DeepSeek V4 Pro

DeepSeek releases V4 Pro, a 1.6-trillion-parameter Mixture-of-Experts model with 49 billion active parameters per token, achieving a top open-weight score of 80.6% on SWE-bench Verified. The model requires multi-H200/H100 clusters for inference, limiting deployment to cloud providers despite MIT-licensed open weights.

DeepSeek V4 Pro MoE premier1.6T total, 49B active per token MoE . Hybrid attention: Compressed Sparse Attention CSA, 4x KV compression + Heavily Compressed Attention HCA, 128x across 61 layers, manifold-constrained Hyper-Connections mHC . FP4 experts + FP8 rest, 32T+ pretraining tokens, Muon optimizer. 1M native context; Think Max mode recommends =384K. At ~800GB+ for Q4 weights it needs a multi-H200/H100 cluster or DGX-class system - not workstation-fittable, so cloud-only for nearly everyone despite open weights. Top open-weight agentic coding score SWE-bench Verified 80.6 . Open weights under MIT. - 1600.0B - 1000k - mit - Apr 2026 Scores Or run it in the cloud Live per-provider pricing, throughput and uptime - refreshed 3 days ago via OpenRouter. Click a column to sort. | Provider | Type | Input $/M | Output $/M | Cache $/M | Tok/s | Latency | Uptime | Value | |---|---|---|---|---|---|---|---|---| | API | 0.44 | 0.87 | - | - | - | - | cheapest | Default order: throughput among 95%+ uptime providers, then latency; subscriptions last. Sort by any column. Subscription rows show $/mo in the Value column - per-token columns are "-". Affiliate links are marked sponsored / nofollow. Confirm current pricing on the provider's site before committing. Detailed API pricing page + JSON endpoint → /models/deepseek-v4-pro/pricing Inference cost over time Data accumulates from the first daily sync - longer ranges populate over time. Prices come from OpenRouter snapshots, not a historical API.