1	68.1	74.2	67.1
	RACE-High (Acc.)	5-shot	52.6	50.3	56.8	51.3
	TriviaQA (EM)	5-shot	80.0	71.9	82.7	82.9
	NaturalQuestions (EM)	5-shot	38.6	33.2	41.5	40.0
	AGIEval (Acc.)	0-shot	57.5	75.8	60.6	79.6
Code	HumanEval (Pass@1)	0-shot	43.3	53.0	54.9	65.2
	MBPP (Pass@1)	3-shot	65.0	72.6	68.4	75.4
	LiveCodeBench-Base (Pass@1)	3-shot	11.6	12.9	15.5	19.4
	CRUXEval-I (Acc.)	2-shot	52.5	59.1	58.5	67.3
	CRUXEval-O (Acc.)	2-shot	49.8	59.9	59.9	69.8
Math	GSM8K (EM)	8-shot	81.6	88.3	83.5	89.3
	MATH (EM)	4-shot	43.4	54.4	49.0	61.6
	MGSM (EM)	8-shot	63.6	76.2	69.9	79.8
	CMath (EM)	3-shot	78.7	84.5	77.3	90.7
Chinese	CLUEWSC (EM)	5-shot	82.0	82.5	83.0	82.7
	C-Eval (Acc.)	5-shot	81.4	89.2	72.5	90.1
	CMMLU (Acc.)	5-shot	84.0	89.5	73.7	88.8
	CMRC (EM)	1-shot	77.4	75.8	76.0	76.3
	C3 (Acc.)	0-shot	77.4	76.7	79.7	78.6
	CCPM (Acc.)	0-shot	93.0	88.5	78.6	92.0
Multilingual	MMMLU-non-English (Acc.)	5-shot	64.0	74.8	73.8	79.4

Note: Best results are shown in bold. Scores with a gap not exceeding 0.3 are considered to be at the same level. DeepSeek-V3 achieves the best performance on most benchmarks, especially on math and code tasks. For more evaluation details, please check our paper.

Context Window

Evaluation results on the Needle In A Haystack (NIAH) tests. DeepSeek-V3 performs well across all context window lengths up to 128K.

Chat Model – Standard Benchmarks (Models larger than 67B)

	Benchmark (Metric)	DeepSeek V2-0506	DeepSeek V2.5-0905	Qwen2.5 72B-Inst.	Llama3.1 405B-Inst.	Claude-3.5-Sonnet-1022	GPT-4o 0513	DeepSeek V3
	Architecture	MoE	MoE	Dense	Dense	–	–	MoE
	# Activated Params	21B	21B	72B	405B	–	–	37B
	# Total Params	236B	236B	72B	405B	–	–	671B
English	MMLU (EM)	78.2	80.6	85.3	88.6	88.3	87.2	88.5
	MMLU-Redux (EM)	77.9	80.3	85.6	86.2	88.9	88.0	89.1
	MMLU-Pro (EM)	58.5	66.2	71.6	73.3	78.0	72.6	75.9
	DROP (3-shot F1)	83.0	87.8	76.7	88.7	88.3	83.7	91.6
	IF-Eval (Prompt Strict)	57.7	80.6	84.1	86.0	86.5	84.3	86.1
	GPQA-Diamond (Pass@1)	35.3	41.3	49.0	51.1	65.0	49.9	59.1
	SimpleQA (Correct)	9.0	10.2	9.1	17.1	28.4	38.2	24.9
	FRAMES (Acc.)	66.9	65.4	69.8	70.0	72.5	80.5	73.3
	LongBench v2 (Acc.)	31.6	35.4	39.4	36.1	41.0	48.1	48.7
Code	HumanEval-Mul (Pass@1)	69.3	77.4	77.3	77.2	81.7	80.5	82.6
	LiveCodeBench (Pass@1-COT)	18.8	29.2	31.1	28.4	36.3	33.4	40.5
	LiveCodeBench (Pass@1)	20.3	28.4	28.7	30.1	32.8	34.2	37.6
	Codeforces (Percentile)	17.5	35.6	24.8	25.3	20.3	23.6	51.6
	SWE Verified (Resolved)	–	22.6	23.8	24.5	50.8	38.8	42.0
	Aider-Edit (Acc.)	60.3	71.6	65.4	63.9	84.2	72.9	79.7
	Aider-Polyglot (Acc.)	–	18.2	7.6	5.8	45.3	16.0	49.6
Math	AIME 2024 (Pass@1)	4.6	16.7	23.3	23.3	16.0	9.3	39.2
	MATH-500 (EM)	56.3	74.7	80.0	73.8	78.3	74.6	90.2
	CNMO 2024 (Pass@1)	2.8	10.8	15.9	6.8	13.1	10.8	43.2
Chinese	CLUEWSC (EM)	89.9	90.4	91.4	84.7	85.4	87.9	90.9
	C-Eval (EM)	78.6	79.5	86.1	61.5	76.7	76.0	86.5
	C-SimpleQA (Correct)	48.5	54.1	48.4	50.4	51.3	59.3	64.8

Note: All models are evaluated in a configuration that limits the output length to 8K. Benchmarks containing fewer than 1000 samples are tested multiple times using varying temperature settings to derive robust final results. DeepSeek-V3 stands as the best-performing open-source model, and also exhibits competitive performance against frontier closed-source models.

