1M
2.262
4.524
Chat
Active
DeepSeek V4 Pro
A 1.6 trillion-parameter mixture-of-experts model designed for world-class reasoning, agentic coding, and long-context intelligence at a fraction of the cost of comparable frontier models.
V4 Pro's architecture is the first open-weights model to make 1M-token context viable not just technically, but economically at competitive pricing, not as a premium add-on.
DeepSeek V4 Pro is the flagship model from DeepSeek's fourth-generation release, dropped on April 24, 2026. It is the largest open-weights model currently available, larger than Kimi K2.6 at 1.1T and more than twice the size of its predecessor, DeepSeek V3.2 at 685B. That scale alone would mean little without efficiency; what makes V4 Pro genuinely remarkable is how little of that scale it uses during inference.
Using a Mixture-of-Experts (MoE) design, V4 Pro activates only 49 billion parameters per token, roughly 3% of its full weight. In the one-million-token context setting, it requires just 27% of the inference FLOPs and 10% of the KV cache size compared with DeepSeek V3.2. Those are not incremental improvements. They represent a step-change in what's economically feasible to run at production scale.
Most models label million-token context windows as a marketing feature. At that scale, standard attention is quadratically expensive — memory balloons, inference slows, and costs multiply. DeepSeek solved this with three architectural breakthroughs developed and published before the V4 launch.
Compressed Sparse Attention (CSA) combined with Heavily Compressed Attention (HCA) replaces standard full attention. The result: 27% of the inference FLOPs and just 10% of the KV cache at 1M tokens — making long-context inference genuinely deployable at scale.
Standard Hyper-Connections caused 3,000× signal amplification in 27B experiments, crashing training. The mHC framework constrains mixing matrices using the Sinkhorn-Knopp algorithm, cutting amplification to 1.6×, enabling stable training at 1.6T parameters.
Pre-training uses the Muon optimizer for faster convergence and training stability versus standard AdamW. At 1.6T-parameter scale, gradient collapse compounds quickly — Muon alongside mHC's stability guarantees made 33T-token training achievable.
Independent SFT and RL cultivation of domain-specific experts (using GRPO), followed by unified model consolidation via on-policy distillation. Each domain's strength is preserved, then blended into a single generalist model without capability regression.
DeepSeek benchmarks V4 Pro as competitive with top closed-source models across reasoning, coding, and knowledge tasks. On SWE-bench Verified — a real-world software engineering benchmark — it scores 80.6%, sitting within 0.2 points of Claude Opus 4.6 at roughly one-seventh the output cost.
V4 Pro and V4 Flash both support three configurable reasoning modes, letting you trade off speed against depth depending on what the task actually requires — rather than paying for maximum thinking on every call.
Default mode. Fast, direct responses without extended chain-of-thought. Best for retrieval, summarization, structured outputs, and tasks where latency matters more than deep multi-step reasoning.
Activates step-by-step reasoning before the final answer. The model works through the problem internally before responding. Visible reasoning tokens appear in the reasoning_details response field. Suitable for complex coding, math, and analytical tasks.
V4 Pro's combination of 1M-token context, strong agentic coding performance, and competitive pricing makes it suited to a specific class of workloads. Here is where it fits best — and where you might opt for V4 Flash instead.
At 1M tokens, you can load an entire medium-sized repository into context. V4 Pro's SOTA performance on Terminal-Bench and SWE-bench makes it genuinely capable at cross-file refactoring, bug investigation, and architectural review without truncation.
Multi-step automation, research synthesis, and complex workflow execution where the agent must track state across many turns. V4 Pro leads open-source models on agentic coding benchmarks and holds comparable performance to V4 Flash on simpler agent tasks.
Beats all current open-weight models on math and STEM benchmarks. Competitive with top closed-source models on GPQA Diamond. Suitable for technical research assistance, problem solving, and educational tooling requiring deep domain knowledge.
