DeepSeek V4 Pro
Intelligence benchmarks
Artificial Analysis indexes - compared with the best open and proprietary models
Intelligence
51.5
AA Index
Coding
47.5
AA Index
Agentic
67.2
AA Index
Intelligence Index - DeepSeek V4 Pro vs. the field
Best open-weight models (you can run locally) and leading proprietary models for context.
Coding Index comparison
Agentic Index comparison
Benchmark data from Artificial Analysis · updated 2026-06-07.
Standard benchmarks
Performance across standard evaluations
| Benchmark | Score |
|---|---|
| MMLU | 90.1 |
| HumanEval | 76.8 |
| GSM8K | 92.6 |
| GPQA | 88.8 |
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
vllm serve deepseek-ai/DeepSeek-V4-ProNew to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
DeepSeek V4 Pro
DeepSeek V4 Pro is the flagship of the V4 family: 1.6 trillion total parameters with 49 billion active per token, supporting 1 million token context under an MIT license. In its Max reasoning mode, it is the strongest open-weight model for agentic coding - topping the GDPval-AA leaderboard - and ties closed-source frontier models like GPT-5.4 and Claude Opus 4.6 on SWE-Bench.
What makes DeepSeek V4 Pro special
- 1.6T total / 49B active MoE - the most capable open model for agentic work
- #1 agentic coding - tops GDPval-AA leaderboard among open weights
- 1M token context - unprecedented ability to work across entire codebases
- Hybrid Attention Architecture - CSA + HCA reduces single-token FLOPs to 27% of V3.2
- Three reasoning modes - Non-think, Think High, Think Max
- MIT license - fully open weights with no restrictions
- 32T training tokens - pre-trained on massive, diverse corpus
Benchmarks
| Benchmark | DeepSeek V4 Pro-Max | GPT-5.4 xHigh | Claude Opus 4.6 Max | Gemini 3.1 Pro High |
|---|---|---|---|---|
| MMLU-Pro | 87.5 | 87.5 | 89.1 | 91.0 |
| GPQA Diamond | 90.1 | 93.0 | 91.3 | 94.3 |
| LiveCodeBench | 93.5 | - | 88.8 | 91.7 |
| Codeforces Rating | 3206 | 3168 | - | 3052 |
| HLE | 37.7 | 39.8 | 40.0 | 44.4 |
| SimpleQA-Verified | 57.9 | 45.3 | 46.2 | 75.6 |
Source: DeepSeek-V4 technical report. Max effort mode for all models.
VRAM requirements
| Precision | VRAM | Recommended Hardware |
|---|---|---|
| FP4+FP8 mixed | ~320 GB | NVIDIA L40S (x4), NVIDIA A6000 (x4+) |
| FP8 | ~880 GB | Server cluster required |
DeepSeek V4 Pro requires data-center class hardware. The FP4+FP8 native precision makes it uniquely efficient for its 1.6T total size, but this is not a model for consumer GPUs.
How to run
# Via vLLM (0.19.0+)
vllm serve deepseek-ai/DeepSeek-V4-Pro --port 8010 --tensor-parallel-size 8What the community says
"DeepSeek V4 Pro-Max is the first open model that genuinely ties closed frontier models on SWE-Bench. MIT license makes it an easy choice for enterprise."
- r/LocalLLaMA, 312 upvotes
How it compares
DeepSeek V4 Pro is the most capable open model for agentic coding, edging out Kimi K2.6 on SWE-Bench Pro and Codeforces while trailing on general knowledge benchmarks. It is a clear upgrade over DeepSeek V3 in every dimension, with architectural improvements that reduce per-token FLOPs by 73% at 1M context.
Compared to Kimi K2.6: V4 Pro wins on coding-specific tasks but K2.6 has a higher overall Artificial Analysis index score (54 vs 52). Both are MIT-adjacent licensed.
Use it with
vLLM, Open WebUI, Text Generation Inference
When to use something else
If you do not have access to multi-GPU server hardware, DeepSeek V4 Pro is not deployable. Consider DeepSeek V4 Flash (284B, single-host capable) or Kimi K2.6 (1T, 32B active) as alternatives that still deliver frontier-class performance with lower hardware requirements.
Frequently asked
Quick answers to common questions
How much VRAM does DeepSeek V4 Pro need?
DeepSeek V4 Pro with 1600B parameters needs approximately 3000 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is DeepSeek V4 Pro better than other DeepSeek models?
DeepSeek V4 Pro scores 90.1 on MMLU and 76.8 on HumanEval. It has 1600B parameters with 1,048,576 context - a strong choice for agentic-coding, reasoning, knowledge-tasks.
What license is DeepSeek V4 Pro under?
DeepSeek V4 Pro is released under the MIT license, making it suitable for most commercial and personal projects.
What hardware runs DeepSeek V4 Pro well?
With 1600B parameters, DeepSeek V4 Pro requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for DeepSeek V4 Pro?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Step up to Q5_K_M or Q8_0 only if you have spare VRAM. Use our VRAM calculator to compare.
How long can DeepSeek V4 Pro's context window handle?
DeepSeek V4 Pro supports a 1,048,576-token context window - enough for very long documents, codebases, or multi-turn conversations. Real-world usable context may vary by implementation.
What models compete with DeepSeek V4 Pro?
DeepSeek V4 Pro competes with other 800B–2400B. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
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