Open Ended Generation Evaluation

Model	Arena-Hard	AlpacaEval 2.0
DeepSeek-V2.5-0905	76.2	50.5
Qwen2.5-72B-Instruct	81.2	49.1
LLaMA-3.1 405B	69.3	40.5
GPT-4o-0513	80.4	51.1
Claude-Sonnet-3.5-1022	85.2	52.0
DeepSeek-V3	85.5	70.0

Note: English open-ended conversation evaluations. For AlpacaEval 2.0, we use the length-controlled win rate as the metric.

5. Chat Website & API Platform

You can chat with DeepSeek-V3 on DeepSeek’s official website: chat.deepseek.com

We also provide OpenAI-Compatible API at DeepSeek Platform: platform.deepseek.com

6. How to Run Locally

DeepSeek-V3 can be deployed locally using the following hardware and open-source community software:

DeepSeek-Infer Demo: We provide a simple and lightweight demo for FP8 and BF16 inference.
SGLang: Fully support the DeepSeek-V3 model in both BF16 and FP8 inference modes, with Multi-Token Prediction coming soon.
LMDeploy: Enables efficient FP8 and BF16 inference for local and cloud deployment.
TensorRT-LLM: Currently supports BF16 inference and INT4/8 quantization, with FP8 support coming soon.
vLLM: Support DeepSeek-V3 model with FP8 and BF16 modes for tensor parallelism and pipeline parallelism.
AMD GPU: Enables running the DeepSeek-V3 model on AMD GPUs via SGLang in both BF16 and FP8 modes.
Huawei Ascend NPU: Supports running DeepSeek-V3 on Huawei Ascend devices.

Since FP8 training is natively adopted in our framework, we only provide FP8 weights. If you require BF16 weights for experimentation, you can use the provided conversion script to perform the transformation.

Here is an example of converting FP8 weights to BF16:

cd inference
python fp8_cast_bf16.py --input-fp8-hf-path /path/to/fp8_weights --output-bf16-hf-path /path/to/bf16_weights

Note: Hugging Face’s Transformers has not been directly supported yet.

6.1 Inference with DeepSeek-Infer Demo (example only)

Model Weights & Demo Code Preparation

First, clone our DeepSeek-V3 GitHub repository:

git clone https://github.com/deepseek-ai/DeepSeek-V3.git

Navigate to the inference folder and install dependencies listed in requirements.txt.

cd DeepSeek-V3/inference
pip install -r requirements.txt

Download the model weights from Hugging Face, and put them into /path/to/DeepSeek-V3 folder.

Model Weights Conversion

Convert Hugging Face model weights to a specific format:

python convert.py --hf-ckpt-path /path/to/DeepSeek-V3 --save-path /path/to/DeepSeek-V3-Demo --n-experts 256 --model-parallel 16

Run

Then you can chat with DeepSeek-V3:

torchrun --nnodes 2 --nproc-per-node 8 --node-rank $RANK --master-addr $ADDR generate.py --ckpt-path /path/to/DeepSeek-V3-Demo --config configs/config_671B.json --interactive --temperature 0.7 --max-new-tokens 200

Or batch inference on a given file:

torchrun --nnodes 2 --nproc-per-node 8 --node-rank $RANK --master-addr $ADDR generate.py --ckpt-path /path/to/DeepSeek-V3-Demo --config configs/config_671B.json --input-file $FILE

6.2 Inference with SGLang (recommended)

SGLang currently supports MLA optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, delivering state-of-the-art latency and throughput performance among open-source frameworks.

Notably, SGLang v0.4.1 fully supports running DeepSeek-V3 on both NVIDIA and AMD GPUs, making it a highly versatile and robust solution.

SGLang also supports multi-node tensor parallelism, enabling you to run this model on multiple network-connected machines.

Multi-Token Prediction (MTP) is in development, and progress can be tracked in the optimization plan.

Here are the launch instructions from the SGLang team: https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3

6.3 Inference with LMDeploy (recommended)

LMDeploy, a flexible and high-performance inference and serving framework tailored for large language models, now supports DeepSeek-V3. It offers both offline pipeline processing and online deployment capabilities, seamlessly integrating with PyTorch-based workflows.

For comprehensive step-by-step instructions on running DeepSeek-V3 with LMDeploy, please refer to here: InternLM/lmdeploy#2960

6.4 Inference with TRT-LLM (recommended)

TensorRT-LLM now supports the DeepSeek-V3 model, offering precision options such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in progress and will be released soon. You can access the custom branch of TRTLLM specifically for DeepSeek-V3 support through the following link to experience the new features directly: https://github.com/NVIDIA/TensorRT-LLM/tree/deepseek/examples/deepseek_v3.

6.5 Inference with vLLM (recommended)

vLLM v0.6.6 supports DeepSeek-V3 inference for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from standard techniques, vLLM offers pipeline parallelism allowing you to run this model on multiple machines connected by networks. For detailed guidance, please refer to the vLLM instructions. Please feel free to follow the enhancement plan as well.

6.6 Recommended Inference Functionality with AMD GPUs

In collaboration with the AMD team, we have achieved Day-One support for AMD GPUs using SGLang, with full compatibility for both FP8 and BF16 precision. For detailed guidance, please refer to the SGLang instructions.

6.7 Recommended Inference Functionality with Huawei Ascend NPUs

The MindIE framework from the Huawei Ascend community has successfully adapted the BF16 version of DeepSeek-V3. For step-by-step guidance on Ascend NPUs, please follow the instructions here.

7. License

This code repository is licensed under the MIT License. The use of DeepSeek-V3 Base/Chat models is subject to the Model License. DeepSeek-V3 series (including Base and Chat) supports commercial use.