A high-performance MoE foundation model developed by DeepSeek. It has 1.6T parameters (49B active) and handles a wide range of tasks.
Parameters
Undisclosed
Context Window
License
MIT
Release Date
2026-04-24
Benchmark Performance
AA Intelligence Index
—
LMArena Elo
—
HLE
—
ARC-AGI-2
—
SWE-bench Verified
—
GPQA Diamond
—
MMLU-Pro
—
LiveCodeBench
—
AIME 2025
—
MATH-500
—
Japanese Language Capability
Multilingual model with strong Japanese language processing capabilities.
API Pricing
API pricing for this model is not yet available
Strengths
Weaknesses
Use Cases
Deep Analysis
Total Parameters
1.6T
49B activated per token (MoE)
Context Window
1M tokens
Default, no premium tier
MMLU (5-shot)
90.1
vs V3.2-Base: 87.8
SimpleQA Verified
55.2%
25-shot, vs V3.2: 28.3%
LongBench-V2
51.5
1-shot, vs V3.2: 40.2
Input Price
$1.74/1M
via DeepInfra; $0.145 cached
Strengths
- ・Massive 1.6T-parameter MoE with only 49B active — frontier knowledge density at efficient inference cost
- ・Dramatic long-context efficiency: 27% FLOPs and 10% KV cache of V3.2 at 1M tokens via hybrid CSA/HCA attention
- ・MIT-licensed open weights enabling unrestricted fine-tuning, self-hosting, and commercial deployment
Weaknesses
- ・Base model only — requires post-training (SFT/RL) for instruction-following, chat, or agentic use cases
- ・Preview release status with limited BenchLM benchmark coverage (24 of 249 benchmarks); not yet fully evaluated
- ・Massive weight footprint (~862 GB FP4+FP8) demands enterprise-grade multi-GPU infrastructure for self-hosting
Competitor Comparison
| Model | Arena | SWE | GPQA |
|---|---|---|---|
| DeepSeek-V3.2-Base | N/A | N/A (base) | N/A (base) |
| DeepSeek-V4-Flash-Base | N/A | N/A (base) | N/A (base) |
| DeepSeek-V4-Pro-Base | N/A | N/A (base) | N/A (base) |
DeepSeek-V4-Pro-Base is the pre-trained foundation checkpoint of DeepSeek's flagship V4 series, released April 24, 2026 under the MIT license. As a 1.6-trillion-parameter Mixture-of-Experts model with 49 billion parameters activated per token, it represents the largest open-weight base model available and sets new performance ceilings across knowledge, reasoning, coding, and long-context benchmarks among DeepSeek foundation models. The model was pre-trained on over 33 trillion tokens using a novel architecture that combines Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA), achieving dramatic efficiency gains: at 1M-token context, it requires only 27% of the single-token inference FLOPs and 10% of the KV cache compared to its predecessor, DeepSeek-V3.2.
Key architectural innovations include Manifold-Constrained Hyper-Connections (mHC) for stable deep-layer signal propagation, the Muon optimizer for faster convergence, and FP4+FP8 mixed-precision training for MoE expert weights. The base model demonstrates comprehensive superiority over V3.2-Base across virtually all evaluated domains — achieving 90.1 on MMLU, 73.5 on MMLU-Pro, 76.8 on HumanEval, 92.6 on GSM8K, and 51.5 on LongBench-V2 — while operating with a significantly larger expert pool and supporting native 1M-token contexts.
As a base model, DeepSeek-V4-Pro-Base is designed for researchers and developers building custom post-training pipelines. It is not intended for direct conversational use; that role falls to the instruct variants (DeepSeek-V4-Pro with Non-think, Think High, and Think Max reasoning modes). The companion DeepSeek-V4-Flash-Base offers a more deployable alternative at 284B total parameters (13B active), achieving most of Pro's quality at roughly one-fifth the parameter count.
Sources
- DeepSeek V4 Technical Report (HuggingFace README)
- DeepSeek V4 Preview Release - DeepSeek API Docs
- BenchLM - DeepSeek V4 Pro Base Benchmarks
- DeepSeek V4 Pro Model Overview - DeepInfra
- DeepSeek V4 Technical Report (GitHub mirror)
- DeepSeek V4 Dell Enterprise Hub
- What Is DeepSeek V4? - DEV Community
- DeepSeek V4-Pro vs V4-Flash: Benchmarks & Guide - Lushbinary
Analysis generated: 2026-07-17