The Open-Source AI Stack
RSS
All models

Models · deepseek

DeepSeek-V3

Open DeepSeek · · DeepSeek License

The cost-quality reset. Pretrained for a reported $5.6M on H800 GPUs (US-export-constrained silicon) and matched closed-frontier benchmarks. Triggered the "DeepSeek moment" in January 2025 when US markets re-priced AI capex assumptions.

Cost

$0.40 / Mtok input
$0.89 / Mtok output

DeepSeek API · as of 2026-05-21

via Artificial Analysis ↗

Speed

0 tok/sec output
0 ms TTFT

DeepSeek API · as of 2026-05-21

via Artificial Analysis ↗

Why people cared

DeepSeek V3 reset the cost-quality frontier on December 26, 2024, and is the model that the January 2025 "DeepSeek moment" was actually about. The technical report disclosed a reported $5.6M pretraining run on H800 GPUs (which were the export-controlled variant available to Chinese labs, not the H100s used by US frontier labs), and the resulting checkpoint matched closed-frontier scores on MMLU, GPQA-Diamond, and HumanEval. Three architectural innovations carried the story: Multi-head Latent Attention compressed KV-cache memory by ~93%, an auxiliary-loss-free load balancing mechanism kept MoE expert utilization smooth without the convergence problems earlier MoE work hit, and multi-token prediction during pretraining served as both a training-signal amplifier and a deployment-time speculative decoding accelerator. The economic argument that landed on Wall Street was that frontier capability had been reproduced for less than 1% of what US labs were widely reported to be spending on equivalent training runs. The model itself shipped with a custom DeepSeek License (not OSI-approved), but the technical report's level of detail set a new bar for what an open-weights frontier release should look like.

Architecture

tokens in Embedding vocab not disclosed · deepseek tokenizer × 61 layers Multi-head Latent Attention RoPE + YaRN context 128,000 tokens MoE Router 256 experts total · 8 active per token shown: 32 of 256 Output projection tokens out
Schema-generated from data/models.yaml. Every label is auditable against the model's sources.

Specs

Architecture
moe
Total params
671B
Active params
37B
Experts
256 total · 8 active
Context window
128K tokens
Attention
mla
Position encoding
rope-yarn
Pretraining tokens
14.8T
Training hardware
H800
Post-training
sft, grpo
OSI-approved
no
Data released
no
Training code
not released

Benchmarks

Each score carries the date it was published; we never infer or interpolate missing scores.

General reasoning

MMLU 87.1 as of 2024-12-27 source ↗
MMLU-Pro 75.2 as of 2026-05-21 source ↗
GPQA-Diamond 59.1 as of 2024-12-27 source ↗

Code

HumanEval 65.2 as of 2024-12-27 source ↗
LiveCodeBench 35.9 as of 2026-05-21 source ↗

Math

MATH 90.2 as of 2024-12-27 source ↗
AIME 2024 25.3 as of 2026-05-21 source ↗
AIME 2025 26.0 as of 2026-05-21 source ↗

Recommended use cases

  • frontier-quality chat at sub-$1/M output
  • long-context tasks
  • code assistance

Available quantizations

GGUF llama.cpp's container; the common local format, k-quants from Q2 to Q8. runs on llama.cpp, Ollama
AWQ Activation-aware 4-bit weight quantization for GPU serving. runs on vLLM, SGLang
GPTQ Post-training 4-bit weight quantization for GPU serving. runs on vLLM, SGLang, Transformers
MLX Apple MLX 4/8-bit layout for Apple silicon. runs on Apple MLX
FP8 8-bit float, frequently a native release on Hopper / Blackwell GPUs. runs on vLLM, SGLang, TensorRT-LLM

Verified via the Hugging Face model tree ↗. Community quantizations change over time; the families shown are those with published weights at audit time.

Notable innovations

  • · FP8 mixed-precision pretraining
  • · Auxiliary-loss-free load balancing
  • · Multi-token prediction

Known limitations

  • · DeepSeek License includes field-of-use restrictions including a ban on military use and on competing with DeepSeek's API service. source ↗
  • · 37B active parameters still require enough memory to load all 256 experts (~671B total). source ↗

Lineage

New pretrain using MLA and MoE architectures validated in DeepSeek-V2; base for the R1 reasoning model.

Derivatives

Sources