DeepSeek V4 Pro Complete Guide: 1.6T Parameters, 80.6% SWE-bench, Open Source (2026)
DeepSeek V4 Pro landed on April 24, 2026, and it rewrites the rules for open-source AI. This is a 1.6 trillion parameter Mixture-of-Experts model with 49 billion active parameters per forward pass, a 1 million token context window, and an MIT license that lets you do whatever you want with it.
The numbers speak for themselves: 80.6% on SWE-bench Verified, 93.5% on LiveCodeBench, and a Codeforces rating of 3206 that places it 23rd among all human competitors. It matches or beats every closed-source frontier model on coding and math while costing a fraction of the price.
This guide covers the full picture: architecture, benchmarks, pricing, API setup, agentic coding workflows, and where V4 Pro still falls short. If you want the lighter variant, check out our DeepSeek V4 Flash guide.
What is DeepSeek V4 Pro?
DeepSeek V4 Pro is the flagship model in the V4 family from DeepSeek, a Chinese AI lab based in Hangzhou. It is a text-only, decoder-only transformer built on a Mixture-of-Experts architecture. The model ships under the MIT license, meaning there are zero restrictions on commercial use, fine-tuning, or redistribution.
Key specs at a glance:
- Total parameters: 1.6 trillion
- Active parameters per token: 49 billion
- Context window: 1,000,000 tokens
- Training data: 33 trillion tokens
- License: MIT (fully open)
- Release date: April 24, 2026
V4 Pro sits at the top of the DeepSeek V4 lineup. The V4 Flash variant offers a smaller, faster alternative for latency-sensitive workloads. For API-specific details, see our DeepSeek V4 API guide.
Architecture deep dive
DeepSeek V4 Pro builds on the MoE foundation from V3 but introduces several architectural innovations that push efficiency and quality forward simultaneously.
Core transformer structure
The model uses 61 transformer layers with a hidden dimension of 7168. Each MoE layer contains 384 routed experts plus 1 shared expert, with 6 experts active per token. The shared expert processes every token, providing a stable baseline representation, while the router selects 6 task-specific experts from the pool of 384.
This design means only 49B of the 1.6T total parameters fire on any given token, keeping inference costs manageable despite the massive parameter count.
Hybrid CSA + HCA attention
The biggest architectural change from V3.2 is the hybrid attention mechanism combining Compressed Sparse Attention (CSA) and Hierarchical Chunked Attention (HCA). At the 1 million token context length, this hybrid approach uses only 27% of the FLOPs and 10% of the KV cache compared to V3.2’s standard attention.
This is what makes the 1M context window practical. Without the hybrid attention, serving a 1M context model at this scale would be prohibitively expensive. The CSA layers handle local patterns efficiently while HCA layers capture long-range dependencies through a hierarchical chunking strategy.
Manifold-Constrained Hyper-Connections
V4 Pro introduces Manifold-Constrained Hyper-Connections, a new residual connection design that replaces standard skip connections. Instead of simple additive residuals, hyper-connections route information through learned manifold projections between layers. This improves gradient flow during training and gives the model better control over how information propagates through the 61-layer stack.
Training details
- Optimizer: Muon optimizer, replacing AdamW from V3. Muon provides better convergence on MoE architectures by handling the sparse gradient patterns more effectively.
- Precision: FP4 + FP8 mixed precision training. Weights are stored in FP4 during forward passes with FP8 accumulation, cutting memory requirements roughly in half compared to BF16 training.
- Data: 33 trillion tokens across a multilingual corpus with heavy emphasis on code, math, and scientific text.
Three reasoning modes
DeepSeek V4 Pro supports three distinct reasoning modes that let you trade off between speed and depth of reasoning.
Non-think mode
The default mode. The model responds directly without explicit chain-of-thought reasoning. Best for simple queries, chat, summarization, and tasks where latency matters more than deep analysis.
Think High mode
Enables extended chain-of-thought reasoning. The model generates internal reasoning steps before producing its final answer. This improves performance on math, coding, and complex analytical tasks at the cost of higher token usage and latency.
To activate Think High, include the following in your system prompt:
Please think step by step before answering.Think Max mode
The most powerful reasoning mode. Think Max uses a special system prompt that instructs the model to perform exhaustive multi-step reasoning with self-verification. This is the mode used for benchmark evaluations.
To activate Think Max, use this system prompt:
You are DeepSeek V4 Pro in maximum reasoning mode. For every problem:
1. Break it into sub-problems
2. Solve each sub-problem with detailed reasoning
3. Verify each step before proceeding
4. Cross-check your final answer against the original problem
Take as much space as you need. Accuracy matters more than brevity.Think Max produces the best results on hard benchmarks but uses significantly more output tokens. For most production workloads, Think High offers the best quality-to-cost ratio.
