DeepSeek V4 is a public model family described by DeepSeek as a mixture-of-experts release with up to one million tokens of context support, including Pro and Flash variants.

What makes DeepSeek-V4-Pro notable?

The public model card highlights DeepSeek-V4-Pro for its large routed scale, long-context efficiency work, and three reasoning effort modes ranging from fast responses to Think Max.

Is this the official DeepSeek website?

No. This page is an independent static summary that links back to the original DeepSeek and Hugging Face references.

Where should I verify specs or benchmark tables?

Use the Hugging Face model card and linked DeepSeek technical report as the source of truth for current specs, weights, and benchmark details.

Independent Static Guide

DeepSeek V4, reduced to the signals that actually matter.

This page summarizes the public DeepSeek-V4-Pro model card into a clean keyword landing page: long context, routed scale, reasoning modes, and the links you need to verify the claims yourself.

Open on Hugging Face Visit DeepSeek

Public summary only. For exact weights, benchmark methodology, and runtime guidance, verify against the official model card and technical report.

Context Window: Up to 1M tokens
DeepSeek-V4-Pro: 1.6T total, 49B active
Reasoning Modes: Non-think, Think, Think Max
License: MIT

Model Snapshot

What the public release emphasizes.

Architecture Mixture-of-Experts

DeepSeek presents V4 as a routed model family rather than one dense monolith.

Efficiency Hybrid attention

The model card highlights long-context efficiency upgrades for the 1M-token setting.

Deployment Public weights + local inference docs

The release points to encoding and inference folders instead of treating the model as a black box.

Why DeepSeek V4

The release is about scale under control, not just bigger numbers.

Long-context design

DeepSeek frames V4 around million-token context handling, with the Pro model positioned for heavier long-range reasoning.

Routed capacity

Instead of presenting one always-on network, the public card stresses active parameters and expert routing as a core design choice.

Thinking budgets

Three reasoning effort modes let the same family swing from fast everyday responses to more deliberate Think Max behavior.

Operational transparency

The release links to downloads, local inference guidance, encoding examples, and benchmark tables rather than a single marketing promise.

Performance Snapshot

Compact benchmarks pulled from the public comparison table.

These figures summarize the DeepSeek-V4-Pro Max row from the public model card. Use the source page for full benchmark definitions and test setups.

LiveCodeBench 93.5

Pass@1 result in the published frontier-model comparison.

Codeforces 3206

Published rating figure for the max reasoning configuration.

MRCR 1M 83.5

Long-context score used to represent million-token retrieval performance.

SWE Verified 80.6

Resolved score reported in the agentic section of the model card.

Reasoning Modes

One family, three effort levels.

Non-think

Fast, direct answers for routine prompts and lower-risk tasks.

Think

Higher-latency reasoning mode for multi-step analysis, planning, and more careful synthesis.

Think Max

The public release frames this as the furthest-reasoning mode for extracting the strongest agentic and coding performance.

Local Use

What to open first

Model card for specs, tables, and download links.
encoding reference for OpenAI-style message formatting.
inference guidance for local runtime steps and recommended settings.

Published Guidance

Runtime note

The public instructions recommend temperature = 1.0 and top_p = 1.0, and suggest at least a 384K context window when using Think Max.

Frequently Asked Questions

Short answers before you leave the page.

Is this an official DeepSeek website?

No. It is an independent static reference page that organizes public DeepSeek-V4-Pro information into a faster overview.

Does this page host the model weights?

No. The downloads remain on the original distribution pages linked in the references section.

What should I trust for the latest benchmark numbers?

Use the Hugging Face model card and linked DeepSeek technical report, because those numbers can change as the release is updated.

What is this page optimized for?

Fast keyword discovery around “deepseek-v4”, strong first-screen clarity, and quick navigation to the official public references.

References

Go back to the source material.

Hugging Face model card Specs, downloads, benchmark tables, and runtime notes. DeepSeek website Primary company site and product entry point. DeepSeek chat Consumer-facing product entry linked from the model page. Technical report section Public summary heading for the V4 release narrative.