DeepSeekOpen Source

DeepSeek V4 Pro Base

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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

High-Quality JP

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

        ModelArenaSWEGPQA
        DeepSeek-V3.2-BaseN/AN/A (base)N/A (base)
        DeepSeek-V4-Flash-BaseN/AN/A (base)N/A (base)
        DeepSeek-V4-Pro-BaseN/AN/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.

        Analysis generated: 2026-07-17