Benchmarks
All scores below are for V4 Pro in Think Max mode unless noted. Competing models are also in their strongest reasoning configurations.
Coding benchmarks
| Benchmark | V4-Pro-Max | Opus 4.6 | GPT-5.4 | Gemini 3.1 Pro | K2.6 | GLM-5.1 |
|---|---|---|---|---|---|---|
| SWE-bench Verified | 80.6% | 79.8% | 78.2% | 75.1% | 74.3% | 71.9% |
| SWE-bench Pro | 55.4% | 54.1% | 52.8% | 49.6% | 48.2% | 45.7% |
| Terminal-Bench | 67.9% | 66.3% | 69.4% | 64.8% | 62.1% | 59.5% |
| LiveCodeBench | 93.5% | 91.2% | 90.8% | 88.4% | 87.1% | 84.6% |
| Codeforces (rating) | 3206 | 3104 | 3089 | 2945 | 2878 | 2756 |
V4 Pro leads on SWE-bench Verified, SWE-bench Pro, LiveCodeBench, and Codeforces. GPT-5.4 edges it out on Terminal-Bench by 1.5 points. For a detailed comparison, see DeepSeek V4 vs GPT-5.5 and DeepSeek V4 vs Claude Opus 4.6.
Math benchmarks
| Benchmark | V4-Pro-Max | Opus 4.6 | GPT-5.4 | Gemini 3.1 Pro | K2.6 | GLM-5.1 |
|---|---|---|---|---|---|---|
| AIME 2026 | 94.3% | 92.1% | 91.7% | 89.5% | 88.2% | 85.4% |
| HMMT | 95.2% | 93.8% | 92.4% | 90.1% | 89.6% | 86.3% |
| IMOAnswerBench | 89.8% | 87.4% | 86.9% | 84.2% | 83.1% | 79.8% |
V4 Pro sweeps the math benchmarks. The Codeforces rating of 3206 places it 23rd among all human competitors on the platform, a first for any AI model.
Knowledge and reasoning benchmarks
| Benchmark | V4-Pro-Max | Opus 4.6 | GPT-5.4 | Gemini 3.1 Pro | K2.6 | GLM-5.1 |
|---|---|---|---|---|---|---|
| MMLU-Pro | 87.5% | 88.1% | 89.2% | 89.2% | 86.4% | 85.1% |
| GPQA Diamond | 90.1% | 89.3% | 88.7% | 87.9% | 86.5% | 84.2% |
| HLE | 37.7% | 39.2% | 38.4% | 36.8% | 35.1% | 33.6% |
Knowledge benchmarks are more mixed. V4 Pro leads on GPQA Diamond but trails GPT-5.4 and Gemini 3.1 Pro on MMLU-Pro, and Opus 4.6 on HLE.
Tool use and agentic benchmarks
| Benchmark | V4-Pro-Max | Opus 4.6 | GPT-5.4 | Gemini 3.1 Pro | K2.6 | GLM-5.1 |
|---|---|---|---|---|---|---|
| MCPAtlas | 73.6% | 72.1% | 71.8% | 70.4% | 68.9% | 66.2% |
| Toolathlon | 51.8% | 53.4% | 52.1% | 50.7% | 49.3% | 47.1% |
V4 Pro leads on MCPAtlas but Opus 4.6 takes the top spot on Toolathlon. Tool use remains an area where all models have significant room to improve.
Pricing
DeepSeek V4 Pro is one of the cheapest frontier models to run through the API. For full pricing comparisons across providers, see our AI API pricing compared breakdown.
| Token type | Price per 1M tokens |
|---|---|
| Input (cache miss) | $1.74 |
| Input (cache hit) | $0.145 |
| Output | $3.48 |
The cache hit price of $0.145 per million tokens is remarkably low. If your workload involves repeated system prompts or shared context prefixes, you can cut input costs by over 90% through prompt caching.
For comparison, GPT-5.4 charges roughly 8x more for output tokens, and Opus 4.6 charges about 5x more. The cost advantage is one of V4 Pro’s strongest selling points, especially for high-volume agentic workloads.
API setup
DeepSeek V4 Pro uses an OpenAI-compatible API, so you can swap it into any existing OpenAI SDK integration with minimal changes.
