DeepSeek V4 Pro

Overview

DeepSeek-V4-Pro is the flagship model of the DeepSeek-V4 series, a Mixture-of-Experts (MoE) model with 1.6 trillion total parameters and 49 billion activated parameters. It achieves state-of-the-art performance among open-source models, rivaling top closed-source models in coding benchmarks (LiveCodeBench 93.5%, Codeforces Rating 3206) and approaching frontier models on reasoning and agentic tasks.

Key Specifications

Capabilities

• Text Generation — State-of-the-art reasoning and generation quality
• Code Generation — SOTA among open-source: LiveCodeBench 93.5%, Codeforces Rating 3206
• Function Calling — Structured tool use for API integrations
• JSON Mode — Guaranteed valid JSON output
• Thinking Mode — Supports non-thinking, think-high, and think-max reasoning levels (default: enabled)
• Streaming — Server-sent events for real-time token delivery
• Chat Prefix Completion — Beta support for agent-style workflows
• FIM (Fill-In-the-Middle) — Beta support for code completion

Pricing

Usage Example

from openai import OpenAI

client = OpenAI(
    api_key="sk-...",
    base_url="https://api.yolorouter.com/v1",
)

response = client.chat.completions.create(
    model="deepseek-v4-pro",
    messages=[{"role": "user", "content": "Solve this step by step: ..."}],
)

print(response.choices[0].message.content)

Best Practices

• Use non-thinking mode for fast, intuitive responses on routine tasks
• Use think-high mode for complex problem-solving, planning, and coding
• Use think-max mode for hard agentic tasks — but monitor output token usage carefully
• Leverage the 1M context window with cache-friendly prompts to maximize cache hit rate
• The hybrid attention architecture (CSA + HCA) makes V4-Pro use only 27% of the per-token FLOPs compared to V3.2 at 1M context