V4 Pro ranks first among open models for world knowledge, trailing only Gemini 3.1 Pro overall. Enterprises building RAG pipelines or document-heavy Q&A systems that need strong factual grounding will find V4 Pro's recall noticeably above peer open-source models.
DeepSeek V4 Pro is the flagship model from DeepSeek's fourth-generation release, dropped on April 24, 2026. It is the largest open-weights model currently available, larger than Kimi K2.6 at 1.1T and more than twice the size of its predecessor, DeepSeek V3.2 at 685B. That scale alone would mean little without efficiency; what makes V4 Pro genuinely remarkable is how little of that scale it uses during inference.
Using a Mixture-of-Experts (MoE) design, V4 Pro activates only 49 billion parameters per token, roughly 3% of its full weight. In the one-million-token context setting, it requires just 27% of the inference FLOPs and 10% of the KV cache size compared with DeepSeek V3.2. Those are not incremental improvements. They represent a step-change in what's economically feasible to run at production scale.
Most models label million-token context windows as a marketing feature. At that scale, standard attention is quadratically expensive — memory balloons, inference slows, and costs multiply. DeepSeek solved this with three architectural breakthroughs developed and published before the V4 launch.
Compressed Sparse Attention (CSA) combined with Heavily Compressed Attention (HCA) replaces standard full attention. The result: 27% of the inference FLOPs and just 10% of the KV cache at 1M tokens — making long-context inference genuinely deployable at scale.
Standard Hyper-Connections caused 3,000× signal amplification in 27B experiments, crashing training. The mHC framework constrains mixing matrices using the Sinkhorn-Knopp algorithm, cutting amplification to 1.6×, enabling stable training at 1.6T parameters.
Pre-training uses the Muon optimizer for faster convergence and training stability versus standard AdamW. At 1.6T-parameter scale, gradient collapse compounds quickly — Muon alongside mHC's stability guarantees made 33T-token training achievable.
Independent SFT and RL cultivation of domain-specific experts (using GRPO), followed by unified model consolidation via on-policy distillation. Each domain's strength is preserved, then blended into a single generalist model without capability regression.
DeepSeek benchmarks V4 Pro as competitive with top closed-source models across reasoning, coding, and knowledge tasks. On SWE-bench Verified — a real-world software engineering benchmark — it scores 80.6%, sitting within 0.2 points of Claude Opus 4.6 at roughly one-seventh the output cost.
V4 Pro and V4 Flash both support three configurable reasoning modes, letting you trade off speed against depth depending on what the task actually requires — rather than paying for maximum thinking on every call.
Default mode. Fast, direct responses without extended chain-of-thought. Best for retrieval, summarization, structured outputs, and tasks where latency matters more than deep multi-step reasoning.
Activates step-by-step reasoning before the final answer. The model works through the problem internally before responding. Visible reasoning tokens appear in the reasoning_details response field. Suitable for complex coding, math, and analytical tasks.
V4 Pro's combination of 1M-token context, strong agentic coding performance, and competitive pricing makes it suited to a specific class of workloads. Here is where it fits best — and where you might opt for V4 Flash instead.
At 1M tokens, you can load an entire medium-sized repository into context. V4 Pro's SOTA performance on Terminal-Bench and SWE-bench makes it genuinely capable at cross-file refactoring, bug investigation, and architectural review without truncation.
Multi-step automation, research synthesis, and complex workflow execution where the agent must track state across many turns. V4 Pro leads open-source models on agentic coding benchmarks and holds comparable performance to V4 Flash on simpler agent tasks.
Beats all current open-weight models on math and STEM benchmarks. Competitive with top closed-source models on GPQA Diamond. Suitable for technical research assistance, problem solving, and educational tooling requiring deep domain knowledge.
V4 Pro ranks first among open models for world knowledge, trailing only Gemini 3.1 Pro overall. Enterprises building RAG pipelines or document-heavy Q&A systems that need strong factual grounding will find V4 Pro's recall noticeably above peer open-source models.
.webp)
.png){kind=small}