Python example
from openai import OpenAI
client = OpenAI(
api_key="your-deepseek-api-key",
base_url="https://api.deepseek.com/v1"
)
response = client.chat.completions.create(
model="deepseek-v4-pro",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Explain the difference between CSA and HCA attention."}
],
max_tokens=4096
)
print(response.choices[0].message.content)cURL example
curl https://api.deepseek.com/v1/chat/completions \
-H "Authorization: Bearer $DEEPSEEK_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-v4-pro",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Write a Python function to merge two sorted lists."}
],
"max_tokens": 2048
}'The model ID is deepseek-v4-pro. For Think High mode, adjust your system prompt as described above. For local deployment options, see How to run DeepSeek V4 locally.
Agentic coding
V4 Pro works as a drop-in backend for several popular agentic coding tools:
Claude Code
You can point Claude Code at the DeepSeek API by setting the provider configuration:
export ANTHROPIC_BASE_URL=https://api.deepseek.com/v1
export ANTHROPIC_API_KEY=your-deepseek-api-key
claude-code --model deepseek-v4-proOpenClaw
OpenClaw natively supports DeepSeek models. Add the following to your .openclawrc:
{
"provider": "deepseek",
"model": "deepseek-v4-pro",
"apiKey": "your-deepseek-api-key"
}OpenCode
OpenCode works with any OpenAI-compatible endpoint:
opencode --provider openai-compatible \
--base-url https://api.deepseek.com/v1 \
--model deepseek-v4-pro \
--api-key your-deepseek-api-keyThe 1M context window makes V4 Pro particularly effective for agentic coding. It can hold entire codebases in context, reducing the need for retrieval-augmented approaches. The low output token pricing also helps, since agentic workflows tend to generate large volumes of output tokens across multiple tool calls.
For a broader look at open-source options for coding, see our Best open-source coding models roundup.
Limitations
V4 Pro is not the best model at everything. Here is where it falls short:
- General knowledge: Trails Gemini 3.1 Pro and GPT-5.4 on MMLU-Pro by about 1.7 points. For knowledge-heavy tasks like trivia, encyclopedic Q&A, or broad factual recall, the closed-source models still have an edge.
- Terminal-Bench: GPT-5.4 scores 69.4% vs V4 Pro’s 67.9%. For complex terminal-based workflows involving multi-step system administration tasks, GPT-5.4 is slightly more reliable.
- Long-context retrieval: Opus 4.6 outperforms V4 Pro on needle-in-a-haystack and long-document retrieval tasks at the upper end of the context window. The hybrid attention mechanism trades some retrieval precision for efficiency.
- Text only: V4 Pro has no vision, audio, or video capabilities. If you need multimodal input, you will need a different model. DeepSeek has hinted at a multimodal V4 variant but nothing has shipped yet.
- HLE: Opus 4.6 leads on the Humanity’s Last Exam benchmark (39.2% vs 37.7%), suggesting slightly stronger performance on the hardest reasoning problems.
For a full model-by-model comparison, check out DeepSeek V4 vs Claude Opus 4.6 and DeepSeek V4 vs GPT-5.5.
Who should use DeepSeek V4 Pro?
V4 Pro is the strongest choice if you need:
- Top-tier coding performance at low cost
- An open-source model you can self-host, fine-tune, or modify
- A 1M context window for large codebase analysis
- Strong math and competition-level problem solving
- An OpenAI-compatible API for easy integration
It is less ideal if you need multimodal capabilities, maximum general knowledge accuracy, or the absolute best long-context retrieval performance.
For teams evaluating Chinese AI models more broadly, our Best Chinese AI models 2026 guide covers the full landscape.
FAQ
Is DeepSeek V4 Pro really open source?
Yes. It ships under the MIT license, which is one of the most permissive open-source licenses available. You can use it commercially, modify it, redistribute it, and fine-tune it without restrictions. The model weights are available on Hugging Face.
How does V4 Pro compare to V3?
V4 Pro is a generational leap. It roughly doubles the total parameter count (1.6T vs 671B), adds the hybrid CSA+HCA attention for efficient 1M context, introduces Manifold-Constrained Hyper-Connections, and was trained on 33T tokens (up from 14.8T). Benchmark scores improve across the board, with SWE-bench Verified jumping from around 52% to 80.6%.
Can I run DeepSeek V4 Pro locally?
Yes, but you need serious hardware. The full FP4 model requires roughly 400GB of VRAM, which means a multi-GPU setup (for example, 8x H100 80GB or equivalent). Quantized versions are available that reduce requirements. See our How to run DeepSeek V4 locally guide for detailed hardware requirements and setup instructions.
Which reasoning mode should I use?
For most tasks, Think High offers the best balance of quality and cost. Use Non-think for simple queries, chat, and summarization where speed matters. Reserve Think Max for hard problems where you need maximum accuracy and do not mind higher token usage. Think Max can use 3-5x more output tokens than Think High on complex problems.