DeepSeek V4 Pro
DeepSeek's April 2026 flagship. 1.6T MoE / 49B active params, 1M context, rivals top closed-source models on STEM and coding at a fraction of the price.
DeepSeek V4 Pro is text & chat AI model from DeepSeek, priced at €0.000 per 1M input tokens with a 1.0M tokens context window.
About this model
0.7
Pricing
API Integration
Use our OpenAI-compatible API to integrate DeepSeek V4 Pro into your application.
npm install railwailimport railwail from "railwail";
const rw = railwail("YOUR_API_KEY");
// Simple — just pass a string
const reply = await rw.run("deepseek-v4-pro", "Hello! What can you do?");
console.log(reply);
// With message history
const reply2 = await rw.run("deepseek-v4-pro", [
{ role: "system", content: "You are a helpful assistant." },
{ role: "user", content: "Explain quantum computing simply." },
]);
console.log(reply2);
// Full response with usage info
const res = await rw.chat("deepseek-v4-pro", [
{ role: "user", content: "Hello!" },
], { temperature: 0.7, max_tokens: 500 });
console.log(res.choices[0].message.content);
console.log(res.usage);Deep dive — DeepSeek AI's DeepSeek V4 Pro
DeepSeek AI is a Chinese AI research lab founded in 2023 by Liang Wenfeng, the founder of the High-Flyer quantitative hedge fund. The lab is funded primarily by High-Flyer's profits and operates independently of major Chinese tech conglomerates. DeepSeek's mission is to build open frontier AI: every flagship model has been released with open weights and a permissive license. Major releases include DeepSeek LLM (late 2023), DeepSeek-V2 (May 2024, MoE), DeepSeek-V3 (December 2024, 671B MoE / 37B active), DeepSeek-R1 (January 2026 family, reasoning), DeepSeek V3.1 (early 2026) and the DeepSeek V4 family released as preview April 24, 2026. DeepSeek's models repeatedly top open-weights leaderboards on coding, math and reasoning at a fraction of the training cost claimed by Western labs, and the team is credited with popularising large-scale Reinforcement Learning from Verifiable Rewards.
Visit DeepSeek AI →DeepSeek-V4-Pro was released April 24, 2026 as the flagship of the V4 family. It is a Sparse MoE Transformer with 1.6T total parameters and 49B activated per token, supporting a native 1M-token context window with up to 384K-token max output. The model was trained on the lab's expanded GPU cluster using DeepSeek's signature recipe: large-scale pretraining on a multi-trillion-token mixture of web text, code, books, scientific papers and curated math/STEM data, followed by extensive Reinforcement Learning from Verifiable Rewards (RLVR) on math, coding and tool-use trajectories. Architectural innovations introduced in V3 - Multi-head Latent Attention (MLA), DeepSeekMoE with fine-grained expert specialization and shared experts, and FP8 mixed-precision training - are retained and refined. V4 Pro is published with open weights under a permissive license and runs natively in both server and inference frameworks such as vLLM and SGLang. DeepSeek treats the V4 launch as a preview phase and has announced that the older deepseek-chat and deepseek-reasoner endpoints will be deprecated on July 24, 2026.
- Parameters
- 1.6T total / 49B active per token
- Context
- 1.0M tokens
- 1M token native context window with 384K max output
- ~81% SWE-bench Verified - rivals top closed-source models
- Top open-weights scores on Math/STEM/Coding benchmarks
- 1.6T MoE / 49B active parameters
- FP8-trained for compute efficiency
- Multi-head Latent Attention for memory-efficient long context
- Function calling and structured JSON output
- Cache hit pricing at $0.0145 per million tokens enables cheap multi-turn agents
- Available via DeepSeek API, OpenRouter, Together, Fireworks and self-hosted with vLLM/SGLang
- Open weights under a permissive license
- Best for: open-weights coding agents, long-document analysis, cost-efficient reasoning workloads, on-premise enterprise deployments.
Pretrained on a multi-trillion-token mixture of web text, code, books, scientific papers and curated math/STEM data. Post-training applies large-scale Reinforcement Learning from Verifiable Rewards (RLVR) on math, coding and tool-use tasks, plus supervised fine-tuning and instruction tuning. Knowledge cutoff approximately early 2026.
License: Open weights under a permissive license that allows commercial use. Hosted API access via deepseek.com.
Known limitations
- Light built-in safety alignment relative to Western frontier models
- No native vision or audio input (text-only)
- Pro variant requires substantial GPU resources to self-host
- Older deepseek-chat / deepseek-reasoner endpoints will be deprecated July 24, 2026
- Some Chinese-language safety constraints apply
Frequently asked questions
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