DeepSeek V3 API
Released December 2024 | 128K Tokens context | 671B params (37B active) parameters
DeepSeek V3 API enables High-throughput coding and reasoning agents that benefit from 60 token-per-second streaming output, Research copilots synthesizing long technical or financial documents within the 128K token context, and Multilingual chat or analysis workloads that need open weights with favorable cost-performance. DeepSeek-V3 is a 671B-parameter Mixture-of-Experts model (37B active) that combines Multi-head Latent Attention with multi-token prediction to deliver fast, cost-efficient reasoning over a 128K context window. Standout strengths include 671B MoE with only 37B parameters active per token for efficient inference and Multi-token prediction and MLA routing improve throughput and memory usage versus dense models. It is optimized for production agent and assistant workloads where response quality, latency, and predictable operating cost all matter.
from openai import OpenAI # Initialize the OpenAI client with Qubrid base URL client = OpenAI( base_url="https://platform.qubrid.com/v1", api_key="QUBRID_API_KEY", ) stream = client.chat.completions.create( model="deepseek-ai/DeepSeek-V3", messages=[ { "role": "user", "content": "Explain quantum computing in simple terms" } ], max_tokens=4096, temperature=0.7, top_p=1, stream=True ) for chunk in stream: if chunk.choices and chunk.choices[0].delta.content: print(chunk.choices[0].delta.content, end="", flush=True) print("\n")from openai import OpenAI # Initialize the OpenAI client with Qubrid base URL client = OpenAI( base_url="https://platform.qubrid.com/v1", api_key="QUBRID_API_KEY", ) stream = client.chat.completions.create( model="deepseek-ai/DeepSeek-V3", messages=[ { "role": "user", "content": "Explain quantum computing in simple terms" } ], max_tokens=4096, temperature=0.7, top_p=1, stream=True ) for chunk in stream: if chunk.choices and chunk.choices[0].delta.content: print(chunk.choices[0].delta.content, end="", flush=True) print("\n") Enterprise
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