Advanced AI Model for Reasoning & Code

$1.74/1M input (promo: $0.435 until May 31). New accounts get 5M free tokens. (3) Self-host from Hugging Face — 865 GB weights under MIT license， it requires only 10% of the KV cache memory that V3.2 needed. This means 1M context is economically viable in production， FAQ Questions About V4-Pro What is DeepSeek-V4-Pro and how is it different from V3? + V4-Pro is DeepSeek's flagship 1.6T parameter MoE model， Claude leads on HLE (40.0% vs 37.7%) and HMMT 2026 math (96.2% vs 95.2%)， exhaustively exploring the problem space. This achieves the headline benchmark scores but generates ~190M output tokens per benchmark run — far above the 47M median. Monitor output costs carefully in Think Max mode. Set context window to at least 384K tokens for best results. How do I access V4-Pro? Is it free? + Three ways: (1) Free web chat at chat.deepseek.com — enable Expert Mode. Full V4-Pro， and leads all models on Codeforces (rating 3206). It also beats Claude on Terminal-Bench 2.0 for agentic coding. However， and fine-tuning — everything most developers and enterprises need. How does the reasoning_content field work? + When using Think High or Think Max mode， suitable for most complex tasks，000 words — enough to fit the entire Harry Potter series。

or months of conversation history in a single request. Most importantly， a large codebase， educational applications， recommended for production coding agents. Think Max (Pro-Max mode) gives the model unlimited reasoning budget， requires 8×H100 80GB minimum for full V4-Pro. Is V4-Pro actually open source? + The model weights are fully open under the MIT license at huggingface.co/deepseek-ai/DeepSeek-V4-Pro. This means you can download。

free。

not just a benchmark number. DeepSeek recommends setting context to at least 384K tokens when using Think Max mode. What's the difference between Think High and Think Max? + Think High applies structured analytical reasoning with a fixed budget — faster， and build commercial products without restrictions or fees. The training code and full dataset are not published (standard for large model releases). For practical purposes: open weights enable self-hosting。

and Gemini leads on factual recall. For most coding and software engineering use cases， V4-Pro is a viable alternative to closed models at 7× lower cost. What does the 1M token context window actually mean? + 1 million tokens is roughly 750， run， and verifying the model's logic. Note: a common gotcha reported by developers — many OpenAI-compatible client libraries don't expose reasoning_content by default and require accessing the raw response object. ， released April 24， the response includes a reasoning_content field in addition to the standard content field. reasoning_content contains the model's internal chain-of-thought — the full reasoning process before the final answer. This is useful for debugging， auditing， beats Claude on LiveCodeBench (93.5 vs 88.8)， no subscription. (2) API at platform.deepseek.com — model name deepseek-v4-pro， 2026. It is not a scaled-up V3 — it introduces four genuinely new architectural innovations: (1) Hybrid attention (CSA + HCA) that cuts inference FLOPs to 27% and KV cache to 10% of V3.2 at 1M context. (2) Manifold-Constrained Hyper-Connections (mHC) for training stability at trillion-parameter scale. (3) The Muon optimizer replacing AdamW for faster convergence. (4) FP4 quantization-aware training on MoE expert weights. It was pre-trained on 33T tokens (vs 14.8T for V3) and scores 80.6% on SWE-bench Verified. Is V4-Pro really better than Claude Opus or GPT-5.5? + On coding tasks: V4-Pro matches Claude Opus 4.6 on SWE-bench (80.6% vs 80.8% — a 0.2% gap)， V4-Pro's CSA+HCA hybrid attention makes this practical: at 1M context， fine-tune，。