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DeepSeek's open-weight flagship 1.6T MoE model with hybrid sparse attention, 1M-token context, competition-grade coding (LiveCodeBench 93.5), and ultra-low pricing — built for code, reasoning, and long-horizon agents.
DeepSeek
| Item | Specification |
|---|---|
| Input | Text |
| Output | Text |
| Context | 1,000,000 tokens (1M) |
| Max Output | 384,000 tokens |
| Vision | ✗ Not Supported |
| Function Calling | ✓ Supported (OpenAI / Anthropic-compatible API) |
DeepSeek V4 Pro is DeepSeek-AI's open-weight flagship model, released on April 24, 2026 under the MIT License. It is a Mixture-of-Experts (MoE) model with 1.6 trillion total parameters and 49 billion activated per token (61 layers, 384 routed experts plus 1 shared expert, with 6 experts selected per token), pre-trained on 33T tokens. The model uses FP4 + FP8 mixed-precision quantization-aware training (FP4 for MoE experts, FP8 elsewhere) and supports Thinking/Non-Thinking dual modes with three reasoning effort levels (Think Low / High / Max).
V4 Pro's defining breakthrough is efficient million-token-context inference — via a Hybrid Attention design (Compressed Sparse Attention + Heavily Compressed Attention) and Manifold-Constrained Hyper-Connections (mHC), it consumes just 27% of single-token FLOPs and 10% of KV cache compared to V3.2 at 1M context, and is trained with the Muon optimizer. V4-Pro-Max scores 93.5 on LiveCodeBench (#1 across open and closed models), a Codeforces rating of 3,206 (ahead of GPT-5.4's 3,168), and 80.6% on SWE-Bench Verified (within 0.2 of Claude Opus 4.6) — all at roughly 1/7 the price of frontier closed models.
| Feature | Benefit |
|---|---|
| Hybrid Attention (CSA + HCA) | Cuts long-context FLOPs to 27% and KV cache to 10% of V3.2, making 1M-token inference economically practical in production |
| 1.6T MoE / 49B Active | Trillion-scale knowledge capacity with inference cost close to a mid-sized dense model |
| FP4 + FP8 Quantization-Aware Training | Low-precision baked in during training avoids post-hoc quantization loss and reduces memory/compute footprint |
| Muon Optimizer + mHC | Faster convergence and stable training at 1T+ scale; mHC preserves signal propagation through deep networks |
| MIT License | Weights freely downloadable on Hugging Face, commercial use and fine-tuning allowed — no vendor lock-in |
| OpenAI/Anthropic-Compatible API | Drop-in integration for Cursor, Claude Code, Cline, and other tooling with zero code changes |
| Dimension | Rating | Notes |
|---|---|---|
| Reasoning | Excellent | GPQA Diamond 90.1 and IMOAnswerBench 89.8 lead open-source; HLE 37.7 trails top closed models slightly |
| Coding | Top-tier | #1 on LiveCodeBench (93.5) and Codeforces (3,206); SWE-Bench Verified statistically tied with Claude Opus |
| Creative Writing | Strong | Solid prose quality, though training and tuning prioritize coding and reasoning over creative output |
| Multimodal | Moderate | Current release is text-only — no native vision or video input |
| Response Speed | Moderate | Hybrid attention keeps long-context inference efficient, but Think Max mode is verbose |
| Context Window | Huge | 1M tokens — among the largest open-source context windows, with sparse attention keeping it affordable |
Our pricing is based on image and text token usage. The final cost depends on the tokens consumed.
| Token Type | LinkAI Price | Official Price |
|---|---|---|
| Input | $1.392 / 1M tokens | $1.74 / 1M tokens |
| Cached input | $0.0116 / 1M tokens | $0.0145 / 1M tokens |
| Output | $2.784 / 1M tokens | $3.48 / 1M tokens |